├── CHANGES.md ├── CONTRIBUTING.rst ├── COPYRIGHTS.txt ├── INSTALL.txt ├── LICENSE ├── LICENSE.txt ├── README.md ├── README.rst ├── README_l1.txt ├── docs ├── en │ ├── GLMNotes.lyx │ ├── GLMNotes.pdf │ ├── Makefile │ ├── README.md │ ├── fix_longtable.py │ ├── make.bat │ ├── source │ │ ├── _static │ │ │ ├── blogger.png │ │ │ ├── blogger_sm.png │ │ │ ├── bullet.gif │ │ │ ├── closelabel.png │ │ │ ├── examples.css │ │ │ ├── facebox.css │ │ │ ├── facebox.js │ │ │ ├── gettingstarted_0.png │ │ │ ├── images │ │ │ │ ├── anova.png │ │ │ │ ├── categorical_interaction_plot.png │ │ │ │ ├── chi2_fitting.png │ │ │ │ ├── contrasts.png │ │ │ │ ├── discrete_overview.png │ │ │ │ ├── distributed_estimation.png │ │ │ │ ├── exponential_smoothing.png │ │ │ │ ├── fairs_data.png │ │ │ │ ├── formulas_intro.png │ │ │ │ ├── generic_mle.png │ │ │ │ ├── glm_formulas.png │ │ │ │ ├── glm_overview.png │ │ │ │ ├── glm_weights.png │ │ │ │ ├── gls.png │ │ │ │ ├── influence_glm_logit.png │ │ │ │ ├── kde_overview.png │ │ │ │ ├── markov_autoregression.png │ │ │ │ ├── markov_regression.png │ │ │ │ ├── mixed_lm.png │ │ │ │ ├── ols.png │ │ │ │ ├── pca_fertility_factors.png │ │ │ │ ├── plots_boxplots.png │ │ │ │ ├── prediction.png │ │ │ │ ├── quantile_regression.png │ │ │ │ ├── recursive_ls.png │ │ │ │ ├── regression_diagnostics.png │ │ │ │ ├── regression_plots.png │ │ │ │ ├── rlm.png │ │ │ │ ├── rlm_overview.png │ │ │ │ ├── statespace_arma0.png │ │ │ │ ├── statespace_concentrated_scale.png │ │ │ │ ├── statespace_cycles.png │ │ │ │ ├── statespace_dfm_coincident.png │ │ │ │ ├── statespace_local_linear_trend.png │ │ │ │ ├── statespace_sarimax_internet.png │ │ │ │ ├── statespace_sarimax_stata.png │ │ │ │ ├── statespace_seasonal.png │ │ │ │ ├── statespace_structural_harvey_jaeger.png │ │ │ │ ├── statespace_varmax.png │ │ │ │ ├── tsa_arma0.png │ │ │ │ ├── tsa_arma1.png │ │ │ │ ├── tsa_dates.png │ │ │ │ ├── tsa_filters.png │ │ │ │ └── wls.png │ │ │ ├── ipython.min.css │ │ │ ├── loading.gif │ │ │ ├── minus.gif │ │ │ ├── mktree.css │ │ │ ├── mktree.js │ │ │ ├── nbviewer.pygments.css │ │ │ ├── plus.gif │ │ │ └── scripts.js │ │ ├── _templates │ │ │ └── autosummary │ │ │ │ ├── class.rst │ │ │ │ └── glmfamilies.rst │ │ ├── about.rst │ │ ├── anova.rst │ │ ├── conf.py │ │ ├── contingency_tables.rst │ │ ├── contrasts.rst │ │ ├── datasets │ │ │ ├── dataset_proposal.rst │ │ │ └── index.rst │ │ ├── dev │ │ │ ├── dataset_notes.rst │ │ │ ├── examples.rst │ │ │ ├── get_involved.rst │ │ │ ├── git_notes.rst │ │ │ ├── images │ │ │ │ └── git_merge.png │ │ │ ├── index.rst │ │ │ ├── internal.rst │ │ │ ├── maintainer_notes.rst │ │ │ ├── naming_conventions.rst │ │ │ ├── test_notes.rst │ │ │ ├── testing.rst │ │ │ └── vbench.rst │ │ ├── diagnostic.rst │ │ ├── discretemod.rst │ │ ├── distributions.rst │ │ ├── duration.rst │ │ ├── emplike.rst │ │ ├── endog_exog.rst │ │ ├── example_formulas.rst │ │ ├── examples │ │ │ ├── README │ │ │ ├── index.rst │ │ │ └── landing.json │ │ ├── extending.rst.TXT │ │ ├── faq.rst │ │ ├── gee.rst │ │ ├── genericmle.rst.TXT │ │ ├── gettingstarted.rst │ │ ├── glm.rst │ │ ├── glm_techn1.rst.TXT │ │ ├── glm_techn2.rst.TXT │ │ ├── gmm.rst │ │ ├── gmm_techn1.rst.TXT │ │ ├── graphics.rst │ │ ├── images │ │ │ ├── aw.png │ │ │ ├── hl.png │ │ │ ├── ht.png │ │ │ ├── ls.png │ │ │ ├── re.png │ │ │ ├── statsmodels_hybi_banner.png │ │ │ ├── statsmodels_hybi_banner.svg │ │ │ ├── statsmodels_hybi_favico.ico │ │ │ ├── tk.png │ │ │ └── tm.png │ │ ├── importpaths.rst │ │ ├── imputation.rst │ │ ├── index.rst │ │ ├── install.rst │ │ ├── iolib.rst │ │ ├── miscmodels.rst │ │ ├── missing.rst │ │ ├── mixed_glm.rst │ │ ├── mixed_linear.rst │ │ ├── multivariate.rst │ │ ├── nonparametric.rst │ │ ├── pitfalls.rst │ │ ├── plots │ │ │ ├── arma_predict_plot.py │ │ │ ├── bkf_plot.py │ │ │ ├── cff_plot.py │ │ │ ├── graphics-mean_diff_plot.py │ │ │ ├── graphics_boxplot_beanplot.py │ │ │ ├── graphics_boxplot_violinplot.py │ │ │ ├── graphics_functional_fboxplot.py │ │ │ ├── graphics_functional_hdrboxplot.py │ │ │ ├── graphics_functional_rainbowplot.py │ │ │ ├── graphics_gofplots_qqplot.py │ │ │ ├── graphics_month_plot.py │ │ │ ├── graphics_plot_fit_ex.py │ │ │ ├── hpf_plot.py │ │ │ ├── load_macrodata.py │ │ │ ├── var_plot_acorr.py │ │ │ ├── var_plot_fevd.py │ │ │ ├── var_plot_forecast.py │ │ │ ├── var_plot_input.py │ │ │ ├── var_plot_irf.py │ │ │ ├── var_plot_irf_cum.py │ │ │ └── var_plots.py │ │ ├── regression.rst │ │ ├── regression_techn1.rst.TXT │ │ ├── release │ │ │ ├── github-stats-0.5.rst │ │ │ ├── github-stats-0.6.rst │ │ │ ├── index.rst │ │ │ ├── old_changes.rst │ │ │ ├── version0.5.rst │ │ │ ├── version0.6.rst │ │ │ ├── version0.7.rst │ │ │ ├── version0.8.rst │ │ │ └── version0.9.rst │ │ ├── rlm.rst │ │ ├── rlm_techn1.rst │ │ ├── sandbox.rst │ │ ├── statespace.rst │ │ ├── stats.rst │ │ ├── tools.rst │ │ ├── tsa.rst │ │ ├── tsastats.rst.TXT │ │ └── vector_ar.rst │ ├── sphinxext │ │ ├── LICENSE.txt │ │ ├── MANIFEST.in │ │ ├── README.txt │ │ ├── github.py │ │ ├── ipython_console_highlighting.py │ │ ├── ipython_directive.py │ │ └── numpy_ext │ │ │ ├── __init__.py │ │ │ ├── docscrape.py │ │ │ ├── docscrape_sphinx.py │ │ │ └── plot_directive.py │ └── themes │ │ └── statsmodels │ │ ├── indexsidebar.html │ │ ├── layout.html │ │ ├── page.html │ │ ├── relations.html │ │ ├── sidelinks.html │ │ ├── static │ │ ├── nature.css_t │ │ └── statsmodels_hybi_banner.png │ │ └── theme.conf └── zh │ ├── GLMNotes.lyx │ ├── GLMNotes.pdf │ ├── Makefile │ ├── README.md │ ├── fix_longtable.py │ ├── make.bat │ ├── source │ ├── _static │ │ ├── blogger.png │ │ ├── blogger_sm.png │ │ ├── bullet.gif │ │ ├── closelabel.png │ │ ├── examples.css │ │ ├── facebox.css │ │ ├── facebox.js │ │ ├── gettingstarted_0.png │ │ ├── images │ │ │ ├── anova.png │ │ │ ├── categorical_interaction_plot.png │ │ │ ├── chi2_fitting.png │ │ │ ├── contrasts.png │ │ │ ├── discrete_overview.png │ │ │ ├── distributed_estimation.png │ │ │ ├── exponential_smoothing.png │ │ │ ├── fairs_data.png │ │ │ ├── formulas_intro.png │ │ │ ├── generic_mle.png │ │ │ ├── glm_formulas.png │ │ │ ├── glm_overview.png │ │ │ ├── glm_weights.png │ │ │ ├── gls.png │ │ │ ├── influence_glm_logit.png │ │ │ ├── kde_overview.png │ │ │ ├── markov_autoregression.png │ │ │ ├── markov_regression.png │ │ │ ├── mixed_lm.png │ │ │ ├── ols.png │ │ │ ├── pca_fertility_factors.png │ │ │ ├── plots_boxplots.png │ │ │ ├── prediction.png │ │ │ ├── quantile_regression.png │ │ │ ├── recursive_ls.png │ │ │ ├── regression_diagnostics.png │ │ │ ├── regression_plots.png │ │ │ ├── rlm.png │ │ │ ├── rlm_overview.png │ │ │ ├── statespace_arma0.png │ │ │ ├── statespace_concentrated_scale.png │ │ │ ├── statespace_cycles.png │ │ │ ├── statespace_dfm_coincident.png │ │ │ ├── statespace_local_linear_trend.png │ │ │ ├── statespace_sarimax_internet.png │ │ │ ├── statespace_sarimax_stata.png │ │ │ ├── statespace_seasonal.png │ │ │ ├── statespace_structural_harvey_jaeger.png │ │ │ ├── statespace_varmax.png │ │ │ ├── tsa_arma0.png │ │ │ ├── tsa_arma1.png │ │ │ ├── tsa_dates.png │ │ │ ├── tsa_filters.png │ │ │ └── wls.png │ │ ├── ipython.min.css │ │ ├── loading.gif │ │ ├── minus.gif │ │ ├── mktree.css │ │ ├── mktree.js │ │ ├── nbviewer.pygments.css │ │ ├── plus.gif │ │ └── scripts.js │ ├── _templates │ │ └── autosummary │ │ │ ├── class.rst │ │ │ └── glmfamilies.rst │ ├── about.rst │ ├── anova.rst │ ├── conf.py │ ├── contingency_tables.rst │ ├── contrasts.rst │ ├── datasets │ │ ├── dataset_proposal.rst │ │ └── index.rst │ ├── dev │ │ ├── dataset_notes.rst │ │ ├── examples.rst │ │ ├── get_involved.rst │ │ ├── git_notes.rst │ │ ├── images │ │ │ └── git_merge.png │ │ ├── index.rst │ │ ├── internal.rst │ │ ├── maintainer_notes.rst │ │ ├── naming_conventions.rst │ │ ├── test_notes.rst │ │ ├── testing.rst │ │ └── vbench.rst │ ├── diagnostic.rst │ ├── discretemod.rst │ ├── distributions.rst │ ├── duration.rst │ ├── emplike.rst │ ├── endog_exog.rst │ ├── example_formulas.rst │ ├── examples │ │ ├── README │ │ ├── index.rst │ │ └── landing.json │ ├── extending.rst.TXT │ ├── faq.rst │ ├── gee.rst │ ├── genericmle.rst.TXT │ ├── gettingstarted.rst │ ├── glm.rst │ ├── glm_techn1.rst.TXT │ ├── glm_techn2.rst.TXT │ ├── gmm.rst │ ├── gmm_techn1.rst.TXT │ ├── graphics.rst │ ├── images │ │ ├── aw.png │ │ ├── hl.png │ │ ├── ht.png │ │ ├── ls.png │ │ ├── re.png │ │ ├── statsmodels_hybi_banner.png │ │ ├── statsmodels_hybi_banner.svg │ │ ├── statsmodels_hybi_favico.ico │ │ ├── tk.png │ │ └── tm.png │ ├── importpaths.rst │ ├── imputation.rst │ ├── index.rst │ ├── install.rst │ ├── iolib.rst │ ├── miscmodels.rst │ ├── missing.rst │ ├── mixed_glm.rst │ ├── mixed_linear.rst │ ├── multivariate.rst │ ├── nonparametric.rst │ ├── pitfalls.rst │ ├── plots │ │ ├── arma_predict_plot.py │ │ ├── bkf_plot.py │ │ ├── cff_plot.py │ │ ├── graphics-mean_diff_plot.py │ │ ├── graphics_boxplot_beanplot.py │ │ ├── graphics_boxplot_violinplot.py │ │ ├── graphics_functional_fboxplot.py │ │ ├── graphics_functional_hdrboxplot.py │ │ ├── graphics_functional_rainbowplot.py │ │ ├── graphics_gofplots_qqplot.py │ │ ├── graphics_month_plot.py │ │ ├── graphics_plot_fit_ex.py │ │ ├── hpf_plot.py │ │ ├── load_macrodata.py │ │ ├── var_plot_acorr.py │ │ ├── var_plot_fevd.py │ │ ├── var_plot_forecast.py │ │ ├── var_plot_input.py │ │ ├── var_plot_irf.py │ │ ├── var_plot_irf_cum.py │ │ └── var_plots.py │ ├── regression.rst │ ├── regression_techn1.rst.TXT │ ├── release │ │ ├── github-stats-0.5.rst │ │ ├── github-stats-0.6.rst │ │ ├── index.rst │ │ ├── old_changes.rst │ │ ├── version0.5.rst │ │ ├── version0.6.rst │ │ ├── version0.7.rst │ │ ├── version0.8.rst │ │ └── version0.9.rst │ ├── rlm.rst │ ├── rlm_techn1.rst │ ├── sandbox.rst │ ├── statespace.rst │ ├── stats.rst │ ├── tools.rst │ ├── tsa.rst │ ├── tsastats.rst.TXT │ └── vector_ar.rst │ ├── sphinxext │ ├── LICENSE.txt │ ├── MANIFEST.in │ ├── README.txt │ ├── github.py │ ├── ipython_console_highlighting.py │ ├── ipython_directive.py │ └── numpy_ext │ │ ├── __init__.py │ │ ├── docscrape.py │ │ ├── docscrape_sphinx.py │ │ └── plot_directive.py │ └── themes │ └── statsmodels │ ├── indexsidebar.html │ ├── layout.html │ ├── page.html │ ├── relations.html │ ├── sidelinks.html │ ├── static │ ├── nature.css_t │ └── statsmodels_hybi_banner.png │ └── theme.conf └── examples ├── incomplete ├── arima.py ├── arma2.py ├── dates.py ├── glsar.py ├── ols_table.py ├── ols_tftest.py └── wls_extended.py ├── notebooks ├── categorical_interaction_plot.ipynb ├── chi2_fitting.ipynb ├── contrasts.ipynb ├── discrete_choice_example.ipynb ├── discrete_choice_overview.ipynb ├── distributed_estimation.ipynb ├── exponential_smoothing.ipynb ├── formulas.ipynb ├── generic_mle.ipynb ├── glm.ipynb ├── glm_formula.ipynb ├── glm_weights.ipynb ├── gls.ipynb ├── influence_glm_logit.ipynb ├── interactions_anova.ipynb ├── kernel_density.ipynb ├── markov_autoregression.ipynb ├── markov_regression.ipynb ├── mixed_lm_example.ipynb ├── ols.ipynb ├── pca_fertility_factors.ipynb ├── plots_boxplots.ipynb ├── predict.ipynb ├── quantile_regression.ipynb ├── recursive_ls.ipynb ├── regression_diagnostics.ipynb ├── regression_plots.ipynb ├── robust_models_0.ipynb ├── robust_models_1.ipynb ├── star_diagram.png ├── statespace_arma_0.ipynb ├── statespace_concentrated_scale.ipynb ├── statespace_cycles.ipynb ├── statespace_dfm_coincident.ipynb ├── statespace_local_linear_trend.ipynb ├── statespace_sarimax_internet.ipynb ├── statespace_sarimax_stata.ipynb ├── statespace_seasonal.ipynb ├── statespace_structural_harvey_jaeger.ipynb ├── statespace_varmax.ipynb ├── tsa_arma_0.ipynb ├── tsa_arma_1.ipynb ├── tsa_dates.ipynb ├── tsa_filters.ipynb └── wls.ipynb ├── python ├── categorical_interaction_plot.py ├── contrasts.py ├── discrete_choice_example.py ├── discrete_choice_overview.py ├── formulas.py ├── generic_mle.py ├── glm.py ├── glm_formula.py ├── gls.py ├── interactions_anova.py ├── kernel_density.py ├── ols.py ├── predict.py ├── quantile_regression.py ├── regression_diagnostics.py ├── regression_plots.py ├── robust_models_0.py ├── robust_models_1.py ├── tsa_arma_0.py ├── tsa_arma_1.py ├── tsa_dates.py ├── tsa_filters.py └── wls.py └── run_all.py /CHANGES.md: -------------------------------------------------------------------------------- 1 | Release Notes 2 | ============= 3 | 4 | The list of changes for each statsmodels release can be found [here](https://www.statsmodels.org/devel/release/index.html). Full details are available in the [commit logs](https://github.com/statsmodels/statsmodels). 5 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Copyright (C) 2006, Jonathan E. Taylor 2 | All rights reserved. 3 | 4 | Copyright (c) 2006-2008 Scipy Developers. 5 | All rights reserved. 6 | 7 | Copyright (c) 2009-2018 Statsmodels Developers. 8 | All rights reserved. 9 | 10 | 11 | Redistribution and use in source and binary forms, with or without 12 | modification, are permitted provided that the following conditions are met: 13 | 14 | a. Redistributions of source code must retain the above copyright notice, 15 | this list of conditions and the following disclaimer. 16 | b. Redistributions in binary form must reproduce the above copyright 17 | notice, this list of conditions and the following disclaimer in the 18 | documentation and/or other materials provided with the distribution. 19 | c. Neither the name of Statsmodels nor the names of its contributors 20 | may be used to endorse or promote products derived from this software 21 | without specific prior written permission. 22 | 23 | 24 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 25 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 26 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 27 | ARE DISCLAIMED. IN NO EVENT SHALL STATSMODELS OR CONTRIBUTORS BE LIABLE FOR 28 | ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 29 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 30 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 31 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 32 | LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY 33 | OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH 34 | DAMAGE. 35 | -------------------------------------------------------------------------------- /README_l1.txt: -------------------------------------------------------------------------------- 1 | What the l1 addition is 2 | ======================= 3 | A slight modification that allows l1 regularized LikelihoodModel. 4 | 5 | Regularization is handled by a fit_regularized method. 6 | 7 | Main Files 8 | ========== 9 | 10 | l1_demo/demo.py 11 | $ python demo.py --get_l1_slsqp_results logit 12 | does a quick demo of the regularization using logistic regression. 13 | 14 | l1_demo/sklearn_compare.py 15 | $ python sklearn_compare.py 16 | Plots a comparison of regularization paths. Modify the source to use 17 | different datasets. 18 | 19 | statsmodels/base/l1_cvxopt.py 20 | fit_l1_cvxopt_cp() 21 | Fit likelihood model using l1 regularization. Use the CVXOPT package. 22 | Lots of small functions supporting fit_l1_cvxopt_cp 23 | 24 | statsmodels/base/l1_slsqp.py 25 | fit_l1_slsqp() 26 | Fit likelihood model using l1 regularization. Use scipy.optimize 27 | Lots of small functions supporting fit_l1_slsqp 28 | 29 | statsmodels/base/l1_solvers_common.py 30 | Common methods used by l1 solvers 31 | 32 | statsmodels/base/model.py 33 | Likelihoodmodel.fit() 34 | 3 lines modified to allow for importing and calling of l1 fitting functions 35 | 36 | statsmodels/discrete/discrete_model.py 37 | L1MultinomialResults class 38 | Child of MultinomialResults 39 | MultinomialModel.fit() 40 | 3 lines re-directing l1 fit results to the L1MultinomialResults class 41 | -------------------------------------------------------------------------------- /docs/en/GLMNotes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/en/GLMNotes.pdf -------------------------------------------------------------------------------- /docs/en/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = 6 | SPHINXBUILD = sphinx-build 7 | SPHINXPROJ = statsmodels 8 | SOURCEDIR = source 9 | BUILDDIR = build 10 | 11 | PAPER = 12 | TOOLSPATH = ../tools/ 13 | DATASETBUILD = dataset_rst.py 14 | EXAMPLEBUILD = examples_rst.py 15 | NOTEBOOKBUILD = nbgenerate.py 16 | FOLDTOC = fold_toc.py 17 | 18 | # Internal variables. 19 | PAPEROPT_a4 = -D latex_paper_size=a4 20 | PAPEROPT_letter = -D latex_paper_size=letter 21 | ALLSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) 22 | 23 | 24 | # Put it first so that "make" without argument is like "make help". 25 | help: 26 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 27 | 28 | .PHONY: help Makefile 29 | 30 | cleanall: 31 | @$(SPHINXBUILD) -M clean "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 32 | -rm source/examples/generated/* 33 | -rm -rf source/examples/notebooks/generated/* 34 | -rm -rf ../tools/hash_dict.pickle 35 | -rm -rf source/datasets/generated/* 36 | 37 | notebooks: 38 | @echo "Generating notebooks from examples/notebooks folder" 39 | $(TOOLSPATH)$(NOTEBOOKBUILD) --execute=True --allow_errors=True 40 | 41 | html: 42 | # make directories for images 43 | @echo "Make static directory for images" 44 | mkdir -p $(BUILDDIR)/html/_static 45 | # generate the examples rst files 46 | @echo "Generating reST from examples folder" 47 | #$(TOOLSPATH)$(EXAMPLEBUILD) 48 | @echo "Generating datasets from installed statsmodels.datasets" 49 | $(TOOLSPATH)$(DATASETBUILD) 50 | @echo "Generating notebooks from examples/notebooks folder" 51 | $(TOOLSPATH)$(NOTEBOOKBUILD) --parallel --report-errors --skip-existing 52 | @echo "Running sphinx-build" 53 | @echo @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 54 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 55 | @echo "Copying rendered example notebooks" 56 | mkdir -p $(BUILDDIR)/html/examples/notebooks/generated 57 | cp source/examples/notebooks/generated/*html $(BUILDDIR)/html/examples/notebooks/generated 58 | @echo $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/index.html 59 | $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/index.html 60 | @echo $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/dev/index.html ../_static 61 | $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/dev/index.html ../_static 62 | 63 | # Catch-all target: route all unknown targets to Sphinx using the new 64 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 65 | %: Makefile 66 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 67 | -------------------------------------------------------------------------------- /docs/en/README.md: -------------------------------------------------------------------------------- 1 | # Documentation Documentation 2 | 3 | We use a combination of sphinx and Jupyter notebooks for the documentation. 4 | Jupyter notebooks should be used for longer, self-contained examples demonstrating 5 | a topic. 6 | Sphinx is nice because we get the tables of contents and API documentation. 7 | 8 | ## Build Process 9 | 10 | Building the docs requires a few additional dependencies. You can get most 11 | of these with 12 | 13 | ```bash 14 | 15 | pip install -e .[docs] 16 | 17 | ``` 18 | 19 | From the root of the project. 20 | Some of the examples rely on `rpy2` to execute R code from the notebooks. 21 | It's not included in the setup requires since it's known to be difficult to 22 | install. 23 | 24 | To generate the HTML docs, run ``make html`` from the ``docs`` directory. 25 | This executes a few distinct builds 26 | 27 | 1. datasets 28 | 2. notebooks 29 | 3. sphinx 30 | 31 | # Notebook Builds 32 | 33 | We're using `nbconvert` to execute the notebooks, and then convert them 34 | to HTML. The conversion is handled by `statsmodels/tools/nbgenerate.py`. 35 | The default python kernel (embedded in the notebook) is `python3`. 36 | You need at least `nbconvert==4.2.0` to specify a non-default kernel, 37 | which can be passed in the Makefile. 38 | -------------------------------------------------------------------------------- /docs/en/fix_longtable.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import sys 3 | import os 4 | 5 | 6 | BUILDDIR = sys.argv[-1] 7 | read_file_path = os.path.join(BUILDDIR,'latex','statsmodels.tex') 8 | write_file_path = os.path.join(BUILDDIR, 'latex','statsmodels_tmp.tex') 9 | 10 | read_file = open(read_file_path,'r') 11 | write_file = open(write_file_path, 'w') 12 | 13 | for line in read_file: 14 | if 'longtable}{LL' in line: 15 | line = line.replace('longtable}{LL', 'longtable}{|l|l|') 16 | write_file.write(line) 17 | 18 | read_file.close() 19 | write_file.close() 20 | 21 | os.remove(read_file_path) 22 | os.rename(write_file_path, read_file_path) 23 | -------------------------------------------------------------------------------- /docs/en/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | 11 | set SOURCEDIR=source 12 | set BUILDDIR=build 13 | set SPHINXPROJ=statsmodels 14 | set SPHINXOPTS=-j auto 15 | 16 | set TOOLSPATH=../tools 17 | set DATASETBUILD=dataset_rst.py 18 | set NOTEBOOKBUILD=nbgenerate.py 19 | set FOLDTOC=fold_toc.py 20 | 21 | if "%1" == "" goto help 22 | 23 | %SPHINXBUILD% >NUL 2>NUL 24 | if errorlevel 9009 ( 25 | echo. 26 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 27 | echo.installed, then set the SPHINXBUILD environment variable to point 28 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 29 | echo.may add the Sphinx directory to PATH. 30 | echo. 31 | echo.If you don't have Sphinx installed, grab it from 32 | echo.http://sphinx-doc.org/ 33 | exit /b 1 34 | ) 35 | 36 | if "%1" == "html" ( 37 | echo mkdir %BUILDDIR%\html\_static 38 | mkdir %BUILDDIR%\html\_static 39 | echo python %TOOLSPATH%/%NOTEBOOKBUILD% --parallel --report-errors --skip-existing 40 | python %TOOLSPATH%/%NOTEBOOKBUILD% --parallel --report-errors --skip-existing 41 | echo python %TOOLSPATH%/%DATASETBUILD% 42 | python %TOOLSPATH%/%DATASETBUILD% 43 | ) 44 | 45 | echo %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 46 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 47 | if errorlevel 1 exit /b 1 48 | 49 | if "%1" == "html" ( 50 | echo xcopy /s source/examples/notebooks/generated/*.html %BUILDDIR%/html/examples/notebooks/generated 51 | xcopy /s source/examples/notebooks/generated/*.html %BUILDDIR%/html/examples/notebooks/generated 52 | echo python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/index.html 53 | python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/index.html 54 | echo python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/examples/index.html ../_static 55 | python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/examples/index.html ../_static 56 | echo python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/dev/index.html ../_static 57 | python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/dev/index.html ../_static 58 | if NOT EXIST %BUILDDIR%/html/examples/notebooks/generated mkdir %BUILDDIR%\html\examples\notebooks\generated 59 | ) 60 | 61 | goto end 62 | 63 | :help 64 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 65 | 66 | :end 67 | popd 68 | -------------------------------------------------------------------------------- /docs/en/source/_static/blogger.