8 | Vignettes and other documentation
9 |
11 |
12 |
17 |
39 |
40 |
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/revdep/library/nestedLogit/old/nestedLogit/help/AnIndex:
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1 | Anova.nestedLogit nestedHypotheses
2 | anova.nestedLogit nestedHypotheses
3 | as.character.dichotomies nestedMethods
4 | as.data.frame.predictNestedLogit as.data.frame.predictNestedLogit
5 | as.dichotomies nestedMethods
6 | as.dichotomies.matrix nestedMethods
7 | as.matrix.continuationDichotomies nestedMethods
8 | as.matrix.dichotomies nestedMethods
9 | broomMethods broomMethods
10 | coef.nestedLogit nestedMethods
11 | confint.predictNestedLogit nestedMethods
12 | continuationLogits nestedLogit
13 | dichotomy nestedLogit
14 | fitted.nestedLogit nestedMethods
15 | glance.nestedLogit broomMethods
16 | GSS GSS
17 | HealthInsurance HealthInsurance
18 | linearHypothesis.nestedLogit nestedHypotheses
19 | logits nestedLogit
20 | logLik.nestedLogit nestedHypotheses
21 | models models
22 | models.nestedLogit models
23 | nestedHypotheses nestedHypotheses
24 | nestedLogit nestedLogit
25 | nestedMethods nestedMethods
26 | plot.nestedLogit plot.nestedLogit
27 | predict.nestedLogit nestedMethods
28 | print.Anova.nestedLogit nestedHypotheses
29 | print.anova.nestedLogit nestedHypotheses
30 | print.dichotomies nestedMethods
31 | print.nestedLogit nestedMethods
32 | print.predictDichotomies nestedMethods
33 | print.predictNestedLogit nestedMethods
34 | print.summary.nestedLogit nestedMethods
35 | summary.nestedLogit nestedMethods
36 | tidy.nestedLogit broomMethods
37 | update.nestedLogit nestedMethods
38 | vcov.nestedLogit nestedMethods
39 |
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/revdep/library/nestedLogit/old/nestedLogit/html/R.css:
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1 | @media screen {
2 | .container {
3 | padding-right: 10px;
4 | padding-left: 10px;
5 | margin-right: auto;
6 | margin-left: auto;
7 | max-width: 900px;
8 | }
9 | }
10 |
11 | .rimage img { /* from knitr - for examples and demos */
12 | width: 96%;
13 | margin-left: 2%;
14 | }
15 |
16 | .katex { font-size: 1.1em; }
17 |
18 | code {
19 | color: inherit;
20 | background: inherit;
21 | }
22 |
23 | body {
24 | line-height: 1.4;
25 | background: white;
26 | color: black;
27 | }
28 |
29 | a:link {
30 | background: white;
31 | color: blue;
32 | }
33 |
34 | a:visited {
35 | background: white;
36 | color: rgb(50%, 0%, 50%);
37 | }
38 |
39 | h1 {
40 | background: white;
41 | color: rgb(55%, 55%, 55%);
42 | font-family: monospace;
43 | font-size: 1.4em; /* x-large; */
44 | text-align: center;
45 | }
46 |
47 | h2 {
48 | background: white;
49 | color: rgb(40%, 40%, 40%);
50 | font-family: monospace;
51 | font-size: 1.2em; /* large; */
52 | text-align: center;
53 | }
54 |
55 | h3 {
56 | background: white;
57