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/en/source/_static/blogger.png 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30 | margin-bottom: 9px; 31 | background-color: #fff; 32 | } 33 | 34 | /* Example sites showcase */ 35 | .example-sites img { 36 | max-width: 100%; 37 | margin: 0 auto; 38 | } 39 | 40 | .marketing-byline { 41 | font-size: 18px; 42 | font-weight: 300; 43 | line-height: 24px; 44 | color: #999; 45 | text-align: center; 46 | } 47 | 48 | /* From bootstrap.css */ 49 | .thumbnails { 50 | margin-left: -20px; 51 | list-style: none; 52 | *zoom: 1; 53 | } 54 | 55 | .thumbnails:before, 56 | .thumbnails:after { 57 | display: table; 58 | line-height: 0; 59 | content: ""; 60 | } 61 | 62 | .thumbnails:after { 63 | clear: both; 64 | } 65 | 66 | .row-fluid .thumbnails { 67 | margin-left: 0; 68 | } 69 | 70 | .thumbnails > li { 71 | float: left; 72 | margin-bottom: 20px; 73 | margin-left: 20px; 74 | } 75 | 76 | .thumbnail { 77 | display: block; 78 | padding: 4px; 79 | line-height: 20px; 80 | border: 1px solid #ddd; 81 | -webkit-border-radius: 4px; 82 | -moz-border-radius: 4px; 83 | border-radius: 4px; 84 | -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); 85 | -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); 86 | box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); 87 | -webkit-transition: all 0.2s ease-in-out; 88 | -moz-transition: all 0.2s ease-in-out; 89 | -o-transition: all 0.2s ease-in-out; 90 | transition: all 0.2s ease-in-out; 91 | } 92 | 93 | a.thumbnail:hover, 94 | a.thumbnail:focus { 95 | border-color: #0088cc; 96 | -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); 97 | -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); 98 | box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); 99 | } 100 | 101 | .thumbnail > img { 102 | display: block; 103 | width: 360px; 104 | max-width: 100%; 105 | height: auto; 106 | margin-right: auto; 107 | margin-left: auto; 108 | } 109 | 110 | .thumbnail .caption { 111 | padding: 9px; 112 | color: #555555; 113 | } 114 | -------------------------------------------------------------------------------- /docs/en/source/_static/facebox.css: -------------------------------------------------------------------------------- 1 | #facebox { 2 | position: absolute; 3 | top: 0; 4 | left: 0; 5 | z-index: 100; 6 | text-align: left; 7 | } 8 | 9 | 10 | #facebox .popup{ 11 | position:relative; 12 | border:3px solid rgba(0,0,0,0); 13 | -webkit-border-radius:5px; 14 | -moz-border-radius:5px; 15 | border-radius:5px; 16 | -webkit-box-shadow:0 0 18px rgba(0,0,0,0.4); 17 | -moz-box-shadow:0 0 18px rgba(0,0,0,0.4); 18 | box-shadow:0 0 18px rgba(0,0,0,0.4); 19 | } 20 | 21 | #facebox .content { 22 | display:table; 23 | width: 370px; 24 | padding: 10px; 25 | background: #fff; 26 | -webkit-border-radius:4px; 27 | -moz-border-radius:4px; 28 | border-radius:4px; 29 | } 30 | 31 | #facebox .content > p:first-child{ 32 | margin-top:0; 33 | } 34 | #facebox .content > p:last-child{ 35 | margin-bottom:0; 36 | } 37 | 38 | #facebox .close{ 39 | position:absolute; 40 | top:5px; 41 | right:5px; 42 | padding:2px; 43 | background:#fff; 44 | } 45 | #facebox 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ul.mktree li { margin-left:10px; padding:0px; } 7 | /* Provide space for our own "bullet" inside the LI */ 8 | ul.mktree li .bullet { padding-left: 15px; } 9 | /* Show "bullets" in the links, depending on the class of the LI that the link's in */ 10 | ul.mktree li.liOpen .bullet { cursor: pointer; background: url(minus.gif) center left no-repeat; } 11 | ul.mktree li.liClosed .bullet { cursor: pointer; background: url(plus.gif) center left no-repeat; } 12 | ul.mktree li.liBullet .bullet { cursor: default; background: url(bullet.gif) center left no-repeat; } 13 | /* Sublists are visible or not based on class of parent LI */ 14 | ul.mktree li.liOpen ul { display: block; } 15 | ul.mktree li.liClosed ul { display: none; } 16 | 17 | /* Format menu items differently depending on what level of the tree they are in */ 18 | /* Uncomment this if you want your fonts to decrease in size the deeper they are in the tree */ 19 | /* 20 | ul.mktree li ul li { font-size: 90% } 21 | */ 22 | 23 | } 24 | 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13 | 14 | return codebox; 15 | } 16 | 17 | function htmlescape(text){ 18 | return (text.replace(/&/g, "&") 19 | .replace(//g, ">") 21 | .replace(/"/g, """) 22 | .replace(/'/g, "'")) 23 | } 24 | 25 | function scrapeText(codebox){ 26 | /// Returns input lines cleaned of prompt1 and prompt2 27 | var lines = codebox.split('\n'); 28 | var newlines = new Array(); 29 | $.each(lines, function() { 30 | if (this.match(/^In \[\d+]: /)){ 31 | newlines.push(this.replace(/^(\s)*In \[\d+]: /,"")); 32 | } 33 | else if (this.match(/^(\s)*\.+:/)){ 34 | newlines.push(this.replace(/^(\s)*\.+: /,"")); 35 | } 36 | 37 | } 38 | ); 39 | return newlines.join('\\n'); 40 | } 41 | 42 | $(document).ready( 43 | function() { 44 | // grab all code boxes 45 | var ipythoncode = $(".highlight-ipython"); 46 | $.each(ipythoncode, function() { 47 | var codebox = scrapeText($(this).text()); 48 | // give them a facebox pop-up with plain text code 49 | $(this).append('View Code'); 50 | $(this,"textarea").select(); 51 | }); 52 | }); 53 | -------------------------------------------------------------------------------- /docs/en/source/_templates/autosummary/class.rst: -------------------------------------------------------------------------------- 1 | {{ fullname }} 2 | {{ underline }} 3 | 4 | .. currentmodule:: {{ module }} 5 | 6 | .. autoclass:: {{ objname }} 7 | 8 | {% block methods %} 9 | 10 | {% if methods %} 11 | .. rubric:: Methods 12 | 13 | .. autosummary:: 14 | :toctree: 15 | {% for item in methods %} 16 | {% if item != '__init__' %} 17 | ~{{ name }}.{{ item }} 18 | {% endif %} 19 | {%- endfor %} 20 | {% endif %} 21 | {% endblock %} 22 | 23 | {% block attributes %} 24 | {% if attributes %} 25 | .. rubric:: Attributes 26 | 27 | .. autosummary:: 28 | {% for item in attributes %} 29 | ~{{ name }}.{{ item }} 30 | {%- endfor %} 31 | {% endif %} 32 | {% endblock %} 33 | -------------------------------------------------------------------------------- /docs/en/source/_templates/autosummary/glmfamilies.rst: -------------------------------------------------------------------------------- 1 | {{ fullname }} 2 | {{ underline }} 3 | 4 | .. currentmodule:: {{ module }} 5 | 6 | .. autoclass:: {{ objname }} 7 | 8 | {% block methods %} 9 | 10 | {% if methods %} 11 | .. rubric:: Methods 12 | 13 | .. autosummary:: 14 | :toctree: 15 | {% for item in methods %} 16 | {% if item != '__init__' %} 17 | ~{{ name }}.{{ item }} 18 | {% endif %} 19 | {%- endfor %} 20 | {% endif %} 21 | {% endblock %} 22 | -------------------------------------------------------------------------------- /docs/en/source/anova.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.stats.anova 2 | 3 | .. _anova: 4 | 5 | ANOVA 6 | ===== 7 | 8 | Analysis of Variance models containing anova_lm for ANOVA analysis with a 9 | linear OLSModel, and AnovaRM for repeated measures ANOVA, within ANOVA for 10 | balanced data. 11 | 12 | Examples 13 | -------- 14 | 15 | .. ipython:: python 16 | 17 | import statsmodels.api as sm 18 | from statsmodels.formula.api import ols 19 | 20 | moore = sm.datasets.get_rdataset("Moore", "carData", 21 | cache=True) # load data 22 | data = moore.data 23 | data = data.rename(columns={"partner.status": 24 | "partner_status"}) # make name pythonic 25 | moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)', 26 | data=data).fit() 27 | 28 | table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 ANOVA DataFrame 29 | print(table) 30 | 31 | A more detailed example for `anova_lm` can be found here: 32 | 33 | * `ANOVA `__ 34 | 35 | Module Reference 36 | ---------------- 37 | 38 | .. module:: statsmodels.stats.anova 39 | :synopsis: Analysis of Variance 40 | 41 | .. autosummary:: 42 | :toctree: generated/ 43 | 44 | anova_lm 45 | AnovaRM 46 | -------------------------------------------------------------------------------- /docs/en/source/dev/examples.rst: -------------------------------------------------------------------------------- 1 | .. _examples: 2 | 3 | Examples 4 | ======== 5 | 6 | Examples are invaluable for new users who hope to get up and running quickly 7 | with `statsmodels`, and they are extremely useful to those who wish to explore 8 | new features of `statsmodels`. We hope to provide documentation and tutorials 9 | for as many models and use-cases as possible! Please consider submitting an example with any PR that introduces new functionality. 10 | 11 | User-contributed examples/tutorials/recipes can be placed on the 12 | `statsmodels examples wiki page `_ 13 | That wiki page is freely editable. Please post your cool tricks, 14 | examples, and recipes on there! 15 | 16 | If you would rather have your example file officially accepted to the 17 | `statsmodels` distribution and posted on this website, you will need to go 18 | through the normal `patch submission process `_ and follow the instructions that follow. 19 | 20 | File Format 21 | ~~~~~~~~~~~ 22 | 23 | Examples are best contributed as IPython notebooks. Save your notebook with all output cells cleared in ``examples/notebooks``. From the notebook save the pure Python output to ``examples/python``. The first line of the Notebook *must* be a header cell that contains a title for the notebook, if you want the notebook to be included in the documentation. 24 | 25 | 26 | The Example Gallery 27 | ~~~~~~~~~~~~~~~~~~~ 28 | 29 | We have a gallery of example notebooks available `here `_. If you would like your example to show up in this gallery, add a link to the notebook in ``docs/source/examples/landing.json``. For the thumbnail, take a screenshot of what you think is the best "hook" for the notebook. The image will be displayed at 360 x 225 (W x H). It's best to save the image as a PNG with a resolution that is some multiple of 360 x 225 (720 x 450 is preferred). 30 | 31 | 32 | Before submitting a PR 33 | ~~~~~~~~~~~~~~~~~~~~~~ 34 | 35 | To save you some time and to make the new examples nicely fit into the existing 36 | ones consider the following points. 37 | 38 | **Look at examples source code** to get a feel for how statsmodels examples should look like. 39 | 40 | **Build the docs** by running `make html` from the docs directory to see how your example looks in the fully rendered html pages. 41 | -------------------------------------------------------------------------------- /docs/en/source/dev/get_involved.rst: -------------------------------------------------------------------------------- 1 | Get Involved 2 | ============ 3 | 4 | Where to Start? 5 | --------------- 6 | 7 | Use grep or download a tool like `grin `__ to search the code for TODO notes:: 8 | 9 | grin -i -I "*.py*" todo 10 | 11 | This shows almost 700 TODOs in the code base right now. Feel free to inquire on the mailing list about any of these. 12 | 13 | Sandbox 14 | ------- 15 | 16 | We currently have a large amount code in the :ref:`sandbox`. The medium term goal is to move much of this to feature branches as it gets worked on and remove the sandbox folder. Many of these models and functions are close to done, however, and we welcome any and all contributions to complete them, including refactoring, documentation, and tests. These models include generalized additive models (GAM), information theoretic models such as maximum entropy, survival models, systems of equation models, restricted least squares, panel data models, and time series models such as (G)ARCH. 17 | 18 | .. .. toctree:: 19 | .. :maxdepth: 4 20 | .. 21 | .. ../sandbox 22 | 23 | Contribute an Example 24 | --------------------- 25 | 26 | Contribute an :ref:`example `, add some technical documentation, or contribute a statistics tutorial. 27 | -------------------------------------------------------------------------------- /docs/en/source/dev/images/git_merge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/en/source/dev/images/git_merge.png -------------------------------------------------------------------------------- /docs/en/source/dev/internal.rst: -------------------------------------------------------------------------------- 1 | .. _model: 2 | 3 | 4 | 5 | Internal Classes 6 | ================ 7 | 8 | The following summarizes classes and functions that are not intended to be 9 | directly used, but of interest only for internal use or for a developer who 10 | wants to extend on existing model classes. 11 | 12 | 13 | Module Reference 14 | ---------------- 15 | 16 | Model and Results Classes 17 | ^^^^^^^^^^^^^^^^^^^^^^^^^ 18 | 19 | These are the base classes for both the estimation models and the results. 20 | They are not directly useful, but layout the structure of the subclasses and 21 | define some common methods. 22 | 23 | .. module:: statsmodels.base.model 24 | :synopsis: Base classes that are inherited by models 25 | 26 | .. currentmodule:: statsmodels.base.model 27 | 28 | .. autosummary:: 29 | :toctree: generated/ 30 | 31 | Model 32 | LikelihoodModel 33 | GenericLikelihoodModel 34 | Results 35 | LikelihoodModelResults 36 | ResultMixin 37 | GenericLikelihoodModelResults 38 | 39 | .. module:: statsmodels.stats.contrast 40 | :synopsis: Classes for statistical test 41 | 42 | .. currentmodule:: statsmodels.stats.contrast 43 | 44 | .. autosummary:: 45 | :toctree: generated/ 46 | 47 | ContrastResults 48 | 49 | .. inheritance-diagram:: statsmodels.base.model statsmodels.discrete.discrete_model statsmodels.regression.linear_model statsmodels.miscmodels.count 50 | :parts: 3 51 | 52 | 53 | .. inheritance-diagram:: statsmodels.regression.linear_model.GLS statsmodels.regression.linear_model.WLS statsmodels.regression.linear_model.OLS statsmodels.regression.linear_model.GLSAR 54 | :parts: 1 55 | 56 | Linear Model 57 | ^^^^^^^^^^^^ 58 | 59 | .. inheritance-diagram:: statsmodels.regression.linear_model 60 | :parts: 1 61 | 62 | Generalized Linear Model 63 | ^^^^^^^^^^^^^^^^^^^^^^^^ 64 | 65 | .. inheritance-diagram:: statsmodels.genmod.generalized_linear_model 66 | statsmodels.genmod.families.family statsmodels.genmod.families.links 67 | :parts: 1 68 | 69 | Discrete Model 70 | ^^^^^^^^^^^^^^ 71 | 72 | .. inheritance-diagram:: statsmodels.discrete.discrete_model 73 | :parts: 1 74 | 75 | Robust Model 76 | ^^^^^^^^^^^^ 77 | 78 | .. inheritance-diagram:: statsmodels.robust.robust_linear_model 79 | :parts: 1 80 | 81 | Vector Autoregressive Model 82 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ 83 | 84 | .. inheritance-diagram:: statsmodels.tsa.vector_ar.var_model 85 | :parts: 3 86 | -------------------------------------------------------------------------------- /docs/en/source/dev/testing.rst: -------------------------------------------------------------------------------- 1 | Testing on Build Machines 2 | ------------------------- 3 | 4 | There are currently several places that statsmodels is automatically built and tested against different dependency and Python versions and architectures. Check these logs periodically, make sure everything looks okay, and fix any failures.: 5 | 6 | * `Travis CI `_ 7 | * `Daily testing on Ubuntu via Python(x,y) `_ 8 | * `NiPy testing on SPARC Boxes `_ 9 | 10 | The test coverage pages are here. 11 | 12 | * `Coveralls `_ 13 | -------------------------------------------------------------------------------- /docs/en/source/distributions.rst: -------------------------------------------------------------------------------- 1 | .. module:: statsmodels.sandbox.distributions 2 | :synopsis: Probability distributions 3 | 4 | .. currentmodule:: statsmodels.sandbox.distributions 5 | 6 | .. _distributions: 7 | 8 | 9 | Distributions 10 | ============= 11 | 12 | This section collects various additional functions and methods for statistical 13 | distributions. 14 | 15 | Empirical Distributions 16 | ----------------------- 17 | 18 | .. module:: statsmodels.distributions.empirical_distribution 19 | :synopsis: Tools for working with empirical distributions 20 | 21 | .. currentmodule:: statsmodels.distributions.empirical_distribution 22 | 23 | .. autosummary:: 24 | :toctree: generated/ 25 | 26 | ECDF 27 | StepFunction 28 | monotone_fn_inverter 29 | 30 | Distribution Extras 31 | ------------------- 32 | 33 | 34 | .. module:: statsmodels.sandbox.distributions.extras 35 | :synopsis: Probability distributions and random number generators 36 | 37 | .. currentmodule:: statsmodels.sandbox.distributions.extras 38 | 39 | *Skew Distributions* 40 | 41 | .. autosummary:: 42 | :toctree: generated/ 43 | 44 | SkewNorm_gen 45 | SkewNorm2_gen 46 | ACSkewT_gen 47 | skewnorm2 48 | 49 | *Distributions based on Gram-Charlier expansion* 50 | 51 | .. autosummary:: 52 | :toctree: generated/ 53 | 54 | pdf_moments_st 55 | pdf_mvsk 56 | pdf_moments 57 | NormExpan_gen 58 | 59 | *cdf of multivariate normal* wrapper for scipy.stats 60 | 61 | 62 | .. autosummary:: 63 | :toctree: generated/ 64 | 65 | mvstdnormcdf 66 | mvnormcdf 67 | 68 | Univariate Distributions by non-linear Transformations 69 | ------------------------------------------------------ 70 | 71 | Univariate distributions can be generated from a non-linear transformation of an 72 | existing univariate distribution. `Transf_gen` is a class that can generate a new 73 | distribution from a monotonic transformation, `TransfTwo_gen` can use hump-shaped 74 | or u-shaped transformation, such as abs or square. The remaining objects are 75 | special cases. 76 | 77 | .. module:: statsmodels.sandbox.distributions.transformed 78 | :synopsis: Experimental probability distributions and random number generators 79 | 80 | .. currentmodule:: statsmodels.sandbox.distributions.transformed 81 | 82 | .. autosummary:: 83 | :toctree: generated/ 84 | 85 | TransfTwo_gen 86 | Transf_gen 87 | 88 | ExpTransf_gen 89 | LogTransf_gen 90 | SquareFunc 91 | 92 | absnormalg 93 | invdnormalg 94 | 95 | loggammaexpg 96 | lognormalg 97 | negsquarenormalg 98 | 99 | squarenormalg 100 | squaretg 101 | -------------------------------------------------------------------------------- /docs/en/source/emplike.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.emplike 2 | 3 | 4 | .. _emplike: 5 | 6 | 7 | Empirical Likelihood :mod:`emplike` 8 | ==================================== 9 | 10 | 11 | Introduction 12 | ------------ 13 | 14 | Empirical likelihood is a method of nonparametric inference and estimation that lifts the 15 | obligation of having to specify a family of underlying distributions. Moreover, empirical 16 | likelihood methods do not require re-sampling but still 17 | uniquely determine confidence regions whose shape mirrors the shape of the data. 18 | In essence, empirical likelihood attempts to combine the benefits of parametric 19 | and nonparametric methods while limiting their shortcomings. The main difficulties of 20 | empirical likelihood is the computationally intensive methods required to conduct inference. 21 | :mod:`statsmodels.emplike` attempts to provide a user-friendly interface that allows the 22 | end user to effectively conduct empirical likelihood analysis without having to concern 23 | themselves with the computational burdens. 24 | 25 | Currently, :mod:`emplike` provides methods to conduct hypothesis tests and form confidence 26 | intervals for descriptive statistics. Empirical likelihood estimation and inference 27 | in a regression, accelerated failure time and instrumental variable model are 28 | currently under development. 29 | 30 | References 31 | ^^^^^^^^^^ 32 | 33 | The main reference for empirical likelihood is:: 34 | 35 | Owen, A.B. "Empirical Likelihood." Chapman and Hall, 2001. 36 | 37 | 38 | 39 | Examples 40 | -------- 41 | 42 | .. ipython:: python 43 | 44 | import numpy as np 45 | import statsmodels.api as sm 46 | 47 | # Generate Data 48 | x = np.random.standard_normal(50) 49 | 50 | # initiate EL 51 | el = sm.emplike.DescStat(x) 52 | 53 | # confidence interval for the mean 54 | el.ci_mean() 55 | 56 | # test variance is 1 57 | el.test_var(1) 58 | 59 | 60 | Module Reference 61 | ---------------- 62 | 63 | .. module:: statsmodels.emplike 64 | :synopsis: Empirical likelihood tools 65 | 66 | .. autosummary:: 67 | :toctree: generated/ 68 | 69 | descriptive.DescStat 70 | descriptive.DescStatUV 71 | descriptive.DescStatMV 72 | -------------------------------------------------------------------------------- /docs/en/source/examples/README: -------------------------------------------------------------------------------- 1 | The format for landing.json should be self-explanatory. The images should be placed in docs/source/_static/images/. They will be displayed at 360 x 225 (W x H). It's best to save them as a png with a resolution of a multiple of at least 720 x 450. If you want, you can use png crush to make the images smaller. 2 | -------------------------------------------------------------------------------- /docs/en/source/examples/index.rst: -------------------------------------------------------------------------------- 1 | :orphan: 2 | 3 | .. _statsmodels-examples: 4 | 5 | Statsmodels Examples 6 | ==================== 7 | 8 | This page provides a series of examples, tutorials and recipes to help you get 9 | started with ``statsmodels``. Each of the examples shown here is made available 10 | as an IPython Notebook and as a plain python script on the `statsmodels github 11 | repository `_. 12 | 13 | We also encourage users to submit their own examples, tutorials or cool 14 | `statsmodels` trick to the `Examples wiki page 15 | `_ 16 | 17 | The Examples 18 | ------------ 19 | 20 | .. toctree:: 21 | :maxdepth: 3 22 | :glob: 23 | 24 | notebooks/generated/* 25 | -------------------------------------------------------------------------------- /docs/en/source/extending.rst.TXT: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/en/source/extending.rst.TXT -------------------------------------------------------------------------------- /docs/en/source/faq.rst: -------------------------------------------------------------------------------- 1 | :orphan: 2 | 3 | .. currentmodule:: statsmodels 4 | 5 | .. _faq: 6 | 7 | Frequently Asked Question 8 | ------------------------- 9 | 10 | .. _endog-exog-faq: 11 | 12 | What do endog and exog mean? 13 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 14 | 15 | These are shorthand for endogenous and exogenous variables. You might be more comfortable with the common ``y`` and ``X`` notation in linear models. Sometimes the endogenous variable ``y`` is called a dependent variable. Likewise, sometimes the exogenous variables ``X`` are called the independent variables. You can read about this in greater detail at :ref:`endog_exog` 16 | 17 | 18 | .. _missing-faq: 19 | 20 | How does statsmodels handle missing data? 21 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 22 | 23 | Missing data can be handled via the ``missing`` keyword argument. Every model takes this keyword. You can find more information in the docstring of :class:`statsmodels.base.Model `. 24 | 25 | .. `Model class `_. 26 | 27 | .. _build-faq: 28 | 29 | Why won't statsmodels build? 30 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 31 | 32 | If you're on Python 3.4, you *must* use Cython 0.20.1. If you're still having problems, try running 33 | 34 | .. code-block:: bash 35 | 36 | python setup.py clean 37 | 38 | What if my question isn't answered here? 39 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 40 | 41 | You may find answers for questions that have not yet been added here on GitHub under the `FAQ issues tag `_. If not, please ask your question on stackoverflow using the `statsmodels tag `_ or on the `mailing list `_. 42 | -------------------------------------------------------------------------------- /docs/en/source/genericmle.rst.TXT: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/en/source/genericmle.rst.TXT -------------------------------------------------------------------------------- /docs/en/source/glm_techn1.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.glm 2 | 3 | 4 | .. _glm_techn1: 5 | 6 | Technical Documentation 7 | ======================= 8 | 9 | Introduction 10 | ------------ 11 | 12 | Just a placeholder 13 | -------------------------------------------------------------------------------- /docs/en/source/glm_techn2.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.glm 2 | 3 | 4 | .. _glm_techn2: 5 | 6 | Technical Documentation - part 2 7 | ================================ 8 | 9 | Implementation Notes 10 | -------------------- 11 | 12 | Just a placeholder 13 | -------------------------------------------------------------------------------- /docs/en/source/gmm.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.sandbox.regression.gmm 2 | 3 | 4 | .. _gmm: 5 | 6 | 7 | Generalized Method of Moments :mod:`gmm` 8 | ======================================== 9 | 10 | :mod:`statsmodels.gmm` contains model classes and functions that are based on 11 | estimation with Generalized Method of Moments. 12 | Currently the general non-linear case is implemented. An example class for the standard 13 | linear instrumental variable model is included. This has been introduced as a test case, it 14 | works correctly but it does not take the linear structure into account. For the linear 15 | case we intend to introduce a specific implementation which will be faster and numerically 16 | more accurate. 17 | 18 | Currently, GMM takes arbitrary non-linear moment conditions and calculates the estimates 19 | either for a given weighting matrix or iteratively by alternating between estimating 20 | the optimal weighting matrix and estimating the parameters. Implementing models with 21 | different moment conditions is done by subclassing GMM. In the minimal implementation 22 | only the moment conditions, `momcond` have to be defined. 23 | 24 | .. currentmodule:: statsmodels.sandbox.regression.gmm 25 | 26 | 27 | Module Reference 28 | """""""""""""""" 29 | 30 | .. module:: statsmodels.sandbox.regression.gmm 31 | :synopsis: A framework for implementing Generalized Method of Moments (GMM) 32 | 33 | .. autosummary:: 34 | :toctree: generated/ 35 | 36 | GMM 37 | GMMResults 38 | IV2SLS 39 | IVGMM 40 | IVGMMResults 41 | IVRegressionResults 42 | LinearIVGMM 43 | NonlinearIVGMM 44 | -------------------------------------------------------------------------------- /docs/en/source/gmm_techn1.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.sandbox.regression.gmm 2 | 3 | 4 | .. _gmm_techn1: 5 | 6 | Technical Documentation 7 | ======================= 8 | 9 | Introduction 10 | ------------ 11 | 12 | Generalized Method of Moments is an extension of the Method of Moments 13 | if there are more moment conditions than parameters that are estimated. 14 | 15 | simple example 16 | 17 | 18 | General Structure and Implementation 19 | ------------------------------------ 20 | 21 | The main class for GMM estimation, makes little assumptions about the 22 | moment conditions. It is designed for the general case when moment 23 | conditions are given as function by the user. 24 | 25 | :: 26 | 27 | def momcond(params) 28 | 29 | which should return a two dimensional array with observation in rows 30 | and moment conditions in columns. Denote this function by `$g(\theta)$`. Then 31 | the GMM estimator is given as the solution to the maximization problem: 32 | 33 | ..math: max_{\theta) g(theta)' W g(theta) (1) 34 | 35 | The weighting matrix can be estimated in several different ways. The 36 | basic method `fitgmm` takes the weighting matrix as argument or if it is 37 | not given takes the identity matrix and maximizes (1) 38 | taking W as given. Since the optimizing functions solve minimization problems, 39 | we usually minimizes the negative of the objective function. 40 | `fit_iterative` calculates the optimal weighting matrix and maximizes the 41 | criterion function in alternating steps. The number of iterations can 42 | be given as an argument to this fit method. The optimal weighting matrix, 43 | which is the covariance matrix of the moment conditions, can be estimated 44 | in different ways. Kernel and shrinkage estimators are planned but not yet 45 | implemented. TODO 46 | 47 | The GMM class itself does not define any moment conditions. To get an 48 | estimator for given moment conditions, GMM needs to be subclassed. 49 | The basic structure of writing new models based on 50 | the generic MLE or GMM framework and subclassing is described in 51 | `extending.rst` (TODO: link) 52 | 53 | As an example 54 | -------------------------------------------------------------------------------- /docs/en/source/graphics.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.graphics 2 | 3 | .. _graphics: 4 | 5 | Graphics 6 | ======== 7 | 8 | .. automodule:: statsmodels.graphics 9 | 10 | Goodness of Fit Plots 11 | --------------------- 12 | 13 | .. autosummary:: 14 | :toctree: generated/ 15 | 16 | gofplots.qqplot 17 | gofplots.qqline 18 | gofplots.qqplot_2samples 19 | gofplots.ProbPlot 20 | 21 | Boxplots 22 | -------- 23 | 24 | .. autosummary:: 25 | :toctree: generated/ 26 | 27 | boxplots.violinplot 28 | boxplots.beanplot 29 | 30 | Correlation Plots 31 | ------------------ 32 | 33 | .. autosummary:: 34 | :toctree: generated/ 35 | 36 | correlation.plot_corr 37 | correlation.plot_corr_grid 38 | plot_grids.scatter_ellipse 39 | 40 | Functional Plots 41 | ---------------- 42 | 43 | .. autosummary:: 44 | :toctree: generated/ 45 | 46 | functional.hdrboxplot 47 | functional.fboxplot 48 | functional.rainbowplot 49 | functional.banddepth 50 | 51 | Regression Plots 52 | ---------------- 53 | 54 | .. autosummary:: 55 | :toctree: generated/ 56 | 57 | regressionplots.plot_fit 58 | regressionplots.plot_regress_exog 59 | regressionplots.plot_partregress 60 | regressionplots.plot_ccpr 61 | regressionplots.abline_plot 62 | regressionplots.influence_plot 63 | regressionplots.plot_leverage_resid2 64 | 65 | Time Series Plots 66 | ----------------- 67 | 68 | .. autosummary:: 69 | :toctree: generated/ 70 | 71 | tsaplots.plot_acf 72 | tsaplots.plot_pacf 73 | tsaplots.month_plot 74 | tsaplots.quarter_plot 75 | 76 | Other Plots 77 | ----------- 78 | 79 | .. autosummary:: 80 | :toctree: generated/ 81 | 82 | factorplots.interaction_plot 83 | mosaicplot.mosaic 84 | agreement.mean_diff_plot 85 | -------------------------------------------------------------------------------- /docs/en/source/images/aw.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/en/source/images/aw.png -------------------------------------------------------------------------------- /docs/en/source/images/hl.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/en/source/images/hl.png -------------------------------------------------------------------------------- /docs/en/source/images/ht.png: -------------------------------------------------------------------------------- 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The 15 | basic idea is to treat each variable with missing values as the 16 | dependent variable in a regression, with some or all of the remaining 17 | variables as its predictors. The MICE procedure cycles through these 18 | models, fitting each in turn, then uses a procedure called "predictive 19 | mean matching" (PMM) to generate random draws from the predictive 20 | distributions determined by the fitted models. These random draws 21 | become the imputed values for one imputed data set. 22 | 23 | By default, each variable with missing variables is modeled using a 24 | linear regression with main effects for all other variables in the 25 | data set. Note that even when the imputation model is linear, the PMM 26 | procedure preserves the domain of each variable. Thus, for example, 27 | if all observed values for a given variable are positive, all imputed 28 | values for the variable will always be positive. The user also has 29 | the option to specify which model is used to produce imputed values 30 | for each variable. 31 | 32 | .. code 33 | 34 | 35 | Classes 36 | ------- 37 | 38 | .. currentmodule:: statsmodels.imputation.mice 39 | 40 | .. autosummary:: 41 | :toctree: generated/ 42 | 43 | MICE 44 | MICEData 45 | 46 | 47 | Implementation Details 48 | ---------------------- 49 | 50 | Internally, this function uses 51 | `pandas.isnull `_. 52 | Anything that returns True from this function will be treated as missing data. 53 | -------------------------------------------------------------------------------- /docs/en/source/iolib.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.iolib 2 | 3 | .. _iolib: 4 | 5 | Input-Output :mod:`iolib` 6 | ========================= 7 | 8 | ``statsmodels`` offers some functions for input and output. These include a 9 | reader for STATA files, a class for generating tables for printing in several 10 | formats and two helper functions for pickling. 11 | 12 | Users can also leverage the powerful input/output functions provided by :ref:`pandas.io `. Among other things, ``pandas`` (a ``statsmodels`` dependency) allows reading and writing to Excel, CSV, and HDF5 (PyTables). 13 | 14 | Examples 15 | -------- 16 | 17 | `SimpleTable: Basic example `__ 18 | 19 | Module Reference 20 | ---------------- 21 | 22 | .. module:: statsmodels.iolib 23 | :synopsis: Tools for reading datasets and producing summary output 24 | 25 | .. autosummary:: 26 | :toctree: generated/ 27 | 28 | foreign.StataReader 29 | foreign.StataWriter 30 | foreign.genfromdta 31 | foreign.savetxt 32 | table.SimpleTable 33 | table.csv2st 34 | smpickle.save_pickle 35 | smpickle.load_pickle 36 | 37 | 38 | The following are classes and functions used to return the summary of 39 | estimation results, and mostly intended for internal use. There are currently 40 | two versions for creating summaries. 41 | 42 | .. autosummary:: 43 | :toctree: generated/ 44 | 45 | summary.Summary 46 | summary2.Summary 47 | -------------------------------------------------------------------------------- /docs/en/source/miscmodels.rst: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | .. module:: statsmodels.miscmodels 5 | .. currentmodule:: statsmodels.miscmodels 6 | 7 | 8 | .. _miscmodels: 9 | 10 | 11 | Other Models :mod:`miscmodels` 12 | ============================== 13 | 14 | :mod:`statsmodels.miscmodels` contains model classes and that do not yet fit into 15 | any other category, or are basic implementations that are not yet polished and will most 16 | likely still change. Some of these models were written as examples for the generic 17 | maximum likelihood framework, and there will be others that might be based on general 18 | method of moments. 19 | 20 | The models in this category have been checked for basic cases, but might be more exposed 21 | to numerical problems than the complete implementation. For example, count.Poisson has 22 | been added using only the generic maximum likelihood framework, the standard errors 23 | are based on the numerical evaluation of the Hessian, while discretemod.Poisson uses 24 | analytical Gradients and Hessian and will be more precise, especially in cases when there 25 | is strong multicollinearity. 26 | On the other hand, by subclassing GenericLikelihoodModel, it is easy to add new models, 27 | another example can be seen in the zero inflated Poisson model, miscmodels.count. 28 | 29 | 30 | Count Models :mod:`count` 31 | -------------------------- 32 | 33 | .. module:: statsmodels.miscmodels.count 34 | .. currentmodule:: statsmodels.miscmodels.count 35 | 36 | .. autosummary:: 37 | :toctree: generated/ 38 | 39 | PoissonGMLE 40 | PoissonOffsetGMLE 41 | PoissonZiGMLE 42 | 43 | Linear Model with t-distributed errors 44 | -------------------------------------- 45 | 46 | This is a class that shows that a new model can be defined by only specifying the 47 | method for the loglikelihood. All result statistics are inherited from the generic 48 | likelihood model and result classes. The results have been checked against R for a 49 | simple case. 50 | 51 | .. module:: statsmodels.miscmodels.tmodel 52 | .. currentmodule:: statsmodels.miscmodels.tmodel 53 | 54 | .. autosummary:: 55 | :toctree: generated/ 56 | 57 | TLinearModel 58 | 59 | 60 | 61 | 62 | -------------------------------------------------------------------------------- /docs/en/source/missing.rst: -------------------------------------------------------------------------------- 1 | :orphan: 2 | 3 | .. _missing_data: 4 | 5 | Missing Data 6 | ------------ 7 | All of the models can handle missing data. For performance reasons, the default is not to do any checking for missing data. If, however, you would like for missing data to be handled internally, you can do so by using the missing keyword argument. The default is to do nothing 8 | 9 | .. ipython:: python 10 | 11 | import statsmodels.api as sm 12 | data = sm.datasets.longley.load(as_pandas=False) 13 | data.exog = sm.add_constant(data.exog) 14 | # add in some missing data 15 | missing_idx = np.array([False] * len(data.endog)) 16 | missing_idx[[4, 10, 15]] = True 17 | data.endog[missing_idx] = np.nan 18 | ols_model = sm.OLS(data.endog, data.exog) 19 | ols_fit = ols_model.fit() 20 | print(ols_fit.params) 21 | 22 | This silently fails and all of the model parameters are NaN, which is probably not what you expected. If you are not sure whether or not you have missing data you can use `missing = 'raise'`. This will raise a `MissingDataError` during model instantiation if missing data is present so that you know something was wrong in your input data. 23 | 24 | .. ipython:: python 25 | :okexcept: 26 | 27 | ols_model = sm.OLS(data.endog, data.exog, missing='raise') 28 | 29 | If you want statsmodels to handle the missing data by dropping the observations, use `missing = 'drop'`. 30 | 31 | .. ipython:: python 32 | 33 | ols_model = sm.OLS(data.endog, data.exog, missing='drop') 34 | 35 | We are considering adding a configuration framework so that you can set the option with a global setting. 36 | -------------------------------------------------------------------------------- /docs/en/source/mixed_glm.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.genmod.bayes_mixed_glm 2 | 3 | Generalized Linear Mixed Effects Models 4 | ======================================= 5 | 6 | Generalized Linear Mixed Effects (GLIMMIX) models are generalized 7 | linear models with random effects in the linear predictors. 8 | Statsmodels currently supports estimation of binomial and Poisson 9 | GLIMMIX models using two Bayesian methods: the Laplace approximation 10 | to the posterior, and a variational Bayes approximation to the 11 | posterior. Both methods provide point estimates (posterior means) and 12 | assessments of uncertainty (posterior standard deviation). 13 | 14 | The current implementation only supports independent random effects. 15 | 16 | Technical Documentation 17 | ----------------------- 18 | 19 | Unlike Statsmodels mixed linear models, the GLIMMIX implementation is 20 | not group-based. Groups are created by interacting all random effects 21 | with a categorical variable. Note that this creates large, sparse 22 | random effects design matrices `exog_vc`. Internally, `exog_vc` is 23 | converted to a scipy sparse matrix. When passing the arguments 24 | directly to the class initializer, a sparse matrix may be passed. 25 | When using formulas, a dense matrix is created then converted to 26 | sparse. For very large problems, it may not be feasible to use 27 | formulas due to the size of this dense intermediate matrix. 28 | 29 | References 30 | ^^^^^^^^^^ 31 | 32 | Blei, Kucukelbir, McAuliffe (2017). Variational Inference: A review 33 | for Statisticians https://arxiv.org/pdf/1601.00670.pdf 34 | 35 | Module Reference 36 | ---------------- 37 | 38 | .. module:: statsmodels.genmod.bayes_mixed_glm 39 | :synopsis: Bayes Mixed Generalized Linear Models 40 | 41 | 42 | The model classes are: 43 | 44 | .. autosummary:: 45 | :toctree: generated/ 46 | 47 | BinomialBayesMixedGLM 48 | PoissonBayesMixedGLM 49 | 50 | The result class is: 51 | 52 | .. autosummary:: 53 | :toctree: generated/ 54 | 55 | BayesMixedGLMResults 56 | -------------------------------------------------------------------------------- /docs/en/source/multivariate.rst: -------------------------------------------------------------------------------- 1 | .. module:: statsmodels.multivariate 2 | :synopsis: Models for multivariate data 3 | 4 | .. currentmodule:: statsmodels.multivariate 5 | 6 | .. _multivariate: 7 | 8 | 9 | Multivariate Statistics :mod:`multivariate` 10 | =========================================== 11 | 12 | This section includes methods and algorithms from multivariate statistics. 13 | 14 | 15 | Principal Component Analysis 16 | ---------------------------- 17 | 18 | .. module:: statsmodels.multivariate.pca 19 | :synopsis: Principal Component Analaysis 20 | 21 | .. currentmodule:: statsmodels.multivariate.pca 22 | 23 | .. autosummary:: 24 | :toctree: generated/ 25 | 26 | PCA 27 | pca 28 | 29 | 30 | Factor Analysis 31 | --------------- 32 | 33 | .. currentmodule:: statsmodels.multivariate.factor 34 | 35 | .. autosummary:: 36 | :toctree: generated/ 37 | 38 | Factor 39 | FactorResults 40 | 41 | 42 | Factor Rotation 43 | --------------- 44 | 45 | .. currentmodule:: statsmodels.multivariate.factor_rotation 46 | 47 | .. autosummary:: 48 | :toctree: generated/ 49 | 50 | rotate_factors 51 | target_rotation 52 | procrustes 53 | promax 54 | 55 | 56 | Canonical Correlation 57 | --------------------- 58 | 59 | .. currentmodule:: statsmodels.multivariate.cancorr 60 | 61 | .. autosummary:: 62 | :toctree: generated/ 63 | 64 | CanCorr 65 | 66 | 67 | MANOVA 68 | ------ 69 | 70 | .. currentmodule:: statsmodels.multivariate.manova 71 | 72 | .. autosummary:: 73 | :toctree: generated/ 74 | 75 | MANOVA 76 | 77 | 78 | MultivariateOLS 79 | --------------- 80 | 81 | `_MultivariateOLS` is a model class with limited features. Currently it 82 | supports multivariate hypothesis tests and is used as backend for MANOVA. 83 | 84 | .. currentmodule:: statsmodels.multivariate.multivariate_ols 85 | 86 | .. autosummary:: 87 | :toctree: generated/ 88 | 89 | _MultivariateOLS 90 | _MultivariateOLSResults 91 | MultivariateTestResults 92 | -------------------------------------------------------------------------------- /docs/en/source/plots/arma_predict_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | import matplotlib.pyplot as plt 3 | import pandas as pd 4 | 5 | dta = sm.datasets.sunspots.load_pandas().data[['SUNACTIVITY']] 6 | dta.index = pd.DatetimeIndex(start='1700', end='2009', freq='A') 7 | res = sm.tsa.ARMA(dta, (3, 0)).fit(disp=0) 8 | fig, ax = plt.subplots() 9 | ax = dta.loc['1950':].plot(ax=ax) 10 | res.plot_predict('1990', '2012', dynamic=True, ax=ax, 11 | plot_insample=False) 12 | -------------------------------------------------------------------------------- /docs/en/source/plots/bkf_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | 3 | from load_macrodata import dta 4 | 5 | cycles = sm.tsa.filters.bkfilter(dta[['realinv']], 6, 24, 12) 6 | 7 | import matplotlib.pyplot as plt 8 | fig, ax = plt.subplots() 9 | cycles.plot(ax=ax, style=['r--', 'b-']) 10 | -------------------------------------------------------------------------------- /docs/en/source/plots/cff_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | 3 | from load_macrodata import dta 4 | 5 | cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[["infl", "unemp"]]) 6 | 7 | import matplotlib.pyplot as plt 8 | fig, ax = plt.subplots() 9 | cf_cycles.plot(ax=ax, style=['r--', 'b-']) 10 | -------------------------------------------------------------------------------- /docs/en/source/plots/graphics-mean_diff_plot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Test Bland-Altman 3 | """ 4 | 5 | Author: Joses Ho 6 | 7 | """ 8 | 9 | import statsmodels.api as sm 10 | import numpy as np 11 | import matplotlib.pyplot as plt 12 | 13 | # Seed the random number generator. 14 | # This ensures that the results below are reproducible. 15 | np.random.seed(9999) 16 | m1 = np.random.random(20) 17 | m2 = np.random.random(20) 18 | 19 | f, ax = plt.subplots(1, figsize = (8,5)) 20 | sm.graphics.mean_diff_plot(m1, m2, ax = ax) 21 | 22 | plt.show() 23 | -------------------------------------------------------------------------------- /docs/en/source/plots/graphics_boxplot_beanplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Fri May 04 00:22:40 2012 5 | 6 | Author: Ralf Gommers 7 | 8 | """ 9 | 10 | import numpy as np 11 | import matplotlib.pyplot as plt 12 | import statsmodels.api as sm 13 | data = sm.datasets.anes96.load_pandas() 14 | party_ID = np.arange(7) 15 | labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", 16 | "Independent-Indpendent", "Independent-Republican", 17 | "Weak Republican", "Strong Republican"] 18 | 19 | plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible 20 | age = [data.exog['age'][data.endog == id] for id in party_ID] 21 | fig = plt.figure() 22 | ax = fig.add_subplot(111) 23 | sm.graphics.beanplot(age, ax=ax, labels=labels, 24 | plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 25 | 'label_fontsize':'small', 26 | 'label_rotation':30}) 27 | ax.set_xlabel("Party identification of respondent.") 28 | ax.set_ylabel("Age") 29 | 30 | #plt.show() 31 | -------------------------------------------------------------------------------- /docs/en/source/plots/graphics_boxplot_violinplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Fri May 04 00:11:32 2012 5 | 6 | Author: Ralf Gommers 7 | 8 | """ 9 | import numpy as np 10 | import matplotlib.pyplot as plt 11 | import statsmodels.api as sm 12 | 13 | data = sm.datasets.anes96.load_pandas() 14 | party_ID = np.arange(7) 15 | labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", 16 | "Independent-Indpendent", "Independent-Republican", 17 | "Weak Republican", "Strong Republican"] 18 | plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible 19 | age = [data.exog['age'][data.endog == id] for id in party_ID] 20 | fig = plt.figure() 21 | ax = fig.add_subplot(111) 22 | sm.graphics.violinplot(age, ax=ax, labels=labels, 23 | plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 24 | 'label_fontsize':'small', 25 | 'label_rotation':30}) 26 | ax.set_xlabel("Party identification of respondent.") 27 | ax.set_ylabel("Age") 28 | 29 | #plt.show() 30 | -------------------------------------------------------------------------------- /docs/en/source/plots/graphics_functional_fboxplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Fri May 04 11:10:51 2012 5 | 6 | Author: Ralf Gommers 7 | 8 | """ 9 | 10 | #Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea 11 | #surface temperature data. 12 | 13 | import numpy as np 14 | import matplotlib.pyplot as plt 15 | import statsmodels.api as sm 16 | data = sm.datasets.elnino.load(as_pandas=False) 17 | 18 | #Create a functional boxplot. We see that the years 1982-83 and 1997-98 are 19 | #outliers; these are the years where El Nino (a climate pattern 20 | #characterized by warming up of the sea surface and higher air pressures) 21 | #occurred with unusual intensity. 22 | 23 | fig = plt.figure() 24 | ax = fig.add_subplot(111) 25 | res = sm.graphics.fboxplot(data.raw_data[:, 1:], wfactor=2.58, 26 | labels=data.raw_data[:, 0].astype(int), 27 | ax=ax) 28 | 29 | ax.set_xlabel("Month of the year") 30 | ax.set_ylabel("Sea surface temperature (C)") 31 | ax.set_xticks(np.arange(13, step=3) - 1) 32 | ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) 33 | ax.set_xlim([-0.2, 11.2]) 34 | 35 | #plt.show() 36 | -------------------------------------------------------------------------------- /docs/en/source/plots/graphics_functional_hdrboxplot.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | 3 | #Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea 4 | #surface temperature data. 5 | 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | import statsmodels.api as sm 9 | data = sm.datasets.elnino.load(as_pandas=False) 10 | 11 | #Create a HDR functional boxplot. We see that the years 1982-83 and 1997-98 are 12 | #outliers; these are the years where El Nino (a climate pattern 13 | #characterized by warming up of the sea surface and higher air pressures) 14 | #occurred with unusual intensity. 15 | 16 | fig = plt.figure() 17 | ax = fig.add_subplot(111) 18 | fig, res = sm.graphics.hdrboxplot(data.raw_data[:, 1:], 19 | labels=data.raw_data[:, 0].astype(int), 20 | ax=ax) 21 | 22 | ax.plot([0, 10], [25, 25]) 23 | ax.set_xlabel("Month of the year") 24 | ax.set_ylabel("Sea surface temperature (C)") 25 | ax.set_xticks(np.arange(13, step=3) - 1) 26 | ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) 27 | ax.set_xlim([-0.2, 11.2]) 28 | 29 | plt.show() 30 | 31 | print(res) 32 | -------------------------------------------------------------------------------- /docs/en/source/plots/graphics_functional_rainbowplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Fri May 04 11:08:56 2012 5 | 6 | Author: Ralf Gommers 7 | 8 | """ 9 | 10 | #Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea 11 | #surface temperature data. 12 | 13 | import numpy as np 14 | import matplotlib.pyplot as plt 15 | import statsmodels.api as sm 16 | data = sm.datasets.elnino.load(as_pandas=False) 17 | 18 | #Create a rainbow plot: 19 | 20 | fig = plt.figure() 21 | ax = fig.add_subplot(111) 22 | res = sm.graphics.rainbowplot(data.raw_data[:, 1:], ax=ax) 23 | 24 | ax.set_xlabel("Month of the year") 25 | ax.set_ylabel("Sea surface temperature (C)") 26 | ax.set_xticks(np.arange(13, step=3) - 1) 27 | ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) 28 | ax.set_xlim([-0.2, 11.2]) 29 | 30 | #plt.show() 31 | -------------------------------------------------------------------------------- /docs/en/source/plots/graphics_gofplots_qqplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Sun May 06 05:32:15 2012 4 | 5 | Author: Josef Perktold 6 | editted by: Paul Hobson (2012-08-19) 7 | """ 8 | from scipy import stats 9 | from matplotlib import pyplot as plt 10 | import statsmodels.api as sm 11 | 12 | #example from docstring 13 | data = sm.datasets.longley.load(as_pandas=False) 14 | data.exog = sm.add_constant(data.exog, prepend=True) 15 | mod_fit = sm.OLS(data.endog, data.exog).fit() 16 | res = mod_fit.resid 17 | 18 | left = -1.8 #x coordinate for text insert 19 | 20 | fig = plt.figure() 21 | 22 | ax = fig.add_subplot(2, 2, 1) 23 | sm.graphics.qqplot(res, ax=ax) 24 | top = ax.get_ylim()[1] * 0.75 25 | txt = ax.text(left, top, 'no keywords', verticalalignment='top') 26 | txt.set_bbox(dict(facecolor='k', alpha=0.1)) 27 | 28 | ax = fig.add_subplot(2, 2, 2) 29 | sm.graphics.qqplot(res, line='s', ax=ax) 30 | top = ax.get_ylim()[1] * 0.75 31 | txt = ax.text(left, top, "line='s'", verticalalignment='top') 32 | txt.set_bbox(dict(facecolor='k', alpha=0.1)) 33 | 34 | ax = fig.add_subplot(2, 2, 3) 35 | sm.graphics.qqplot(res, line='45', fit=True, ax=ax) 36 | ax.set_xlim(-2, 2) 37 | top = ax.get_ylim()[1] * 0.75 38 | txt = ax.text(left, top, "line='45', \nfit=True", verticalalignment='top') 39 | txt.set_bbox(dict(facecolor='k', alpha=0.1)) 40 | 41 | ax = fig.add_subplot(2, 2, 4) 42 | sm.graphics.qqplot(res, dist=stats.t, line='45', fit=True, ax=ax) 43 | ax.set_xlim(-2, 2) 44 | top = ax.get_ylim()[1] * 0.75 45 | txt = ax.text(left, top, "dist=stats.t, \nline='45', \nfit=True", 46 | verticalalignment='top') 47 | txt.set_bbox(dict(facecolor='k', alpha=0.1)) 48 | 49 | fig.tight_layout() 50 | 51 | plt.gcf() 52 | 53 | 54 | # example with the new ProbPlot class 55 | import numpy as np 56 | x = np.random.normal(loc=8.25, scale=3.5, size=37) 57 | y = np.random.normal(loc=8.00, scale=3.25, size=37) 58 | pp_x = sm.ProbPlot(x, fit=True) 59 | pp_y = sm.ProbPlot(y, fit=True) 60 | 61 | # probability of exceedance 62 | fig2 = pp_x.probplot(exceed=True) 63 | 64 | # compare x quantiles to y quantiles 65 | fig3 = pp_x.qqplot(other=pp_y, line='45') 66 | 67 | # same as above with probabilities/percentiles 68 | fig4 = pp_x.ppplot(other=pp_y, line='45') 69 | -------------------------------------------------------------------------------- /docs/en/source/plots/graphics_month_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | import pandas as pd 3 | 4 | dta = sm.datasets.elnino.load_pandas().data 5 | dta['YEAR'] = dta.YEAR.astype(int).astype(str) 6 | dta = dta.set_index('YEAR').T.unstack() 7 | dates = pd.to_datetime(list(map(lambda x : '-'.join(x) + '-1', dta.index.values))) 8 | 9 | dta.index = pd.DatetimeIndex(list(dates), freq='MS') 10 | dta.name = 'temp' 11 | fig = sm.graphics.tsa.month_plot(dta) 12 | -------------------------------------------------------------------------------- /docs/en/source/plots/graphics_plot_fit_ex.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Monday April 1st 2013 5 | 6 | Author: Padarn Wilson 7 | 8 | """ 9 | 10 | # Load the Statewide Crime data set and perform linear regression with 11 | # 'poverty' and 'hs_grad' as variables and 'muder' as the response 12 | 13 | 14 | import statsmodels.api as sm 15 | import matplotlib.pyplot as plt 16 | import numpy as np 17 | 18 | data = sm.datasets.statecrime.load_pandas().data 19 | murder = data['murder'] 20 | X = data[['poverty', 'hs_grad']].copy() 21 | X['constant'] = 1 22 | 23 | y = murder 24 | model = sm.OLS(y, X) 25 | results = model.fit() 26 | 27 | # Create a plot just for the variable 'Poverty': 28 | 29 | fig, ax = plt.subplots() 30 | fig = sm.graphics.plot_fit(results, 0, ax=ax) 31 | ax.set_ylabel("Murder Rate") 32 | ax.set_xlabel("Poverty Level") 33 | ax.set_title("Linear Regression") 34 | 35 | plt.show() 36 | -------------------------------------------------------------------------------- /docs/en/source/plots/hpf_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | import pandas as pd 3 | from load_macrodata import dta 4 | 5 | cycle, trend = sm.tsa.filters.hpfilter(dta.realgdp, 1600) 6 | gdp_decomp = dta[['realgdp']].copy() 7 | gdp_decomp["cycle"] = cycle 8 | gdp_decomp["trend"] = trend 9 | 10 | import matplotlib.pyplot as plt 11 | fig, ax = plt.subplots() 12 | gdp_decomp[["realgdp", "trend"]]["2000-03-31":].plot(ax=ax, 13 | fontsize=16) 14 | -------------------------------------------------------------------------------- /docs/en/source/plots/load_macrodata.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | import pandas as pd 3 | dta = sm.datasets.macrodata.load_pandas().data 4 | dates = sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3') 5 | index = pd.DatetimeIndex(dates) 6 | dta.set_index(index, inplace=True) 7 | -------------------------------------------------------------------------------- /docs/en/source/plots/var_plot_acorr.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_acorr 2 | plot_acorr() 3 | -------------------------------------------------------------------------------- /docs/en/source/plots/var_plot_fevd.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_fevd 2 | plot_fevd() 3 | -------------------------------------------------------------------------------- /docs/en/source/plots/var_plot_forecast.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_forecast 2 | plot_forecast() 3 | -------------------------------------------------------------------------------- /docs/en/source/plots/var_plot_input.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_input 2 | plot_input() 3 | -------------------------------------------------------------------------------- /docs/en/source/plots/var_plot_irf.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_irf 2 | plot_irf() 3 | -------------------------------------------------------------------------------- /docs/en/source/plots/var_plot_irf_cum.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_irf_cum 2 | plot_irf_cum() 3 | -------------------------------------------------------------------------------- /docs/en/source/plots/var_plots.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from statsmodels.tsa.api import VAR 4 | from statsmodels.api import datasets as ds 5 | from statsmodels.tsa.base.datetools import dates_from_str 6 | 7 | 8 | import pandas 9 | mdata = ds.macrodata.load_pandas().data 10 | 11 | # prepare the dates index 12 | dates = mdata[['year', 'quarter']].astype(int) 13 | quarterly = [str(yr) + 'Q' + str(mo) 14 | for yr, mo in zip(dates["year"], dates["quarter"])] 15 | quarterly = dates_from_str(quarterly) 16 | 17 | mdata = mdata[['realgdp','realcons','realinv']] 18 | mdata.index = pandas.DatetimeIndex(quarterly) 19 | data = np.log(mdata).diff().dropna() 20 | 21 | model = VAR(data) 22 | est = model.fit(maxlags=2) 23 | 24 | def plot_input(): 25 | est.plot() 26 | 27 | def plot_acorr(): 28 | est.plot_acorr() 29 | 30 | def plot_irf(): 31 | est.irf().plot() 32 | 33 | def plot_irf_cum(): 34 | irf = est.irf() 35 | irf.plot_cum_effects() 36 | 37 | def plot_forecast(): 38 | est.plot_forecast(10) 39 | 40 | def plot_fevd(): 41 | est.fevd(20).plot() 42 | -------------------------------------------------------------------------------- /docs/en/source/regression_techn1.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.regression 2 | 3 | 4 | .. _regression-techn1: 5 | 6 | Technical Documentation 7 | ======================= 8 | 9 | Introduction 10 | ------------ 11 | 12 | Just a placeholder 13 | -------------------------------------------------------------------------------- /docs/en/source/release/index.rst: -------------------------------------------------------------------------------- 1 | .. During each release add in this folder information about important changes. 2 | .. Each versionx.x.rst file should have four main sections. 3 | .. (1) Major features (2) Important bug fixes (3) API breakage (4) Credits 4 | 5 | .. The github-stats-x.x.rst files are generated by tools/github_stats.py with 6 | .. some cleanup afterwards. I do python github_stats.py > github-stats-x.x.rst. 7 | .. As of the 0.5 release, this script asks for your github name and password 8 | .. to download the statistics. 9 | 10 | .. _whatsnew_index: 11 | 12 | ========================= 13 | What's new in Statsmodels 14 | ========================= 15 | 16 | .. toctree:: 17 | :maxdepth: 1 18 | 19 | version0.9 20 | version0.8 21 | version0.7 22 | version0.6 23 | github-stats-0.6 24 | version0.5 25 | github-stats-0.5 26 | 27 | For an overview of changes that occured previous to the 0.5.0 release see :ref:`old_changes`. 28 | -------------------------------------------------------------------------------- /docs/en/source/rlm.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.robust 2 | 3 | 4 | .. _rlm: 5 | 6 | Robust Linear Models 7 | ==================== 8 | 9 | Robust linear models with support for the M-estimators listed under `Norms`_. 10 | 11 | See `Module Reference`_ for commands and arguments. 12 | 13 | Examples 14 | -------- 15 | 16 | .. ipython:: python 17 | 18 | # Load modules and data 19 | import statsmodels.api as sm 20 | data = sm.datasets.stackloss.load(as_pandas=False) 21 | data.exog = sm.add_constant(data.exog) 22 | 23 | # Fit model and print summary 24 | rlm_model = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()) 25 | rlm_results = rlm_model.fit() 26 | print(rlm_results.params) 27 | 28 | Detailed examples can be found here: 29 | 30 | * `Robust Models 1 `__ 31 | * `Robust Models 2 `__ 32 | 33 | Technical Documentation 34 | ----------------------- 35 | 36 | .. toctree:: 37 | :maxdepth: 1 38 | 39 | rlm_techn1 40 | 41 | References 42 | ^^^^^^^^^^ 43 | 44 | * PJ Huber. ‘Robust Statistics’ John Wiley and Sons, Inc., New York. 1981. 45 | * PJ Huber. 1973, ‘The 1972 Wald Memorial Lectures: Robust Regression: Asymptotics, Conjectures, and Monte Carlo.’ The Annals of Statistics, 1.5, 799-821. 46 | * R Venables, B Ripley. ‘Modern Applied Statistics in S’ Springer, New York, 47 | 48 | Module Reference 49 | ---------------- 50 | 51 | .. module:: statsmodels.robust 52 | 53 | Model Classes 54 | ^^^^^^^^^^^^^ 55 | 56 | .. module:: statsmodels.robust.robust_linear_model 57 | .. currentmodule:: statsmodels.robust.robust_linear_model 58 | 59 | .. autosummary:: 60 | :toctree: generated/ 61 | 62 | RLM 63 | 64 | Model Results 65 | ^^^^^^^^^^^^^ 66 | 67 | .. autosummary:: 68 | :toctree: generated/ 69 | 70 | RLMResults 71 | 72 | .. _norms: 73 | 74 | Norms 75 | ^^^^^ 76 | 77 | .. module:: statsmodels.robust.norms 78 | .. currentmodule:: statsmodels.robust.norms 79 | 80 | .. autosummary:: 81 | :toctree: generated/ 82 | 83 | AndrewWave 84 | Hampel 85 | HuberT 86 | LeastSquares 87 | RamsayE 88 | RobustNorm 89 | TrimmedMean 90 | TukeyBiweight 91 | estimate_location 92 | 93 | 94 | Scale 95 | ^^^^^ 96 | 97 | .. module:: statsmodels.robust.scale 98 | .. currentmodule:: statsmodels.robust.scale 99 | 100 | .. autosummary:: 101 | :toctree: generated/ 102 | 103 | Huber 104 | HuberScale 105 | mad 106 | hubers_scale 107 | -------------------------------------------------------------------------------- /docs/en/source/rlm_techn1.rst: -------------------------------------------------------------------------------- 1 | .. module:: statsmodels.rlm 2 | :synopsis: Outlier robust linear models 3 | 4 | .. currentmodule:: statsmodels.rlm 5 | 6 | 7 | .. _rlm_techn1: 8 | 9 | Weight Functions 10 | ---------------- 11 | 12 | Andrew's Wave 13 | 14 | .. image:: images/aw.png 15 | 16 | Hampel 17A 17 | 18 | .. image:: images/hl.png 19 | 20 | Huber's t 21 | 22 | .. image:: images/ht.png 23 | 24 | Least Squares 25 | 26 | .. image:: images/ls.png 27 | 28 | Ramsay's Ea 29 | 30 | .. image:: images/re.png 31 | 32 | Trimmed Mean 33 | 34 | .. image:: images/tm.png 35 | 36 | Tukey's Biweight 37 | 38 | .. image:: images/tk.png 39 | 40 | 41 | -------------------------------------------------------------------------------- /docs/en/source/tsastats.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.tsa.tsatools 2 | 3 | Time Series Analysis 4 | ==================== 5 | 6 | These are some of the helper functions for doing time series analysis. First 7 | we can load some a some data from the US Macro Economy 1959:Q1 - 2009:Q3. :: 8 | 9 | >>> data = sm.datasets.macrodata.load(as_pandas=False) 10 | 11 | The macro dataset is a structured array. :: 12 | 13 | >>> data = data.data[['year','quarter','realgdp','tbilrate','cpi','unemp']] 14 | 15 | We can add a lag like so :: 16 | 17 | >>> data = sm.tsa.add_lag(data, 'realgdp', lags=2) 18 | 19 | TODO: 20 | -scikits.timeseries 21 | -link in to var docs 22 | -------------------------------------------------------------------------------- /docs/en/sphinxext/MANIFEST.in: -------------------------------------------------------------------------------- 1 | recursive-include tests *.py 2 | include *.txt 3 | -------------------------------------------------------------------------------- /docs/en/sphinxext/README.txt: -------------------------------------------------------------------------------- 1 | ===================================== 2 | numpydoc -- Numpy's Sphinx extensions 3 | ===================================== 4 | 5 | Numpy's documentation uses several custom extensions to Sphinx. These 6 | are shipped in this ``numpydoc`` package, in case you want to make use 7 | of them in third-party projects. 8 | 9 | The following extensions are available: 10 | 11 | - ``numpydoc``: support for the Numpy docstring format in Sphinx, and add 12 | the code description directives ``np-function``, ``np-cfunction``, etc. 13 | that support the Numpy docstring syntax. 14 | 15 | - ``numpydoc.traitsdoc``: For gathering documentation about Traits attributes. 16 | 17 | - ``numpydoc.plot_directives``: Adaptation of Matplotlib's ``plot::`` 18 | directive. Note that this implementation may still undergo severe 19 | changes or eventually be deprecated. 20 | 21 | - ``numpydoc.only_directives``: (DEPRECATED) 22 | 23 | - ``numpydoc.autosummary``: (DEPRECATED) An ``autosummary::`` directive. 24 | Available in Sphinx 0.6.2 and (to-be) 1.0 as ``sphinx.ext.autosummary``, 25 | and it the Sphinx 1.0 version is recommended over that included in 26 | Numpydoc. 27 | -------------------------------------------------------------------------------- /docs/en/sphinxext/numpy_ext/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/en/sphinxext/numpy_ext/__init__.py -------------------------------------------------------------------------------- /docs/en/themes/statsmodels/indexsidebar.html: -------------------------------------------------------------------------------- 1 |

Download

2 | 3 | {% if 'dev' in version %} 4 | 5 |

This documentation is for version {{ version }}, which is not 6 | released yet. Grab the source code from Github to install this version. You can go to the documentation for the last release here.

7 | 8 | {% else %} 9 | 10 |

This documentation is for the {{ release }} release. You can install it with pip: 11 | 12 | 

pip install --upgrade --no-deps statsmodels
13 | 14 | or conda: 15 | 16 | 
conda install statsmodels
17 | Documentation for the current development version is here.

18 | 19 | {% endif %} 20 | 21 |

Participate

22 |
Join the Google Group:
23 |
24 |
25 | 26 |
27 |

28 | Grab the source from Github. 29 | Report bugs to the Issue Tracker. 30 | Have a look at our Developer Pages. 31 |

32 | -------------------------------------------------------------------------------- /docs/en/themes/statsmodels/page.html: -------------------------------------------------------------------------------- 1 | {# 2 | Overwrites what is displayed on the examples landing page. 3 | #} 4 | {%- extends "layout.html" %} 5 | {% block body %} 6 | 7 | 8 | {% if pagename == 'examples/index' %} 9 |
10 |

Statsmodels Examples

11 |
12 | This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository.

13 | 14 | If you are not comfortable with git, we also encourage users to submit their own examples, tutorials or cool statsmodels tricks to the Examples wiki page. 15 |
16 | 17 | 18 |

Topics

19 |
20 | {% for section in examples %} 21 | 24 | {% endfor %} 25 | 26 |
27 | 28 | {% for section in examples %} 29 |

{{ section.header }}

30 | 31 |
32 |
    33 | {% for link in section.links %} 34 |
  • 35 |

    {{link.text}}

    36 | 37 | 38 | 39 |
    • 40 | {% endfor %} 41 |
42 |
43 | {% endfor %} 44 |
45 | {% else %} 46 | 47 | {{ body }} 48 | 49 | {% endif %} 50 | 51 | {% endblock %} 52 | -------------------------------------------------------------------------------- /docs/en/themes/statsmodels/relations.html: -------------------------------------------------------------------------------- 1 | {# 2 | basic/relations.html 3 | ~~~~~~~~~~~~~~~~~~~~ 4 | 5 | Sphinx sidebar template: relation links. 6 | 7 | :copyright: Copyright 2007-2010 by the Sphinx team, see AUTHORS. 8 | :license: BSD, see LICENSE for details. 9 | #} 10 | {%- if prev %} 11 |

{{ _('Previous topic') }}

12 | {%- if prev.title[:19] == "statsmodels" %} 13 |

{{ "sm." ~ prev.title[20:] }}

15 | {%- else %} 16 |

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18 | 19 | {%- endif %} 20 | {%- endif %} 21 | 22 | {%- if next %} 23 |

{{ _('Next topic') }}

24 | {%- if next.title[:19] == "statsmodels" %} 25 |

{{ "sm." ~ next.title[20:] }}

27 | {%- else %} 28 |

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30 | {%- endif %} 31 | 32 | {%- endif %} 33 | -------------------------------------------------------------------------------- /docs/en/themes/statsmodels/sidelinks.html: -------------------------------------------------------------------------------- 1 |

Follow statsmodels on Twitter 2 | Blog

3 | -------------------------------------------------------------------------------- /docs/en/themes/statsmodels/static/statsmodels_hybi_banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/en/themes/statsmodels/static/statsmodels_hybi_banner.png -------------------------------------------------------------------------------- /docs/en/themes/statsmodels/theme.conf: -------------------------------------------------------------------------------- 1 | [theme] 2 | inherit = basic 3 | stylesheet = nature.css 4 | pygments_style = tango 5 | -------------------------------------------------------------------------------- /docs/zh/GLMNotes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/zh/GLMNotes.pdf -------------------------------------------------------------------------------- /docs/zh/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = 6 | SPHINXBUILD = sphinx-build 7 | SPHINXPROJ = statsmodels 8 | SOURCEDIR = source 9 | BUILDDIR = build 10 | 11 | PAPER = 12 | TOOLSPATH = ../tools/ 13 | DATASETBUILD = dataset_rst.py 14 | EXAMPLEBUILD = examples_rst.py 15 | NOTEBOOKBUILD = nbgenerate.py 16 | FOLDTOC = fold_toc.py 17 | 18 | # Internal variables. 19 | PAPEROPT_a4 = -D latex_paper_size=a4 20 | PAPEROPT_letter = -D latex_paper_size=letter 21 | ALLSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) 22 | 23 | 24 | # Put it first so that "make" without argument is like "make help". 25 | help: 26 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 27 | 28 | .PHONY: help Makefile 29 | 30 | cleanall: 31 | @$(SPHINXBUILD) -M clean "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 32 | -rm source/examples/generated/* 33 | -rm -rf source/examples/notebooks/generated/* 34 | -rm -rf ../tools/hash_dict.pickle 35 | -rm -rf source/datasets/generated/* 36 | 37 | notebooks: 38 | @echo "Generating notebooks from examples/notebooks folder" 39 | $(TOOLSPATH)$(NOTEBOOKBUILD) --execute=True --allow_errors=True 40 | 41 | html: 42 | # make directories for images 43 | @echo "Make static directory for images" 44 | mkdir -p $(BUILDDIR)/html/_static 45 | # generate the examples rst files 46 | @echo "Generating reST from examples folder" 47 | #$(TOOLSPATH)$(EXAMPLEBUILD) 48 | @echo "Generating datasets from installed statsmodels.datasets" 49 | $(TOOLSPATH)$(DATASETBUILD) 50 | @echo "Generating notebooks from examples/notebooks folder" 51 | $(TOOLSPATH)$(NOTEBOOKBUILD) --parallel --report-errors --skip-existing 52 | @echo "Running sphinx-build" 53 | @echo @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 54 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 55 | @echo "Copying rendered example notebooks" 56 | mkdir -p $(BUILDDIR)/html/examples/notebooks/generated 57 | cp source/examples/notebooks/generated/*html $(BUILDDIR)/html/examples/notebooks/generated 58 | @echo $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/index.html 59 | $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/index.html 60 | @echo $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/dev/index.html ../_static 61 | $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/dev/index.html ../_static 62 | 63 | # Catch-all target: route all unknown targets to Sphinx using the new 64 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 65 | %: Makefile 66 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) 67 | -------------------------------------------------------------------------------- /docs/zh/README.md: -------------------------------------------------------------------------------- 1 | # Documentation Documentation 2 | 3 | We use a combination of sphinx and Jupyter notebooks for the documentation. 4 | Jupyter notebooks should be used for longer, self-contained examples demonstrating 5 | a topic. 6 | Sphinx is nice because we get the tables of contents and API documentation. 7 | 8 | ## Build Process 9 | 10 | Building the docs requires a few additional dependencies. You can get most 11 | of these with 12 | 13 | ```bash 14 | 15 | pip install -e .[docs] 16 | 17 | ``` 18 | 19 | From the root of the project. 20 | Some of the examples rely on `rpy2` to execute R code from the notebooks. 21 | It's not included in the setup requires since it's known to be difficult to 22 | install. 23 | 24 | To generate the HTML docs, run ``make html`` from the ``docs`` directory. 25 | This executes a few distinct builds 26 | 27 | 1. datasets 28 | 2. notebooks 29 | 3. sphinx 30 | 31 | # Notebook Builds 32 | 33 | We're using `nbconvert` to execute the notebooks, and then convert them 34 | to HTML. The conversion is handled by `statsmodels/tools/nbgenerate.py`. 35 | The default python kernel (embedded in the notebook) is `python3`. 36 | You need at least `nbconvert==4.2.0` to specify a non-default kernel, 37 | which can be passed in the Makefile. 38 | -------------------------------------------------------------------------------- /docs/zh/fix_longtable.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import sys 3 | import os 4 | 5 | 6 | BUILDDIR = sys.argv[-1] 7 | read_file_path = os.path.join(BUILDDIR,'latex','statsmodels.tex') 8 | write_file_path = os.path.join(BUILDDIR, 'latex','statsmodels_tmp.tex') 9 | 10 | read_file = open(read_file_path,'r') 11 | write_file = open(write_file_path, 'w') 12 | 13 | for line in read_file: 14 | if 'longtable}{LL' in line: 15 | line = line.replace('longtable}{LL', 'longtable}{|l|l|') 16 | write_file.write(line) 17 | 18 | read_file.close() 19 | write_file.close() 20 | 21 | os.remove(read_file_path) 22 | os.rename(write_file_path, read_file_path) 23 | -------------------------------------------------------------------------------- /docs/zh/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | 11 | set SOURCEDIR=source 12 | set BUILDDIR=build 13 | set SPHINXPROJ=statsmodels 14 | set SPHINXOPTS=-j auto 15 | 16 | set TOOLSPATH=../tools 17 | set DATASETBUILD=dataset_rst.py 18 | set NOTEBOOKBUILD=nbgenerate.py 19 | set FOLDTOC=fold_toc.py 20 | 21 | if "%1" == "" goto help 22 | 23 | %SPHINXBUILD% >NUL 2>NUL 24 | if errorlevel 9009 ( 25 | echo. 26 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 27 | echo.installed, then set the SPHINXBUILD environment variable to point 28 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 29 | echo.may add the Sphinx directory to PATH. 30 | echo. 31 | echo.If you don't have Sphinx installed, grab it from 32 | echo.http://sphinx-doc.org/ 33 | exit /b 1 34 | ) 35 | 36 | if "%1" == "html" ( 37 | echo mkdir %BUILDDIR%\html\_static 38 | mkdir %BUILDDIR%\html\_static 39 | echo python %TOOLSPATH%/%NOTEBOOKBUILD% --parallel --report-errors --skip-existing 40 | python %TOOLSPATH%/%NOTEBOOKBUILD% --parallel --report-errors --skip-existing 41 | echo python %TOOLSPATH%/%DATASETBUILD% 42 | python %TOOLSPATH%/%DATASETBUILD% 43 | ) 44 | 45 | echo %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 46 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 47 | if errorlevel 1 exit /b 1 48 | 49 | if "%1" == "html" ( 50 | echo xcopy /s source/examples/notebooks/generated/*.html %BUILDDIR%/html/examples/notebooks/generated 51 | xcopy /s source/examples/notebooks/generated/*.html %BUILDDIR%/html/examples/notebooks/generated 52 | echo python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/index.html 53 | python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/index.html 54 | echo python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/examples/index.html ../_static 55 | python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/examples/index.html ../_static 56 | echo python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/dev/index.html ../_static 57 | python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/dev/index.html ../_static 58 | if NOT EXIST %BUILDDIR%/html/examples/notebooks/generated mkdir %BUILDDIR%\html\examples\notebooks\generated 59 | ) 60 | 61 | goto end 62 | 63 | :help 64 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 65 | 66 | :end 67 | popd 68 | -------------------------------------------------------------------------------- /docs/zh/source/_static/blogger.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/zh/source/_static/blogger.png 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/docs/zh/source/_static/scripts.js: -------------------------------------------------------------------------------- 1 | function cleanUpText(codebox){ 2 | /// Not currently used 3 | /// Strips a whole IPython session of input and output prompts 4 | //escape quotation marks 5 | codebox = codebox.replace(/"/g, "\'"); 6 | 7 | // newlines 8 | codebox = codebox.replace(/[\r\n|\r|\n]$/g, ""); // remove at end 9 | codebox = codebox.replace(/[\r\n|\r|\n]+/g, "\\n"); 10 | // prompts 11 | codebox = codebox.replace(/In \[\d+\]: /g, ""); 12 | codebox = codebox.replace(/Out \[\d+\]: /g, ""); 13 | 14 | return codebox; 15 | } 16 | 17 | function htmlescape(text){ 18 | return (text.replace(/&/g, "&") 19 | .replace(//g, ">") 21 | .replace(/"/g, """) 22 | .replace(/'/g, "'")) 23 | } 24 | 25 | function scrapeText(codebox){ 26 | /// Returns input lines cleaned of prompt1 and prompt2 27 | var lines = codebox.split('\n'); 28 | var newlines = new Array(); 29 | $.each(lines, function() { 30 | if (this.match(/^In \[\d+]: /)){ 31 | newlines.push(this.replace(/^(\s)*In \[\d+]: /,"")); 32 | } 33 | else if (this.match(/^(\s)*\.+:/)){ 34 | newlines.push(this.replace(/^(\s)*\.+: /,"")); 35 | } 36 | 37 | } 38 | ); 39 | return newlines.join('\\n'); 40 | } 41 | 42 | $(document).ready( 43 | function() { 44 | // grab all code boxes 45 | var ipythoncode = $(".highlight-ipython"); 46 | $.each(ipythoncode, function() { 47 | var codebox = scrapeText($(this).text()); 48 | // give them a facebox pop-up with plain text code 49 | $(this).append('View Code'); 50 | $(this,"textarea").select(); 51 | }); 52 | }); 53 | -------------------------------------------------------------------------------- /docs/zh/source/_templates/autosummary/class.rst: -------------------------------------------------------------------------------- 1 | {{ fullname }} 2 | {{ underline }} 3 | 4 | .. currentmodule:: {{ module }} 5 | 6 | .. autoclass:: {{ objname }} 7 | 8 | {% block methods %} 9 | 10 | {% if methods %} 11 | .. rubric:: Methods 12 | 13 | .. autosummary:: 14 | :toctree: 15 | {% for item in methods %} 16 | {% if item != '__init__' %} 17 | ~{{ name }}.{{ item }} 18 | {% endif %} 19 | {%- endfor %} 20 | {% endif %} 21 | {% endblock %} 22 | 23 | {% block attributes %} 24 | {% if attributes %} 25 | .. rubric:: Attributes 26 | 27 | .. autosummary:: 28 | {% for item in attributes %} 29 | ~{{ name }}.{{ item }} 30 | {%- endfor %} 31 | {% endif %} 32 | {% endblock %} 33 | -------------------------------------------------------------------------------- /docs/zh/source/_templates/autosummary/glmfamilies.rst: -------------------------------------------------------------------------------- 1 | {{ fullname }} 2 | {{ underline }} 3 | 4 | .. currentmodule:: {{ module }} 5 | 6 | .. autoclass:: {{ objname }} 7 | 8 | {% block methods %} 9 | 10 | {% if methods %} 11 | .. rubric:: Methods 12 | 13 | .. autosummary:: 14 | :toctree: 15 | {% for item in methods %} 16 | {% if item != '__init__' %} 17 | ~{{ name }}.{{ item }} 18 | {% endif %} 19 | {%- endfor %} 20 | {% endif %} 21 | {% endblock %} 22 | -------------------------------------------------------------------------------- /docs/zh/source/anova.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.stats.anova 2 | 3 | .. _anova: 4 | 5 | ANOVA 6 | ===== 7 | 8 | Analysis of Variance models containing anova_lm for ANOVA analysis with a 9 | linear OLSModel, and AnovaRM for repeated measures ANOVA, within ANOVA for 10 | balanced data. 11 | 12 | Examples 13 | -------- 14 | 15 | .. ipython:: python 16 | 17 | import statsmodels.api as sm 18 | from statsmodels.formula.api import ols 19 | 20 | moore = sm.datasets.get_rdataset("Moore", "carData", 21 | cache=True) # load data 22 | data = moore.data 23 | data = data.rename(columns={"partner.status": 24 | "partner_status"}) # make name pythonic 25 | moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)', 26 | data=data).fit() 27 | 28 | table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 ANOVA DataFrame 29 | print(table) 30 | 31 | A more detailed example for `anova_lm` can be found here: 32 | 33 | * `ANOVA `__ 34 | 35 | Module Reference 36 | ---------------- 37 | 38 | .. module:: statsmodels.stats.anova 39 | :synopsis: Analysis of Variance 40 | 41 | .. autosummary:: 42 | :toctree: generated/ 43 | 44 | anova_lm 45 | AnovaRM 46 | -------------------------------------------------------------------------------- /docs/zh/source/dev/examples.rst: -------------------------------------------------------------------------------- 1 | .. _examples: 2 | 3 | Examples 4 | ======== 5 | 6 | Examples are invaluable for new users who hope to get up and running quickly 7 | with `statsmodels`, and they are extremely useful to those who wish to explore 8 | new features of `statsmodels`. We hope to provide documentation and tutorials 9 | for as many models and use-cases as possible! Please consider submitting an example with any PR that introduces new functionality. 10 | 11 | User-contributed examples/tutorials/recipes can be placed on the 12 | `statsmodels examples wiki page `_ 13 | That wiki page is freely editable. Please post your cool tricks, 14 | examples, and recipes on there! 15 | 16 | If you would rather have your example file officially accepted to the 17 | `statsmodels` distribution and posted on this website, you will need to go 18 | through the normal `patch submission process `_ and follow the instructions that follow. 19 | 20 | File Format 21 | ~~~~~~~~~~~ 22 | 23 | Examples are best contributed as IPython notebooks. Save your notebook with all output cells cleared in ``examples/notebooks``. From the notebook save the pure Python output to ``examples/python``. The first line of the Notebook *must* be a header cell that contains a title for the notebook, if you want the notebook to be included in the documentation. 24 | 25 | 26 | The Example Gallery 27 | ~~~~~~~~~~~~~~~~~~~ 28 | 29 | We have a gallery of example notebooks available `here `_. If you would like your example to show up in this gallery, add a link to the notebook in ``docs/source/examples/landing.json``. For the thumbnail, take a screenshot of what you think is the best "hook" for the notebook. The image will be displayed at 360 x 225 (W x H). It's best to save the image as a PNG with a resolution that is some multiple of 360 x 225 (720 x 450 is preferred). 30 | 31 | 32 | Before submitting a PR 33 | ~~~~~~~~~~~~~~~~~~~~~~ 34 | 35 | To save you some time and to make the new examples nicely fit into the existing 36 | ones consider the following points. 37 | 38 | **Look at examples source code** to get a feel for how statsmodels examples should look like. 39 | 40 | **Build the docs** by running `make html` from the docs directory to see how your example looks in the fully rendered html pages. 41 | -------------------------------------------------------------------------------- /docs/zh/source/dev/get_involved.rst: -------------------------------------------------------------------------------- 1 | Get Involved 2 | ============ 3 | 4 | Where to Start? 5 | --------------- 6 | 7 | Use grep or download a tool like `grin `__ to search the code for TODO notes:: 8 | 9 | grin -i -I "*.py*" todo 10 | 11 | This shows almost 700 TODOs in the code base right now. Feel free to inquire on the mailing list about any of these. 12 | 13 | Sandbox 14 | ------- 15 | 16 | We currently have a large amount code in the :ref:`sandbox`. The medium term goal is to move much of this to feature branches as it gets worked on and remove the sandbox folder. Many of these models and functions are close to done, however, and we welcome any and all contributions to complete them, including refactoring, documentation, and tests. These models include generalized additive models (GAM), information theoretic models such as maximum entropy, survival models, systems of equation models, restricted least squares, panel data models, and time series models such as (G)ARCH. 17 | 18 | .. .. toctree:: 19 | .. :maxdepth: 4 20 | .. 21 | .. ../sandbox 22 | 23 | Contribute an Example 24 | --------------------- 25 | 26 | Contribute an :ref:`example `, add some technical documentation, or contribute a statistics tutorial. 27 | -------------------------------------------------------------------------------- /docs/zh/source/dev/images/git_merge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/zh/source/dev/images/git_merge.png -------------------------------------------------------------------------------- /docs/zh/source/dev/internal.rst: -------------------------------------------------------------------------------- 1 | .. _model: 2 | 3 | 4 | 5 | Internal Classes 6 | ================ 7 | 8 | The following summarizes classes and functions that are not intended to be 9 | directly used, but of interest only for internal use or for a developer who 10 | wants to extend on existing model classes. 11 | 12 | 13 | Module Reference 14 | ---------------- 15 | 16 | Model and Results Classes 17 | ^^^^^^^^^^^^^^^^^^^^^^^^^ 18 | 19 | These are the base classes for both the estimation models and the results. 20 | They are not directly useful, but layout the structure of the subclasses and 21 | define some common methods. 22 | 23 | .. module:: statsmodels.base.model 24 | :synopsis: Base classes that are inherited by models 25 | 26 | .. currentmodule:: statsmodels.base.model 27 | 28 | .. autosummary:: 29 | :toctree: generated/ 30 | 31 | Model 32 | LikelihoodModel 33 | GenericLikelihoodModel 34 | Results 35 | LikelihoodModelResults 36 | ResultMixin 37 | GenericLikelihoodModelResults 38 | 39 | .. module:: statsmodels.stats.contrast 40 | :synopsis: Classes for statistical test 41 | 42 | .. currentmodule:: statsmodels.stats.contrast 43 | 44 | .. autosummary:: 45 | :toctree: generated/ 46 | 47 | ContrastResults 48 | 49 | .. inheritance-diagram:: statsmodels.base.model statsmodels.discrete.discrete_model statsmodels.regression.linear_model statsmodels.miscmodels.count 50 | :parts: 3 51 | 52 | 53 | .. inheritance-diagram:: statsmodels.regression.linear_model.GLS statsmodels.regression.linear_model.WLS statsmodels.regression.linear_model.OLS statsmodels.regression.linear_model.GLSAR 54 | :parts: 1 55 | 56 | Linear Model 57 | ^^^^^^^^^^^^ 58 | 59 | .. inheritance-diagram:: statsmodels.regression.linear_model 60 | :parts: 1 61 | 62 | Generalized Linear Model 63 | ^^^^^^^^^^^^^^^^^^^^^^^^ 64 | 65 | .. inheritance-diagram:: statsmodels.genmod.generalized_linear_model 66 | statsmodels.genmod.families.family statsmodels.genmod.families.links 67 | :parts: 1 68 | 69 | Discrete Model 70 | ^^^^^^^^^^^^^^ 71 | 72 | .. inheritance-diagram:: statsmodels.discrete.discrete_model 73 | :parts: 1 74 | 75 | Robust Model 76 | ^^^^^^^^^^^^ 77 | 78 | .. inheritance-diagram:: statsmodels.robust.robust_linear_model 79 | :parts: 1 80 | 81 | Vector Autoregressive Model 82 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ 83 | 84 | .. inheritance-diagram:: statsmodels.tsa.vector_ar.var_model 85 | :parts: 3 86 | -------------------------------------------------------------------------------- /docs/zh/source/dev/testing.rst: -------------------------------------------------------------------------------- 1 | Testing on Build Machines 2 | ------------------------- 3 | 4 | There are currently several places that statsmodels is automatically built and tested against different dependency and Python versions and architectures. Check these logs periodically, make sure everything looks okay, and fix any failures.: 5 | 6 | * `Travis CI `_ 7 | * `Daily testing on Ubuntu via Python(x,y) `_ 8 | * `NiPy testing on SPARC Boxes `_ 9 | 10 | The test coverage pages are here. 11 | 12 | * `Coveralls `_ 13 | -------------------------------------------------------------------------------- /docs/zh/source/distributions.rst: -------------------------------------------------------------------------------- 1 | .. module:: statsmodels.sandbox.distributions 2 | :synopsis: Probability distributions 3 | 4 | .. currentmodule:: statsmodels.sandbox.distributions 5 | 6 | .. _distributions: 7 | 8 | 9 | Distributions 10 | ============= 11 | 12 | This section collects various additional functions and methods for statistical 13 | distributions. 14 | 15 | Empirical Distributions 16 | ----------------------- 17 | 18 | .. module:: statsmodels.distributions.empirical_distribution 19 | :synopsis: Tools for working with empirical distributions 20 | 21 | .. currentmodule:: statsmodels.distributions.empirical_distribution 22 | 23 | .. autosummary:: 24 | :toctree: generated/ 25 | 26 | ECDF 27 | StepFunction 28 | monotone_fn_inverter 29 | 30 | Distribution Extras 31 | ------------------- 32 | 33 | 34 | .. module:: statsmodels.sandbox.distributions.extras 35 | :synopsis: Probability distributions and random number generators 36 | 37 | .. currentmodule:: statsmodels.sandbox.distributions.extras 38 | 39 | *Skew Distributions* 40 | 41 | .. autosummary:: 42 | :toctree: generated/ 43 | 44 | SkewNorm_gen 45 | SkewNorm2_gen 46 | ACSkewT_gen 47 | skewnorm2 48 | 49 | *Distributions based on Gram-Charlier expansion* 50 | 51 | .. autosummary:: 52 | :toctree: generated/ 53 | 54 | pdf_moments_st 55 | pdf_mvsk 56 | pdf_moments 57 | NormExpan_gen 58 | 59 | *cdf of multivariate normal* wrapper for scipy.stats 60 | 61 | 62 | .. autosummary:: 63 | :toctree: generated/ 64 | 65 | mvstdnormcdf 66 | mvnormcdf 67 | 68 | Univariate Distributions by non-linear Transformations 69 | ------------------------------------------------------ 70 | 71 | Univariate distributions can be generated from a non-linear transformation of an 72 | existing univariate distribution. `Transf_gen` is a class that can generate a new 73 | distribution from a monotonic transformation, `TransfTwo_gen` can use hump-shaped 74 | or u-shaped transformation, such as abs or square. The remaining objects are 75 | special cases. 76 | 77 | .. module:: statsmodels.sandbox.distributions.transformed 78 | :synopsis: Experimental probability distributions and random number generators 79 | 80 | .. currentmodule:: statsmodels.sandbox.distributions.transformed 81 | 82 | .. autosummary:: 83 | :toctree: generated/ 84 | 85 | TransfTwo_gen 86 | Transf_gen 87 | 88 | ExpTransf_gen 89 | LogTransf_gen 90 | SquareFunc 91 | 92 | absnormalg 93 | invdnormalg 94 | 95 | loggammaexpg 96 | lognormalg 97 | negsquarenormalg 98 | 99 | squarenormalg 100 | squaretg 101 | -------------------------------------------------------------------------------- /docs/zh/source/emplike.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.emplike 2 | 3 | 4 | .. _emplike: 5 | 6 | 7 | Empirical Likelihood :mod:`emplike` 8 | ==================================== 9 | 10 | 11 | Introduction 12 | ------------ 13 | 14 | Empirical likelihood is a method of nonparametric inference and estimation that lifts the 15 | obligation of having to specify a family of underlying distributions. Moreover, empirical 16 | likelihood methods do not require re-sampling but still 17 | uniquely determine confidence regions whose shape mirrors the shape of the data. 18 | In essence, empirical likelihood attempts to combine the benefits of parametric 19 | and nonparametric methods while limiting their shortcomings. The main difficulties of 20 | empirical likelihood is the computationally intensive methods required to conduct inference. 21 | :mod:`statsmodels.emplike` attempts to provide a user-friendly interface that allows the 22 | end user to effectively conduct empirical likelihood analysis without having to concern 23 | themselves with the computational burdens. 24 | 25 | Currently, :mod:`emplike` provides methods to conduct hypothesis tests and form confidence 26 | intervals for descriptive statistics. Empirical likelihood estimation and inference 27 | in a regression, accelerated failure time and instrumental variable model are 28 | currently under development. 29 | 30 | References 31 | ^^^^^^^^^^ 32 | 33 | The main reference for empirical likelihood is:: 34 | 35 | Owen, A.B. "Empirical Likelihood." Chapman and Hall, 2001. 36 | 37 | 38 | 39 | Examples 40 | -------- 41 | 42 | .. ipython:: python 43 | 44 | import numpy as np 45 | import statsmodels.api as sm 46 | 47 | # Generate Data 48 | x = np.random.standard_normal(50) 49 | 50 | # initiate EL 51 | el = sm.emplike.DescStat(x) 52 | 53 | # confidence interval for the mean 54 | el.ci_mean() 55 | 56 | # test variance is 1 57 | el.test_var(1) 58 | 59 | 60 | Module Reference 61 | ---------------- 62 | 63 | .. module:: statsmodels.emplike 64 | :synopsis: Empirical likelihood tools 65 | 66 | .. autosummary:: 67 | :toctree: generated/ 68 | 69 | descriptive.DescStat 70 | descriptive.DescStatUV 71 | descriptive.DescStatMV 72 | -------------------------------------------------------------------------------- /docs/zh/source/examples/README: -------------------------------------------------------------------------------- 1 | The format for landing.json should be self-explanatory. The images should be placed in docs/source/_static/images/. They will be displayed at 360 x 225 (W x H). It's best to save them as a png with a resolution of a multiple of at least 720 x 450. If you want, you can use png crush to make the images smaller. 2 | -------------------------------------------------------------------------------- /docs/zh/source/examples/index.rst: -------------------------------------------------------------------------------- 1 | :orphan: 2 | 3 | .. _statsmodels-examples: 4 | 5 | Statsmodels Examples 6 | ==================== 7 | 8 | This page provides a series of examples, tutorials and recipes to help you get 9 | started with ``statsmodels``. Each of the examples shown here is made available 10 | as an IPython Notebook and as a plain python script on the `statsmodels github 11 | repository `_. 12 | 13 | We also encourage users to submit their own examples, tutorials or cool 14 | `statsmodels` trick to the `Examples wiki page 15 | `_ 16 | 17 | The Examples 18 | ------------ 19 | 20 | .. toctree:: 21 | :maxdepth: 3 22 | :glob: 23 | 24 | notebooks/generated/* 25 | -------------------------------------------------------------------------------- /docs/zh/source/extending.rst.TXT: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/zh/source/extending.rst.TXT -------------------------------------------------------------------------------- /docs/zh/source/faq.rst: -------------------------------------------------------------------------------- 1 | :orphan: 2 | 3 | .. currentmodule:: statsmodels 4 | 5 | .. _faq: 6 | 7 | Frequently Asked Question 8 | ------------------------- 9 | 10 | .. _endog-exog-faq: 11 | 12 | What do endog and exog mean? 13 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 14 | 15 | These are shorthand for endogenous and exogenous variables. You might be more comfortable with the common ``y`` and ``X`` notation in linear models. Sometimes the endogenous variable ``y`` is called a dependent variable. Likewise, sometimes the exogenous variables ``X`` are called the independent variables. You can read about this in greater detail at :ref:`endog_exog` 16 | 17 | 18 | .. _missing-faq: 19 | 20 | How does statsmodels handle missing data? 21 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 22 | 23 | Missing data can be handled via the ``missing`` keyword argument. Every model takes this keyword. You can find more information in the docstring of :class:`statsmodels.base.Model `. 24 | 25 | .. `Model class `_. 26 | 27 | .. _build-faq: 28 | 29 | Why won't statsmodels build? 30 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 31 | 32 | If you're on Python 3.4, you *must* use Cython 0.20.1. If you're still having problems, try running 33 | 34 | .. code-block:: bash 35 | 36 | python setup.py clean 37 | 38 | What if my question isn't answered here? 39 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 40 | 41 | You may find answers for questions that have not yet been added here on GitHub under the `FAQ issues tag `_. If not, please ask your question on stackoverflow using the `statsmodels tag `_ or on the `mailing list `_. 42 | -------------------------------------------------------------------------------- /docs/zh/source/genericmle.rst.TXT: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/zh/source/genericmle.rst.TXT -------------------------------------------------------------------------------- /docs/zh/source/glm_techn1.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.glm 2 | 3 | 4 | .. _glm_techn1: 5 | 6 | Technical Documentation 7 | ======================= 8 | 9 | Introduction 10 | ------------ 11 | 12 | Just a placeholder 13 | -------------------------------------------------------------------------------- /docs/zh/source/glm_techn2.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.glm 2 | 3 | 4 | .. _glm_techn2: 5 | 6 | Technical Documentation - part 2 7 | ================================ 8 | 9 | Implementation Notes 10 | -------------------- 11 | 12 | Just a placeholder 13 | -------------------------------------------------------------------------------- /docs/zh/source/gmm.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.sandbox.regression.gmm 2 | 3 | 4 | .. _gmm: 5 | 6 | 7 | Generalized Method of Moments :mod:`gmm` 8 | ======================================== 9 | 10 | :mod:`statsmodels.gmm` contains model classes and functions that are based on 11 | estimation with Generalized Method of Moments. 12 | Currently the general non-linear case is implemented. An example class for the standard 13 | linear instrumental variable model is included. This has been introduced as a test case, it 14 | works correctly but it does not take the linear structure into account. For the linear 15 | case we intend to introduce a specific implementation which will be faster and numerically 16 | more accurate. 17 | 18 | Currently, GMM takes arbitrary non-linear moment conditions and calculates the estimates 19 | either for a given weighting matrix or iteratively by alternating between estimating 20 | the optimal weighting matrix and estimating the parameters. Implementing models with 21 | different moment conditions is done by subclassing GMM. In the minimal implementation 22 | only the moment conditions, `momcond` have to be defined. 23 | 24 | .. currentmodule:: statsmodels.sandbox.regression.gmm 25 | 26 | 27 | Module Reference 28 | """""""""""""""" 29 | 30 | .. module:: statsmodels.sandbox.regression.gmm 31 | :synopsis: A framework for implementing Generalized Method of Moments (GMM) 32 | 33 | .. autosummary:: 34 | :toctree: generated/ 35 | 36 | GMM 37 | GMMResults 38 | IV2SLS 39 | IVGMM 40 | IVGMMResults 41 | IVRegressionResults 42 | LinearIVGMM 43 | NonlinearIVGMM 44 | -------------------------------------------------------------------------------- /docs/zh/source/gmm_techn1.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.sandbox.regression.gmm 2 | 3 | 4 | .. _gmm_techn1: 5 | 6 | Technical Documentation 7 | ======================= 8 | 9 | Introduction 10 | ------------ 11 | 12 | Generalized Method of Moments is an extension of the Method of Moments 13 | if there are more moment conditions than parameters that are estimated. 14 | 15 | simple example 16 | 17 | 18 | General Structure and Implementation 19 | ------------------------------------ 20 | 21 | The main class for GMM estimation, makes little assumptions about the 22 | moment conditions. It is designed for the general case when moment 23 | conditions are given as function by the user. 24 | 25 | :: 26 | 27 | def momcond(params) 28 | 29 | which should return a two dimensional array with observation in rows 30 | and moment conditions in columns. Denote this function by `$g(\theta)$`. Then 31 | the GMM estimator is given as the solution to the maximization problem: 32 | 33 | ..math: max_{\theta) g(theta)' W g(theta) (1) 34 | 35 | The weighting matrix can be estimated in several different ways. The 36 | basic method `fitgmm` takes the weighting matrix as argument or if it is 37 | not given takes the identity matrix and maximizes (1) 38 | taking W as given. Since the optimizing functions solve minimization problems, 39 | we usually minimizes the negative of the objective function. 40 | `fit_iterative` calculates the optimal weighting matrix and maximizes the 41 | criterion function in alternating steps. The number of iterations can 42 | be given as an argument to this fit method. The optimal weighting matrix, 43 | which is the covariance matrix of the moment conditions, can be estimated 44 | in different ways. Kernel and shrinkage estimators are planned but not yet 45 | implemented. TODO 46 | 47 | The GMM class itself does not define any moment conditions. To get an 48 | estimator for given moment conditions, GMM needs to be subclassed. 49 | The basic structure of writing new models based on 50 | the generic MLE or GMM framework and subclassing is described in 51 | `extending.rst` (TODO: link) 52 | 53 | As an example 54 | -------------------------------------------------------------------------------- /docs/zh/source/graphics.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.graphics 2 | 3 | .. _graphics: 4 | 5 | Graphics 6 | ======== 7 | 8 | .. automodule:: statsmodels.graphics 9 | 10 | Goodness of Fit Plots 11 | --------------------- 12 | 13 | .. autosummary:: 14 | :toctree: generated/ 15 | 16 | gofplots.qqplot 17 | gofplots.qqline 18 | gofplots.qqplot_2samples 19 | gofplots.ProbPlot 20 | 21 | Boxplots 22 | -------- 23 | 24 | .. autosummary:: 25 | :toctree: generated/ 26 | 27 | boxplots.violinplot 28 | boxplots.beanplot 29 | 30 | Correlation Plots 31 | ------------------ 32 | 33 | .. autosummary:: 34 | :toctree: generated/ 35 | 36 | correlation.plot_corr 37 | correlation.plot_corr_grid 38 | plot_grids.scatter_ellipse 39 | 40 | Functional Plots 41 | ---------------- 42 | 43 | .. autosummary:: 44 | :toctree: generated/ 45 | 46 | functional.hdrboxplot 47 | functional.fboxplot 48 | functional.rainbowplot 49 | functional.banddepth 50 | 51 | Regression Plots 52 | ---------------- 53 | 54 | .. autosummary:: 55 | :toctree: generated/ 56 | 57 | regressionplots.plot_fit 58 | regressionplots.plot_regress_exog 59 | regressionplots.plot_partregress 60 | regressionplots.plot_ccpr 61 | regressionplots.abline_plot 62 | regressionplots.influence_plot 63 | regressionplots.plot_leverage_resid2 64 | 65 | Time Series Plots 66 | ----------------- 67 | 68 | .. autosummary:: 69 | :toctree: generated/ 70 | 71 | tsaplots.plot_acf 72 | tsaplots.plot_pacf 73 | tsaplots.month_plot 74 | tsaplots.quarter_plot 75 | 76 | Other Plots 77 | ----------- 78 | 79 | .. 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The 15 | basic idea is to treat each variable with missing values as the 16 | dependent variable in a regression, with some or all of the remaining 17 | variables as its predictors. The MICE procedure cycles through these 18 | models, fitting each in turn, then uses a procedure called "predictive 19 | mean matching" (PMM) to generate random draws from the predictive 20 | distributions determined by the fitted models. These random draws 21 | become the imputed values for one imputed data set. 22 | 23 | By default, each variable with missing variables is modeled using a 24 | linear regression with main effects for all other variables in the 25 | data set. Note that even when the imputation model is linear, the PMM 26 | procedure preserves the domain of each variable. Thus, for example, 27 | if all observed values for a given variable are positive, all imputed 28 | values for the variable will always be positive. The user also has 29 | the option to specify which model is used to produce imputed values 30 | for each variable. 31 | 32 | .. code 33 | 34 | 35 | Classes 36 | ------- 37 | 38 | .. currentmodule:: statsmodels.imputation.mice 39 | 40 | .. autosummary:: 41 | :toctree: generated/ 42 | 43 | MICE 44 | MICEData 45 | 46 | 47 | Implementation Details 48 | ---------------------- 49 | 50 | Internally, this function uses 51 | `pandas.isnull `_. 52 | Anything that returns True from this function will be treated as missing data. 53 | -------------------------------------------------------------------------------- /docs/zh/source/iolib.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.iolib 2 | 3 | .. _iolib: 4 | 5 | Input-Output :mod:`iolib` 6 | ========================= 7 | 8 | ``statsmodels`` offers some functions for input and output. These include a 9 | reader for STATA files, a class for generating tables for printing in several 10 | formats and two helper functions for pickling. 11 | 12 | Users can also leverage the powerful input/output functions provided by :ref:`pandas.io `. Among other things, ``pandas`` (a ``statsmodels`` dependency) allows reading and writing to Excel, CSV, and HDF5 (PyTables). 13 | 14 | Examples 15 | -------- 16 | 17 | `SimpleTable: Basic example `__ 18 | 19 | Module Reference 20 | ---------------- 21 | 22 | .. module:: statsmodels.iolib 23 | :synopsis: Tools for reading datasets and producing summary output 24 | 25 | .. autosummary:: 26 | :toctree: generated/ 27 | 28 | foreign.StataReader 29 | foreign.StataWriter 30 | foreign.genfromdta 31 | foreign.savetxt 32 | table.SimpleTable 33 | table.csv2st 34 | smpickle.save_pickle 35 | smpickle.load_pickle 36 | 37 | 38 | The following are classes and functions used to return the summary of 39 | estimation results, and mostly intended for internal use. There are currently 40 | two versions for creating summaries. 41 | 42 | .. autosummary:: 43 | :toctree: generated/ 44 | 45 | summary.Summary 46 | summary2.Summary 47 | -------------------------------------------------------------------------------- /docs/zh/source/miscmodels.rst: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | .. module:: statsmodels.miscmodels 5 | .. currentmodule:: statsmodels.miscmodels 6 | 7 | 8 | .. _miscmodels: 9 | 10 | 11 | Other Models :mod:`miscmodels` 12 | ============================== 13 | 14 | :mod:`statsmodels.miscmodels` contains model classes and that do not yet fit into 15 | any other category, or are basic implementations that are not yet polished and will most 16 | likely still change. Some of these models were written as examples for the generic 17 | maximum likelihood framework, and there will be others that might be based on general 18 | method of moments. 19 | 20 | The models in this category have been checked for basic cases, but might be more exposed 21 | to numerical problems than the complete implementation. For example, count.Poisson has 22 | been added using only the generic maximum likelihood framework, the standard errors 23 | are based on the numerical evaluation of the Hessian, while discretemod.Poisson uses 24 | analytical Gradients and Hessian and will be more precise, especially in cases when there 25 | is strong multicollinearity. 26 | On the other hand, by subclassing GenericLikelihoodModel, it is easy to add new models, 27 | another example can be seen in the zero inflated Poisson model, miscmodels.count. 28 | 29 | 30 | Count Models :mod:`count` 31 | -------------------------- 32 | 33 | .. module:: statsmodels.miscmodels.count 34 | .. currentmodule:: statsmodels.miscmodels.count 35 | 36 | .. autosummary:: 37 | :toctree: generated/ 38 | 39 | PoissonGMLE 40 | PoissonOffsetGMLE 41 | PoissonZiGMLE 42 | 43 | Linear Model with t-distributed errors 44 | -------------------------------------- 45 | 46 | This is a class that shows that a new model can be defined by only specifying the 47 | method for the loglikelihood. All result statistics are inherited from the generic 48 | likelihood model and result classes. The results have been checked against R for a 49 | simple case. 50 | 51 | .. module:: statsmodels.miscmodels.tmodel 52 | .. currentmodule:: statsmodels.miscmodels.tmodel 53 | 54 | .. autosummary:: 55 | :toctree: generated/ 56 | 57 | TLinearModel 58 | 59 | 60 | 61 | 62 | -------------------------------------------------------------------------------- /docs/zh/source/missing.rst: -------------------------------------------------------------------------------- 1 | :orphan: 2 | 3 | .. _missing_data: 4 | 5 | Missing Data 6 | ------------ 7 | All of the models can handle missing data. For performance reasons, the default is not to do any checking for missing data. If, however, you would like for missing data to be handled internally, you can do so by using the missing keyword argument. The default is to do nothing 8 | 9 | .. ipython:: python 10 | 11 | import statsmodels.api as sm 12 | data = sm.datasets.longley.load(as_pandas=False) 13 | data.exog = sm.add_constant(data.exog) 14 | # add in some missing data 15 | missing_idx = np.array([False] * len(data.endog)) 16 | missing_idx[[4, 10, 15]] = True 17 | data.endog[missing_idx] = np.nan 18 | ols_model = sm.OLS(data.endog, data.exog) 19 | ols_fit = ols_model.fit() 20 | print(ols_fit.params) 21 | 22 | This silently fails and all of the model parameters are NaN, which is probably not what you expected. If you are not sure whether or not you have missing data you can use `missing = 'raise'`. This will raise a `MissingDataError` during model instantiation if missing data is present so that you know something was wrong in your input data. 23 | 24 | .. ipython:: python 25 | :okexcept: 26 | 27 | ols_model = sm.OLS(data.endog, data.exog, missing='raise') 28 | 29 | If you want statsmodels to handle the missing data by dropping the observations, use `missing = 'drop'`. 30 | 31 | .. ipython:: python 32 | 33 | ols_model = sm.OLS(data.endog, data.exog, missing='drop') 34 | 35 | We are considering adding a configuration framework so that you can set the option with a global setting. 36 | -------------------------------------------------------------------------------- /docs/zh/source/mixed_glm.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.genmod.bayes_mixed_glm 2 | 3 | Generalized Linear Mixed Effects Models 4 | ======================================= 5 | 6 | Generalized Linear Mixed Effects (GLIMMIX) models are generalized 7 | linear models with random effects in the linear predictors. 8 | Statsmodels currently supports estimation of binomial and Poisson 9 | GLIMMIX models using two Bayesian methods: the Laplace approximation 10 | to the posterior, and a variational Bayes approximation to the 11 | posterior. Both methods provide point estimates (posterior means) and 12 | assessments of uncertainty (posterior standard deviation). 13 | 14 | The current implementation only supports independent random effects. 15 | 16 | Technical Documentation 17 | ----------------------- 18 | 19 | Unlike Statsmodels mixed linear models, the GLIMMIX implementation is 20 | not group-based. Groups are created by interacting all random effects 21 | with a categorical variable. Note that this creates large, sparse 22 | random effects design matrices `exog_vc`. Internally, `exog_vc` is 23 | converted to a scipy sparse matrix. When passing the arguments 24 | directly to the class initializer, a sparse matrix may be passed. 25 | When using formulas, a dense matrix is created then converted to 26 | sparse. For very large problems, it may not be feasible to use 27 | formulas due to the size of this dense intermediate matrix. 28 | 29 | References 30 | ^^^^^^^^^^ 31 | 32 | Blei, Kucukelbir, McAuliffe (2017). Variational Inference: A review 33 | for Statisticians https://arxiv.org/pdf/1601.00670.pdf 34 | 35 | Module Reference 36 | ---------------- 37 | 38 | .. module:: statsmodels.genmod.bayes_mixed_glm 39 | :synopsis: Bayes Mixed Generalized Linear Models 40 | 41 | 42 | The model classes are: 43 | 44 | .. autosummary:: 45 | :toctree: generated/ 46 | 47 | BinomialBayesMixedGLM 48 | PoissonBayesMixedGLM 49 | 50 | The result class is: 51 | 52 | .. autosummary:: 53 | :toctree: generated/ 54 | 55 | BayesMixedGLMResults 56 | -------------------------------------------------------------------------------- /docs/zh/source/multivariate.rst: -------------------------------------------------------------------------------- 1 | .. module:: statsmodels.multivariate 2 | :synopsis: Models for multivariate data 3 | 4 | .. currentmodule:: statsmodels.multivariate 5 | 6 | .. _multivariate: 7 | 8 | 9 | Multivariate Statistics :mod:`multivariate` 10 | =========================================== 11 | 12 | This section includes methods and algorithms from multivariate statistics. 13 | 14 | 15 | Principal Component Analysis 16 | ---------------------------- 17 | 18 | .. module:: statsmodels.multivariate.pca 19 | :synopsis: Principal Component Analaysis 20 | 21 | .. currentmodule:: statsmodels.multivariate.pca 22 | 23 | .. autosummary:: 24 | :toctree: generated/ 25 | 26 | PCA 27 | pca 28 | 29 | 30 | Factor Analysis 31 | --------------- 32 | 33 | .. currentmodule:: statsmodels.multivariate.factor 34 | 35 | .. autosummary:: 36 | :toctree: generated/ 37 | 38 | Factor 39 | FactorResults 40 | 41 | 42 | Factor Rotation 43 | --------------- 44 | 45 | .. currentmodule:: statsmodels.multivariate.factor_rotation 46 | 47 | .. autosummary:: 48 | :toctree: generated/ 49 | 50 | rotate_factors 51 | target_rotation 52 | procrustes 53 | promax 54 | 55 | 56 | Canonical Correlation 57 | --------------------- 58 | 59 | .. currentmodule:: statsmodels.multivariate.cancorr 60 | 61 | .. autosummary:: 62 | :toctree: generated/ 63 | 64 | CanCorr 65 | 66 | 67 | MANOVA 68 | ------ 69 | 70 | .. currentmodule:: statsmodels.multivariate.manova 71 | 72 | .. autosummary:: 73 | :toctree: generated/ 74 | 75 | MANOVA 76 | 77 | 78 | MultivariateOLS 79 | --------------- 80 | 81 | `_MultivariateOLS` is a model class with limited features. Currently it 82 | supports multivariate hypothesis tests and is used as backend for MANOVA. 83 | 84 | .. currentmodule:: statsmodels.multivariate.multivariate_ols 85 | 86 | .. autosummary:: 87 | :toctree: generated/ 88 | 89 | _MultivariateOLS 90 | _MultivariateOLSResults 91 | MultivariateTestResults 92 | -------------------------------------------------------------------------------- /docs/zh/source/plots/arma_predict_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | import matplotlib.pyplot as plt 3 | import pandas as pd 4 | 5 | dta = sm.datasets.sunspots.load_pandas().data[['SUNACTIVITY']] 6 | dta.index = pd.DatetimeIndex(start='1700', end='2009', freq='A') 7 | res = sm.tsa.ARMA(dta, (3, 0)).fit(disp=0) 8 | fig, ax = plt.subplots() 9 | ax = dta.loc['1950':].plot(ax=ax) 10 | res.plot_predict('1990', '2012', dynamic=True, ax=ax, 11 | plot_insample=False) 12 | -------------------------------------------------------------------------------- /docs/zh/source/plots/bkf_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | 3 | from load_macrodata import dta 4 | 5 | cycles = sm.tsa.filters.bkfilter(dta[['realinv']], 6, 24, 12) 6 | 7 | import matplotlib.pyplot as plt 8 | fig, ax = plt.subplots() 9 | cycles.plot(ax=ax, style=['r--', 'b-']) 10 | -------------------------------------------------------------------------------- /docs/zh/source/plots/cff_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | 3 | from load_macrodata import dta 4 | 5 | cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[["infl", "unemp"]]) 6 | 7 | import matplotlib.pyplot as plt 8 | fig, ax = plt.subplots() 9 | cf_cycles.plot(ax=ax, style=['r--', 'b-']) 10 | -------------------------------------------------------------------------------- /docs/zh/source/plots/graphics-mean_diff_plot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Test Bland-Altman 3 | """ 4 | 5 | Author: Joses Ho 6 | 7 | """ 8 | 9 | import statsmodels.api as sm 10 | import numpy as np 11 | import matplotlib.pyplot as plt 12 | 13 | # Seed the random number generator. 14 | # This ensures that the results below are reproducible. 15 | np.random.seed(9999) 16 | m1 = np.random.random(20) 17 | m2 = np.random.random(20) 18 | 19 | f, ax = plt.subplots(1, figsize = (8,5)) 20 | sm.graphics.mean_diff_plot(m1, m2, ax = ax) 21 | 22 | plt.show() 23 | -------------------------------------------------------------------------------- /docs/zh/source/plots/graphics_boxplot_beanplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Fri May 04 00:22:40 2012 5 | 6 | Author: Ralf Gommers 7 | 8 | """ 9 | 10 | import numpy as np 11 | import matplotlib.pyplot as plt 12 | import statsmodels.api as sm 13 | data = sm.datasets.anes96.load_pandas() 14 | party_ID = np.arange(7) 15 | labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", 16 | "Independent-Indpendent", "Independent-Republican", 17 | "Weak Republican", "Strong Republican"] 18 | 19 | plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible 20 | age = [data.exog['age'][data.endog == id] for id in party_ID] 21 | fig = plt.figure() 22 | ax = fig.add_subplot(111) 23 | sm.graphics.beanplot(age, ax=ax, labels=labels, 24 | plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 25 | 'label_fontsize':'small', 26 | 'label_rotation':30}) 27 | ax.set_xlabel("Party identification of respondent.") 28 | ax.set_ylabel("Age") 29 | 30 | #plt.show() 31 | -------------------------------------------------------------------------------- /docs/zh/source/plots/graphics_boxplot_violinplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Fri May 04 00:11:32 2012 5 | 6 | Author: Ralf Gommers 7 | 8 | """ 9 | import numpy as np 10 | import matplotlib.pyplot as plt 11 | import statsmodels.api as sm 12 | 13 | data = sm.datasets.anes96.load_pandas() 14 | party_ID = np.arange(7) 15 | labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", 16 | "Independent-Indpendent", "Independent-Republican", 17 | "Weak Republican", "Strong Republican"] 18 | plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible 19 | age = [data.exog['age'][data.endog == id] for id in party_ID] 20 | fig = plt.figure() 21 | ax = fig.add_subplot(111) 22 | sm.graphics.violinplot(age, ax=ax, labels=labels, 23 | plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 24 | 'label_fontsize':'small', 25 | 'label_rotation':30}) 26 | ax.set_xlabel("Party identification of respondent.") 27 | ax.set_ylabel("Age") 28 | 29 | #plt.show() 30 | -------------------------------------------------------------------------------- /docs/zh/source/plots/graphics_functional_fboxplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Fri May 04 11:10:51 2012 5 | 6 | Author: Ralf Gommers 7 | 8 | """ 9 | 10 | #Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea 11 | #surface temperature data. 12 | 13 | import numpy as np 14 | import matplotlib.pyplot as plt 15 | import statsmodels.api as sm 16 | data = sm.datasets.elnino.load(as_pandas=False) 17 | 18 | #Create a functional boxplot. We see that the years 1982-83 and 1997-98 are 19 | #outliers; these are the years where El Nino (a climate pattern 20 | #characterized by warming up of the sea surface and higher air pressures) 21 | #occurred with unusual intensity. 22 | 23 | fig = plt.figure() 24 | ax = fig.add_subplot(111) 25 | res = sm.graphics.fboxplot(data.raw_data[:, 1:], wfactor=2.58, 26 | labels=data.raw_data[:, 0].astype(int), 27 | ax=ax) 28 | 29 | ax.set_xlabel("Month of the year") 30 | ax.set_ylabel("Sea surface temperature (C)") 31 | ax.set_xticks(np.arange(13, step=3) - 1) 32 | ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) 33 | ax.set_xlim([-0.2, 11.2]) 34 | 35 | #plt.show() 36 | -------------------------------------------------------------------------------- /docs/zh/source/plots/graphics_functional_hdrboxplot.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | 3 | #Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea 4 | #surface temperature data. 5 | 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | import statsmodels.api as sm 9 | data = sm.datasets.elnino.load(as_pandas=False) 10 | 11 | #Create a HDR functional boxplot. We see that the years 1982-83 and 1997-98 are 12 | #outliers; these are the years where El Nino (a climate pattern 13 | #characterized by warming up of the sea surface and higher air pressures) 14 | #occurred with unusual intensity. 15 | 16 | fig = plt.figure() 17 | ax = fig.add_subplot(111) 18 | fig, res = sm.graphics.hdrboxplot(data.raw_data[:, 1:], 19 | labels=data.raw_data[:, 0].astype(int), 20 | ax=ax) 21 | 22 | ax.plot([0, 10], [25, 25]) 23 | ax.set_xlabel("Month of the year") 24 | ax.set_ylabel("Sea surface temperature (C)") 25 | ax.set_xticks(np.arange(13, step=3) - 1) 26 | ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) 27 | ax.set_xlim([-0.2, 11.2]) 28 | 29 | plt.show() 30 | 31 | print(res) 32 | -------------------------------------------------------------------------------- /docs/zh/source/plots/graphics_functional_rainbowplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Fri May 04 11:08:56 2012 5 | 6 | Author: Ralf Gommers 7 | 8 | """ 9 | 10 | #Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea 11 | #surface temperature data. 12 | 13 | import numpy as np 14 | import matplotlib.pyplot as plt 15 | import statsmodels.api as sm 16 | data = sm.datasets.elnino.load(as_pandas=False) 17 | 18 | #Create a rainbow plot: 19 | 20 | fig = plt.figure() 21 | ax = fig.add_subplot(111) 22 | res = sm.graphics.rainbowplot(data.raw_data[:, 1:], ax=ax) 23 | 24 | ax.set_xlabel("Month of the year") 25 | ax.set_ylabel("Sea surface temperature (C)") 26 | ax.set_xticks(np.arange(13, step=3) - 1) 27 | ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) 28 | ax.set_xlim([-0.2, 11.2]) 29 | 30 | #plt.show() 31 | -------------------------------------------------------------------------------- /docs/zh/source/plots/graphics_gofplots_qqplot.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Sun May 06 05:32:15 2012 4 | 5 | Author: Josef Perktold 6 | editted by: Paul Hobson (2012-08-19) 7 | """ 8 | from scipy import stats 9 | from matplotlib import pyplot as plt 10 | import statsmodels.api as sm 11 | 12 | #example from docstring 13 | data = sm.datasets.longley.load(as_pandas=False) 14 | data.exog = sm.add_constant(data.exog, prepend=True) 15 | mod_fit = sm.OLS(data.endog, data.exog).fit() 16 | res = mod_fit.resid 17 | 18 | left = -1.8 #x coordinate for text insert 19 | 20 | fig = plt.figure() 21 | 22 | ax = fig.add_subplot(2, 2, 1) 23 | sm.graphics.qqplot(res, ax=ax) 24 | top = ax.get_ylim()[1] * 0.75 25 | txt = ax.text(left, top, 'no keywords', verticalalignment='top') 26 | txt.set_bbox(dict(facecolor='k', alpha=0.1)) 27 | 28 | ax = fig.add_subplot(2, 2, 2) 29 | sm.graphics.qqplot(res, line='s', ax=ax) 30 | top = ax.get_ylim()[1] * 0.75 31 | txt = ax.text(left, top, "line='s'", verticalalignment='top') 32 | txt.set_bbox(dict(facecolor='k', alpha=0.1)) 33 | 34 | ax = fig.add_subplot(2, 2, 3) 35 | sm.graphics.qqplot(res, line='45', fit=True, ax=ax) 36 | ax.set_xlim(-2, 2) 37 | top = ax.get_ylim()[1] * 0.75 38 | txt = ax.text(left, top, "line='45', \nfit=True", verticalalignment='top') 39 | txt.set_bbox(dict(facecolor='k', alpha=0.1)) 40 | 41 | ax = fig.add_subplot(2, 2, 4) 42 | sm.graphics.qqplot(res, dist=stats.t, line='45', fit=True, ax=ax) 43 | ax.set_xlim(-2, 2) 44 | top = ax.get_ylim()[1] * 0.75 45 | txt = ax.text(left, top, "dist=stats.t, \nline='45', \nfit=True", 46 | verticalalignment='top') 47 | txt.set_bbox(dict(facecolor='k', alpha=0.1)) 48 | 49 | fig.tight_layout() 50 | 51 | plt.gcf() 52 | 53 | 54 | # example with the new ProbPlot class 55 | import numpy as np 56 | x = np.random.normal(loc=8.25, scale=3.5, size=37) 57 | y = np.random.normal(loc=8.00, scale=3.25, size=37) 58 | pp_x = sm.ProbPlot(x, fit=True) 59 | pp_y = sm.ProbPlot(y, fit=True) 60 | 61 | # probability of exceedance 62 | fig2 = pp_x.probplot(exceed=True) 63 | 64 | # compare x quantiles to y quantiles 65 | fig3 = pp_x.qqplot(other=pp_y, line='45') 66 | 67 | # same as above with probabilities/percentiles 68 | fig4 = pp_x.ppplot(other=pp_y, line='45') 69 | -------------------------------------------------------------------------------- /docs/zh/source/plots/graphics_month_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | import pandas as pd 3 | 4 | dta = sm.datasets.elnino.load_pandas().data 5 | dta['YEAR'] = dta.YEAR.astype(int).astype(str) 6 | dta = dta.set_index('YEAR').T.unstack() 7 | dates = pd.to_datetime(list(map(lambda x : '-'.join(x) + '-1', dta.index.values))) 8 | 9 | dta.index = pd.DatetimeIndex(list(dates), freq='MS') 10 | dta.name = 'temp' 11 | fig = sm.graphics.tsa.month_plot(dta) 12 | -------------------------------------------------------------------------------- /docs/zh/source/plots/graphics_plot_fit_ex.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | 4 | Created on Monday April 1st 2013 5 | 6 | Author: Padarn Wilson 7 | 8 | """ 9 | 10 | # Load the Statewide Crime data set and perform linear regression with 11 | # 'poverty' and 'hs_grad' as variables and 'muder' as the response 12 | 13 | 14 | import statsmodels.api as sm 15 | import matplotlib.pyplot as plt 16 | import numpy as np 17 | 18 | data = sm.datasets.statecrime.load_pandas().data 19 | murder = data['murder'] 20 | X = data[['poverty', 'hs_grad']].copy() 21 | X['constant'] = 1 22 | 23 | y = murder 24 | model = sm.OLS(y, X) 25 | results = model.fit() 26 | 27 | # Create a plot just for the variable 'Poverty': 28 | 29 | fig, ax = plt.subplots() 30 | fig = sm.graphics.plot_fit(results, 0, ax=ax) 31 | ax.set_ylabel("Murder Rate") 32 | ax.set_xlabel("Poverty Level") 33 | ax.set_title("Linear Regression") 34 | 35 | plt.show() 36 | -------------------------------------------------------------------------------- /docs/zh/source/plots/hpf_plot.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | import pandas as pd 3 | from load_macrodata import dta 4 | 5 | cycle, trend = sm.tsa.filters.hpfilter(dta.realgdp, 1600) 6 | gdp_decomp = dta[['realgdp']].copy() 7 | gdp_decomp["cycle"] = cycle 8 | gdp_decomp["trend"] = trend 9 | 10 | import matplotlib.pyplot as plt 11 | fig, ax = plt.subplots() 12 | gdp_decomp[["realgdp", "trend"]]["2000-03-31":].plot(ax=ax, 13 | fontsize=16) 14 | -------------------------------------------------------------------------------- /docs/zh/source/plots/load_macrodata.py: -------------------------------------------------------------------------------- 1 | import statsmodels.api as sm 2 | import pandas as pd 3 | dta = sm.datasets.macrodata.load_pandas().data 4 | dates = sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3') 5 | index = pd.DatetimeIndex(dates) 6 | dta.set_index(index, inplace=True) 7 | -------------------------------------------------------------------------------- /docs/zh/source/plots/var_plot_acorr.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_acorr 2 | plot_acorr() 3 | -------------------------------------------------------------------------------- /docs/zh/source/plots/var_plot_fevd.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_fevd 2 | plot_fevd() 3 | -------------------------------------------------------------------------------- /docs/zh/source/plots/var_plot_forecast.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_forecast 2 | plot_forecast() 3 | -------------------------------------------------------------------------------- /docs/zh/source/plots/var_plot_input.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_input 2 | plot_input() 3 | -------------------------------------------------------------------------------- /docs/zh/source/plots/var_plot_irf.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_irf 2 | plot_irf() 3 | -------------------------------------------------------------------------------- /docs/zh/source/plots/var_plot_irf_cum.py: -------------------------------------------------------------------------------- 1 | from var_plots import plot_irf_cum 2 | plot_irf_cum() 3 | -------------------------------------------------------------------------------- /docs/zh/source/plots/var_plots.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from statsmodels.tsa.api import VAR 4 | from statsmodels.api import datasets as ds 5 | from statsmodels.tsa.base.datetools import dates_from_str 6 | 7 | 8 | import pandas 9 | mdata = ds.macrodata.load_pandas().data 10 | 11 | # prepare the dates index 12 | dates = mdata[['year', 'quarter']].astype(int) 13 | quarterly = [str(yr) + 'Q' + str(mo) 14 | for yr, mo in zip(dates["year"], dates["quarter"])] 15 | quarterly = dates_from_str(quarterly) 16 | 17 | mdata = mdata[['realgdp','realcons','realinv']] 18 | mdata.index = pandas.DatetimeIndex(quarterly) 19 | data = np.log(mdata).diff().dropna() 20 | 21 | model = VAR(data) 22 | est = model.fit(maxlags=2) 23 | 24 | def plot_input(): 25 | est.plot() 26 | 27 | def plot_acorr(): 28 | est.plot_acorr() 29 | 30 | def plot_irf(): 31 | est.irf().plot() 32 | 33 | def plot_irf_cum(): 34 | irf = est.irf() 35 | irf.plot_cum_effects() 36 | 37 | def plot_forecast(): 38 | est.plot_forecast(10) 39 | 40 | def plot_fevd(): 41 | est.fevd(20).plot() 42 | -------------------------------------------------------------------------------- /docs/zh/source/regression_techn1.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.regression 2 | 3 | 4 | .. _regression-techn1: 5 | 6 | Technical Documentation 7 | ======================= 8 | 9 | Introduction 10 | ------------ 11 | 12 | Just a placeholder 13 | -------------------------------------------------------------------------------- /docs/zh/source/release/index.rst: -------------------------------------------------------------------------------- 1 | .. During each release add in this folder information about important changes. 2 | .. Each versionx.x.rst file should have four main sections. 3 | .. (1) Major features (2) Important bug fixes (3) API breakage (4) Credits 4 | 5 | .. The github-stats-x.x.rst files are generated by tools/github_stats.py with 6 | .. some cleanup afterwards. I do python github_stats.py > github-stats-x.x.rst. 7 | .. As of the 0.5 release, this script asks for your github name and password 8 | .. to download the statistics. 9 | 10 | .. _whatsnew_index: 11 | 12 | ========================= 13 | What's new in Statsmodels 14 | ========================= 15 | 16 | .. toctree:: 17 | :maxdepth: 1 18 | 19 | version0.9 20 | version0.8 21 | version0.7 22 | version0.6 23 | github-stats-0.6 24 | version0.5 25 | github-stats-0.5 26 | 27 | For an overview of changes that occured previous to the 0.5.0 release see :ref:`old_changes`. 28 | -------------------------------------------------------------------------------- /docs/zh/source/rlm.rst: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.robust 2 | 3 | 4 | .. _rlm: 5 | 6 | Robust Linear Models 7 | ==================== 8 | 9 | Robust linear models with support for the M-estimators listed under `Norms`_. 10 | 11 | See `Module Reference`_ for commands and arguments. 12 | 13 | Examples 14 | -------- 15 | 16 | .. ipython:: python 17 | 18 | # Load modules and data 19 | import statsmodels.api as sm 20 | data = sm.datasets.stackloss.load(as_pandas=False) 21 | data.exog = sm.add_constant(data.exog) 22 | 23 | # Fit model and print summary 24 | rlm_model = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()) 25 | rlm_results = rlm_model.fit() 26 | print(rlm_results.params) 27 | 28 | Detailed examples can be found here: 29 | 30 | * `Robust Models 1 `__ 31 | * `Robust Models 2 `__ 32 | 33 | Technical Documentation 34 | ----------------------- 35 | 36 | .. toctree:: 37 | :maxdepth: 1 38 | 39 | rlm_techn1 40 | 41 | References 42 | ^^^^^^^^^^ 43 | 44 | * PJ Huber. ‘Robust Statistics’ John Wiley and Sons, Inc., New York. 1981. 45 | * PJ Huber. 1973, ‘The 1972 Wald Memorial Lectures: Robust Regression: Asymptotics, Conjectures, and Monte Carlo.’ The Annals of Statistics, 1.5, 799-821. 46 | * R Venables, B Ripley. ‘Modern Applied Statistics in S’ Springer, New York, 47 | 48 | Module Reference 49 | ---------------- 50 | 51 | .. module:: statsmodels.robust 52 | 53 | Model Classes 54 | ^^^^^^^^^^^^^ 55 | 56 | .. module:: statsmodels.robust.robust_linear_model 57 | .. currentmodule:: statsmodels.robust.robust_linear_model 58 | 59 | .. autosummary:: 60 | :toctree: generated/ 61 | 62 | RLM 63 | 64 | Model Results 65 | ^^^^^^^^^^^^^ 66 | 67 | .. autosummary:: 68 | :toctree: generated/ 69 | 70 | RLMResults 71 | 72 | .. _norms: 73 | 74 | Norms 75 | ^^^^^ 76 | 77 | .. module:: statsmodels.robust.norms 78 | .. currentmodule:: statsmodels.robust.norms 79 | 80 | .. autosummary:: 81 | :toctree: generated/ 82 | 83 | AndrewWave 84 | Hampel 85 | HuberT 86 | LeastSquares 87 | RamsayE 88 | RobustNorm 89 | TrimmedMean 90 | TukeyBiweight 91 | estimate_location 92 | 93 | 94 | Scale 95 | ^^^^^ 96 | 97 | .. module:: statsmodels.robust.scale 98 | .. currentmodule:: statsmodels.robust.scale 99 | 100 | .. autosummary:: 101 | :toctree: generated/ 102 | 103 | Huber 104 | HuberScale 105 | mad 106 | hubers_scale 107 | -------------------------------------------------------------------------------- /docs/zh/source/rlm_techn1.rst: -------------------------------------------------------------------------------- 1 | .. module:: statsmodels.rlm 2 | :synopsis: Outlier robust linear models 3 | 4 | .. currentmodule:: statsmodels.rlm 5 | 6 | 7 | .. _rlm_techn1: 8 | 9 | Weight Functions 10 | ---------------- 11 | 12 | Andrew's Wave 13 | 14 | .. image:: images/aw.png 15 | 16 | Hampel 17A 17 | 18 | .. image:: images/hl.png 19 | 20 | Huber's t 21 | 22 | .. image:: images/ht.png 23 | 24 | Least Squares 25 | 26 | .. image:: images/ls.png 27 | 28 | Ramsay's Ea 29 | 30 | .. image:: images/re.png 31 | 32 | Trimmed Mean 33 | 34 | .. image:: images/tm.png 35 | 36 | Tukey's Biweight 37 | 38 | .. image:: images/tk.png 39 | 40 | 41 | -------------------------------------------------------------------------------- /docs/zh/source/tsastats.rst.TXT: -------------------------------------------------------------------------------- 1 | .. currentmodule:: statsmodels.tsa.tsatools 2 | 3 | Time Series Analysis 4 | ==================== 5 | 6 | These are some of the helper functions for doing time series analysis. First 7 | we can load some a some data from the US Macro Economy 1959:Q1 - 2009:Q3. :: 8 | 9 | >>> data = sm.datasets.macrodata.load(as_pandas=False) 10 | 11 | The macro dataset is a structured array. :: 12 | 13 | >>> data = data.data[['year','quarter','realgdp','tbilrate','cpi','unemp']] 14 | 15 | We can add a lag like so :: 16 | 17 | >>> data = sm.tsa.add_lag(data, 'realgdp', lags=2) 18 | 19 | TODO: 20 | -scikits.timeseries 21 | -link in to var docs 22 | -------------------------------------------------------------------------------- /docs/zh/sphinxext/MANIFEST.in: -------------------------------------------------------------------------------- 1 | recursive-include tests *.py 2 | include *.txt 3 | -------------------------------------------------------------------------------- /docs/zh/sphinxext/README.txt: -------------------------------------------------------------------------------- 1 | ===================================== 2 | numpydoc -- Numpy's Sphinx extensions 3 | ===================================== 4 | 5 | Numpy's documentation uses several custom extensions to Sphinx. These 6 | are shipped in this ``numpydoc`` package, in case you want to make use 7 | of them in third-party projects. 8 | 9 | The following extensions are available: 10 | 11 | - ``numpydoc``: support for the Numpy docstring format in Sphinx, and add 12 | the code description directives ``np-function``, ``np-cfunction``, etc. 13 | that support the Numpy docstring syntax. 14 | 15 | - ``numpydoc.traitsdoc``: For gathering documentation about Traits attributes. 16 | 17 | - ``numpydoc.plot_directives``: Adaptation of Matplotlib's ``plot::`` 18 | directive. Note that this implementation may still undergo severe 19 | changes or eventually be deprecated. 20 | 21 | - ``numpydoc.only_directives``: (DEPRECATED) 22 | 23 | - ``numpydoc.autosummary``: (DEPRECATED) An ``autosummary::`` directive. 24 | Available in Sphinx 0.6.2 and (to-be) 1.0 as ``sphinx.ext.autosummary``, 25 | and it the Sphinx 1.0 version is recommended over that included in 26 | Numpydoc. 27 | -------------------------------------------------------------------------------- /docs/zh/sphinxext/numpy_ext/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/zh/sphinxext/numpy_ext/__init__.py -------------------------------------------------------------------------------- /docs/zh/themes/statsmodels/indexsidebar.html: -------------------------------------------------------------------------------- 1 |

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3 | -------------------------------------------------------------------------------- /docs/zh/themes/statsmodels/static/statsmodels_hybi_banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/docs/zh/themes/statsmodels/static/statsmodels_hybi_banner.png -------------------------------------------------------------------------------- /docs/zh/themes/statsmodels/theme.conf: -------------------------------------------------------------------------------- 1 | [theme] 2 | inherit = basic 3 | stylesheet = nature.css 4 | pygments_style = tango 5 | -------------------------------------------------------------------------------- /examples/incomplete/arima.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | from statsmodels.datasets.macrodata import load_pandas 3 | from statsmodels.tsa.base.datetools import dates_from_range 4 | from statsmodels.tsa.arima_model import ARIMA 5 | import matplotlib.pyplot as plt 6 | import numpy as np 7 | import statsmodels.api as sm 8 | plt.interactive(False) 9 | 10 | # let's examine an ARIMA model of CPI 11 | 12 | cpi = load_pandas().data['cpi'] 13 | dates = dates_from_range('1959q1', '2009q3') 14 | cpi.index = dates 15 | 16 | res = ARIMA(cpi, (1, 1, 1), freq='Q').fit() 17 | print(res.summary()) 18 | 19 | # we can look at the series 20 | cpi.diff().plot() 21 | 22 | # maybe logs are better 23 | log_cpi = np.log(cpi) 24 | 25 | # check the ACF and PCF plots 26 | acf, confint_acf = sm.tsa.acf(log_cpi.diff().values[1:], confint=95) 27 | # center the confidence intervals about zero 28 | #confint_acf -= confint_acf.mean(1)[:, None] 29 | pacf = sm.tsa.pacf(log_cpi.diff().values[1:], method='ols') 30 | # confidence interval is now an option to pacf 31 | from scipy import stats 32 | confint_pacf = stats.norm.ppf(1 - .025) * np.sqrt(1 / 202.) 33 | 34 | fig = plt.figure() 35 | ax = fig.add_subplot(121) 36 | ax.set_title('Autocorrelation') 37 | ax.plot(range(41), acf, 'bo', markersize=5) 38 | ax.vlines(range(41), 0, acf) 39 | ax.fill_between(range(41), confint_acf[:, 0], confint_acf[:, 1], alpha=.25) 40 | fig.tight_layout() 41 | ax = fig.add_subplot(122, sharey=ax) 42 | ax.vlines(range(41), 0, pacf) 43 | ax.plot(range(41), pacf, 'bo', markersize=5) 44 | ax.fill_between(range(41), -confint_pacf, confint_pacf, alpha=.25) 45 | 46 | 47 | #NOTE: you'll be able to just to this when tsa-plots is in master 48 | #sm.graphics.acf_plot(x, nlags=40) 49 | #sm.graphics.pacf_plot(x, nlags=40) 50 | 51 | 52 | # still some seasonality 53 | # try an arma(1, 1) with ma(4) term 54 | -------------------------------------------------------------------------------- /examples/incomplete/arma2.py: -------------------------------------------------------------------------------- 1 | """ 2 | Autoregressive Moving Average (ARMA) Model 3 | """ 4 | import numpy as np 5 | import statsmodels.api as sm 6 | 7 | # Generate some data from an ARMA process 8 | from statsmodels.tsa.arima_process import arma_generate_sample 9 | 10 | np.random.seed(12345) 11 | arparams = np.array([.75, -.25]) 12 | maparams = np.array([.65, .35]) 13 | 14 | # The conventions of the arma_generate function require that we specify a 15 | # 1 for the zero-lag of the AR and MA parameters and that the AR parameters 16 | # be negated. 17 | ar = np.r_[1, -arparams] 18 | ma = np.r_[1, maparams] 19 | nobs = 250 20 | y = arma_generate_sample(ar, ma, nobs) 21 | 22 | # Now, optionally, we can add some dates information. For this example, 23 | # we'll use a pandas time series. 24 | import pandas as pd 25 | dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs) 26 | y = pd.Series(y, index=dates) 27 | arma_mod = sm.tsa.ARMA(y, order=(2, 2)) 28 | arma_res = arma_mod.fit(trend='nc', disp=-1) 29 | -------------------------------------------------------------------------------- /examples/incomplete/dates.py: -------------------------------------------------------------------------------- 1 | """ 2 | Using dates with timeseries models 3 | """ 4 | import statsmodels.api as sm 5 | import pandas as pd 6 | 7 | # Getting started 8 | # --------------- 9 | 10 | data = sm.datasets.sunspots.load() 11 | 12 | # Right now an annual date series must be datetimes at the end of the year. 13 | 14 | dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog)) 15 | 16 | # Using Pandas 17 | # ------------ 18 | 19 | # Make a pandas Series or DataFrame with DatetimeIndex 20 | endog = pd.Series(data.endog, index=dates) 21 | 22 | # and instantiate the model 23 | ar_model = sm.tsa.AR(endog, freq='A') 24 | pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1) 25 | 26 | # Let's do some out-of-sample prediction 27 | pred = pandas_ar_res.predict(start='2005', end='2015') 28 | print(pred) 29 | 30 | # Using explicit dates 31 | # -------------------- 32 | 33 | ar_model = sm.tsa.AR(data.endog, dates=dates, freq='A') 34 | ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1) 35 | pred = ar_res.predict(start='2005', end='2015') 36 | print(pred) 37 | 38 | # This just returns a regular array, but since the model has date information 39 | # attached, you can get the prediction dates in a roundabout way. 40 | 41 | print(ar_res.data.predict_dates) 42 | 43 | # This attribute only exists if predict has been called. It holds the dates 44 | # associated with the last call to predict. 45 | #..TODO: should this be attached to the results instance? 46 | -------------------------------------------------------------------------------- /examples/incomplete/ols_table.py: -------------------------------------------------------------------------------- 1 | """Example: statsmodels.OLS 2 | """ 3 | 4 | from statsmodels.datasets.longley import load 5 | import statsmodels.api as sm 6 | from statsmodels.iolib.table import SimpleTable, default_txt_fmt 7 | import numpy as np 8 | 9 | data = load() 10 | 11 | data_orig = (data.endog.copy(), data.exog.copy()) 12 | 13 | #.. Note: In this example using zscored/standardized variables has no effect on 14 | #.. regression estimates. Are there no numerical problems? 15 | 16 | rescale = 0 17 | #0: no rescaling, 1:demean, 2:standardize, 3:standardize and transform back 18 | rescale_ratio = data.endog.std() / data.exog.std(0) 19 | if rescale > 0: 20 | # rescaling 21 | data.endog -= data.endog.mean() 22 | data.exog -= data.exog.mean(0) 23 | if rescale > 1: 24 | data.endog /= data.endog.std() 25 | data.exog /= data.exog.std(0) 26 | 27 | #skip because mean has been removed, but dimension is hardcoded in table 28 | data.exog = sm.tools.add_constant(data.exog, prepend=False) 29 | 30 | 31 | ols_model = sm.OLS(data.endog, data.exog) 32 | ols_results = ols_model.fit() 33 | 34 | # the Longley dataset is well known to have high multicollinearity 35 | # one way to find the condition number is as follows 36 | 37 | 38 | #Find OLS parameters for model with one explanatory variable dropped 39 | 40 | resparams = np.nan * np.ones((7, 7)) 41 | res = sm.OLS(data.endog, data.exog).fit() 42 | resparams[:, 0] = res.params 43 | 44 | indall = range(7) 45 | for i in range(6): 46 | ind = indall[:] 47 | del ind[i] 48 | res = sm.OLS(data.endog, data.exog[:, ind]).fit() 49 | resparams[ind, i + 1] = res.params 50 | 51 | if rescale == 1: 52 | pass 53 | if rescale == 3: 54 | resparams[:-1, :] *= rescale_ratio[:, None] 55 | 56 | txt_fmt1 = default_txt_fmt 57 | numformat = '%10.4f' 58 | txt_fmt1 = dict(data_fmts=[numformat]) 59 | rowstubs = data.names[1:] + ['const'] 60 | headers = ['all'] + ['drop %s' % name for name in data.names[1:]] 61 | tabl = SimpleTable(resparams, headers, rowstubs, txt_fmt=txt_fmt1) 62 | 63 | nanstring = numformat % np.nan 64 | nn = len(nanstring) 65 | nanrep = ' ' * (nn - 1) 66 | nanrep = nanrep[:nn // 2] + '-' + nanrep[nn // 2:] 67 | 68 | print('Longley data - sensitivity to dropping an explanatory variable') 69 | print(str(tabl).replace(nanstring, nanrep)) 70 | -------------------------------------------------------------------------------- /examples/notebooks/categorical_interaction_plot.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Plot Interaction of Categorical Factors" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "In this example, we will vizualize the interaction between categorical factors. First, we will create some categorical data are initialized. Then plotted using the interaction_plot function which internally recodes the x-factor categories to ingegers." 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": { 21 | "collapsed": false 22 | }, 23 | "outputs": [], 24 | "source": [ 25 | "%matplotlib inline\n", 26 | "\n", 27 | "import numpy as np\n", 28 | "import matplotlib.pyplot as plt\n", 29 | "import pandas as pd\n", 30 | "from statsmodels.graphics.factorplots import interaction_plot" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": null, 36 | "metadata": { 37 | "collapsed": false 38 | }, 39 | "outputs": [], 40 | "source": [ 41 | "np.random.seed(12345)\n", 42 | "weight = pd.Series(np.repeat(['low', 'hi', 'low', 'hi'], 15), name='weight')\n", 43 | "nutrition = pd.Series(np.repeat(['lo_carb', 'hi_carb'], 30), name='nutrition')\n", 44 | "days = np.log(np.random.randint(1, 30, size=60))" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "metadata": { 51 | "collapsed": false 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "fig, ax = plt.subplots(figsize=(6, 6))\n", 56 | "fig = interaction_plot(x=weight, trace=nutrition, response=days, \n", 57 | " colors=['red', 'blue'], markers=['D', '^'], ms=10, ax=ax)" 58 | ] 59 | } 60 | ], 61 | "metadata": { 62 | "kernelspec": { 63 | "display_name": "Python 3", 64 | "language": "python", 65 | "name": "python3" 66 | }, 67 | "language_info": { 68 | "codemirror_mode": { 69 | "name": "ipython", 70 | "version": 3 71 | }, 72 | "file_extension": ".py", 73 | "mimetype": "text/x-python", 74 | "name": "python", 75 | "nbconvert_exporter": "python", 76 | "pygments_lexer": "ipython3", 77 | "version": "3.4.3" 78 | } 79 | }, 80 | "nbformat": 4, 81 | "nbformat_minor": 0 82 | } 83 | -------------------------------------------------------------------------------- /examples/notebooks/star_diagram.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/statsmodels-doc-zh/d3bfee2849fb022eab0464341ab430e212f87515/examples/notebooks/star_diagram.png -------------------------------------------------------------------------------- /examples/python/categorical_interaction_plot.py: -------------------------------------------------------------------------------- 1 | 2 | ## Plot Interaction of Categorical Factors 3 | 4 | # In this example, we will vizualize the interaction between categorical factors. First, we will create some categorical data are initialized. Then plotted using the interaction_plot function which internally recodes the x-factor categories to ingegers. 5 | 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | from statsmodels.graphics.factorplots import interaction_plot 9 | from pandas import Series 10 | np.random.seed(12345) 11 | weight = Series(np.repeat(['low', 'hi', 'low', 'hi'], 15), name='weight') 12 | nutrition = Series(np.repeat(['lo_carb', 'hi_carb'], 30), name='nutrition') 13 | days = np.log(np.random.randint(1, 30, size=60)) 14 | plt.figure(figsize=(6, 6)); 15 | interaction_plot(x=weight, trace=nutrition, response=days, 16 | colors=['red', 'blue'], markers=['D', '^'], ms=10) 17 | 18 | 19 | # 20 | 21 | # image file: 22 | 23 | # image file: 24 | -------------------------------------------------------------------------------- /examples/python/glm_formula.py: -------------------------------------------------------------------------------- 1 | 2 | ## Generalized Linear Models (Formula) 3 | 4 | # This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models. 5 | # 6 | # To begin, we load the ``Star98`` dataset and we construct a formula and pre-process the data: 7 | 8 | from __future__ import print_function 9 | import statsmodels.api as sm 10 | import statsmodels.formula.api as smf 11 | star98 = sm.datasets.star98.load_pandas().data 12 | formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF' 13 | dta = star98[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP', 14 | 'PCTCHRT', 'PCTYRRND', 'PERMINTE', 'AVYRSEXP', 'AVSALK', 15 | 'PERSPENK', 'PTRATIO', 'PCTAF']] 16 | endog = dta['NABOVE'] / (dta['NABOVE'] + dta.pop('NBELOW')) 17 | del dta['NABOVE'] 18 | dta['SUCCESS'] = endog 19 | 20 | 21 | # Then, we fit the GLM model: 22 | 23 | mod1 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit() 24 | mod1.summary() 25 | 26 | 27 | # Finally, we define a function to operate customized data transformation using the formula framework: 28 | 29 | def double_it(x): 30 | return 2 * x 31 | 32 | 33 | formula = 'SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + PCTCHRT + PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF' 34 | mod2 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit() 35 | mod2.summary() 36 | 37 | 38 | # As expected, the coefficient for ``double_it(LOWINC)`` in the second model is half the size of the ``LOWINC`` coefficient from the first model: 39 | 40 | print(mod1.params[1]) 41 | print(mod2.params[1] * 2) 42 | -------------------------------------------------------------------------------- /examples/python/kernel_density.py: -------------------------------------------------------------------------------- 1 | 2 | ## Kernel Density Estimation 3 | 4 | import numpy as np 5 | from scipy import stats 6 | import statsmodels.api as sm 7 | import matplotlib.pyplot as plt 8 | from statsmodels.distributions.mixture_rvs import mixture_rvs 9 | 10 | 11 | ##### A univariate example. 12 | 13 | np.random.seed(12345) 14 | 15 | 16 | obs_dist1 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.norm], 17 | kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5))) 18 | 19 | 20 | kde = sm.nonparametric.KDEUnivariate(obs_dist1) 21 | kde.fit() 22 | 23 | 24 | fig = plt.figure(figsize=(12,8)) 25 | ax = fig.add_subplot(111) 26 | ax.hist(obs_dist1, bins=50, normed=True, color='red') 27 | ax.plot(kde.support, kde.density, lw=2, color='black'); 28 | 29 | 30 | obs_dist2 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.beta], 31 | kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=1,args=(1,.5)))) 32 | 33 | kde2 = sm.nonparametric.KDEUnivariate(obs_dist2) 34 | kde2.fit() 35 | 36 | 37 | fig = plt.figure(figsize=(12,8)) 38 | ax = fig.add_subplot(111) 39 | ax.hist(obs_dist2, bins=50, normed=True, color='red') 40 | ax.plot(kde2.support, kde2.density, lw=2, color='black'); 41 | 42 | 43 | # The fitted KDE object is a full non-parametric distribution. 44 | 45 | obs_dist3 = mixture_rvs([.25,.75], size=1000, dist=[stats.norm, stats.norm], 46 | kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5))) 47 | kde3 = sm.nonparametric.KDEUnivariate(obs_dist3) 48 | kde3.fit() 49 | 50 | 51 | kde3.entropy 52 | 53 | 54 | kde3.evaluate(-1) 55 | 56 | 57 | ##### CDF 58 | 59 | fig = plt.figure(figsize=(12,8)) 60 | ax = fig.add_subplot(111) 61 | ax.plot(kde3.support, kde3.cdf); 62 | 63 | 64 | ##### Cumulative Hazard Function 65 | 66 | fig = plt.figure(figsize=(12,8)) 67 | ax = fig.add_subplot(111) 68 | ax.plot(kde3.support, kde3.cumhazard); 69 | 70 | 71 | ##### Inverse CDF 72 | 73 | fig = plt.figure(figsize=(12,8)) 74 | ax = fig.add_subplot(111) 75 | ax.plot(kde3.support, kde3.icdf); 76 | 77 | 78 | ##### Survival Function 79 | 80 | fig = plt.figure(figsize=(12,8)) 81 | ax = fig.add_subplot(111) 82 | ax.plot(kde3.support, kde3.sf); 83 | -------------------------------------------------------------------------------- /examples/python/predict.py: -------------------------------------------------------------------------------- 1 | 2 | ## Prediction (out of sample) 3 | 4 | from __future__ import print_function 5 | import numpy as np 6 | import statsmodels.api as sm 7 | 8 | 9 | # ## Artificial data 10 | 11 | nsample = 50 12 | sig = 0.25 13 | x1 = np.linspace(0, 20, nsample) 14 | X = np.column_stack((x1, np.sin(x1), (x1-5)**2)) 15 | X = sm.add_constant(X) 16 | beta = [5., 0.5, 0.5, -0.02] 17 | y_true = np.dot(X, beta) 18 | y = y_true + sig * np.random.normal(size=nsample) 19 | 20 | 21 | # ## Estimation 22 | 23 | olsmod = sm.OLS(y, X) 24 | olsres = olsmod.fit() 25 | print(olsres.summary()) 26 | 27 | 28 | # ## In-sample prediction 29 | 30 | ypred = olsres.predict(X) 31 | print(ypred) 32 | 33 | 34 | # ## Create a new sample of explanatory variables Xnew, predict and plot 35 | 36 | x1n = np.linspace(20.5,25, 10) 37 | Xnew = np.column_stack((x1n, np.sin(x1n), (x1n-5)**2)) 38 | Xnew = sm.add_constant(Xnew) 39 | ynewpred = olsres.predict(Xnew) # predict out of sample 40 | print(ynewpred) 41 | 42 | 43 | # ## Plot comparison 44 | 45 | import matplotlib.pyplot as plt 46 | 47 | fig, ax = plt.subplots() 48 | ax.plot(x1, y, 'o', label="Data") 49 | ax.plot(x1, y_true, 'b-', label="True") 50 | ax.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), 'r', label="OLS prediction") 51 | ax.legend(loc="best"); 52 | 53 | 54 | ### Predicting with Formulas 55 | 56 | # Using formulas can make both estimation and prediction a lot easier 57 | 58 | from statsmodels.formula.api import ols 59 | 60 | data = {"x1" : x1, "y" : y} 61 | 62 | res = ols("y ~ x1 + np.sin(x1) + I((x1-5)**2)", data=data).fit() 63 | 64 | 65 | # We use the `I` to indicate use of the Identity transform. Ie., we don't want any expansion magic from using `**2` 66 | 67 | res.params 68 | 69 | 70 | # Now we only have to pass the single variable and we get the transformed right-hand side variables automatically 71 | 72 | res.predict(exog=dict(x1=x1n)) 73 | -------------------------------------------------------------------------------- /examples/python/tsa_arma_1.py: -------------------------------------------------------------------------------- 1 | 2 | ## Autoregressive Moving Average (ARMA): Artificial data 3 | 4 | from __future__ import print_function 5 | import numpy as np 6 | import statsmodels.api as sm 7 | from statsmodels.tsa.arima_process import arma_generate_sample 8 | np.random.seed(12345) 9 | 10 | 11 | # Generate some data from an ARMA process: 12 | 13 | arparams = np.array([.75, -.25]) 14 | maparams = np.array([.65, .35]) 15 | 16 | 17 | # The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated. 18 | 19 | arparams = np.r_[1, -arparams] 20 | maparam = np.r_[1, maparams] 21 | nobs = 250 22 | y = arma_generate_sample(arparams, maparams, nobs) 23 | 24 | 25 | # Now, optionally, we can add some dates information. For this example, we'll use a pandas time series. 26 | 27 | import pandas as pd 28 | dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs) 29 | y = pd.Series(y, index=dates) 30 | arma_mod = sm.tsa.ARMA(y, order=(2,2)) 31 | arma_res = arma_mod.fit(trend='nc', disp=-1) 32 | -------------------------------------------------------------------------------- /examples/python/tsa_dates.py: -------------------------------------------------------------------------------- 1 | 2 | ## Dates in timeseries models 3 | 4 | from __future__ import print_function 5 | import statsmodels.api as sm 6 | import pandas as pd 7 | 8 | 9 | # ## Getting started 10 | 11 | data = sm.datasets.sunspots.load() 12 | 13 | 14 | # Right now an annual date series must be datetimes at the end of the year. 15 | 16 | dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog)) 17 | 18 | 19 | # ## Using Pandas 20 | # 21 | # Make a pandas Series or DataFrame with DatetimeIndex 22 | 23 | endog = pd.Series(data.endog, index=dates) 24 | 25 | 26 | # Instantiate the model 27 | 28 | ar_model = sm.tsa.AR(endog, freq='A') 29 | pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1) 30 | 31 | 32 | # Out-of-sample prediction 33 | 34 | pred = pandas_ar_res.predict(start='2005', end='2015') 35 | print(pred) 36 | 37 | 38 | # ## Using explicit dates 39 | 40 | ar_model = sm.tsa.AR(data.endog, dates=dates, freq='A') 41 | ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1) 42 | pred = ar_res.predict(start='2005', end='2015') 43 | print(pred) 44 | 45 | 46 | # This just returns a regular array, but since the model has date information attached, you can get the prediction dates in a roundabout way. 47 | 48 | print(ar_res.data.predict_dates) 49 | 50 | 51 | # Note: This attribute only exists if predict has been called. It holds the dates associated with the last call to predict. 52 | -------------------------------------------------------------------------------- /examples/run_all.py: -------------------------------------------------------------------------------- 1 | """run all examples to make sure we don't get an exception 2 | 3 | Note: 4 | If an example contaings plt.show(), then all plot windows have to be closed 5 | manually, at least in my setup. 6 | 7 | uncomment plt.show() to show all plot windows 8 | 9 | """ 10 | from __future__ import print_function 11 | from statsmodels.compat import input 12 | stop_on_error = True 13 | 14 | 15 | filelist = ['example_glsar.py', 'example_wls.py', 'example_gls.py', 16 | 'example_glm.py', 'example_ols_tftest.py', # 'example_rpy.py', 17 | 'example_ols.py', 'example_rlm.py', 18 | 'example_discrete.py', 'example_predict.py', 19 | 'example_ols_table.py', 20 | # time series 21 | 'tsa/ex_arma2.py', 'tsa/ex_dates.py'] 22 | 23 | if __name__ == '__main__': 24 | #temporarily disable show 25 | import matplotlib.pyplot as plt 26 | plt_show = plt.show 27 | 28 | def noop(*args): 29 | pass 30 | 31 | plt.show = noop 32 | 33 | msg = """Are you sure you want to run all of the examples? 34 | This is done mainly to check that they are up to date. 35 | (y/n) >>> """ 36 | 37 | cont = input(msg) 38 | if 'y' in cont.lower(): 39 | for run_all_f in filelist: 40 | try: 41 | print('\n\nExecuting example file', run_all_f) 42 | print('-----------------------' + '-' * len(run_all_f)) 43 | exec(open(run_all_f).read()) 44 | except: 45 | # f might be overwritten in the executed file 46 | print('**********************' + '*' * len(run_all_f)) 47 | print('ERROR in example file', run_all_f) 48 | print('**********************' + '*' * len(run_all_f)) 49 | if stop_on_error: 50 | raise 51 | 52 | # reenable show after closing windows 53 | plt.close('all') 54 | plt.show = plt_show 55 | plt.show() 56 | --------------------------------------------------------------------------------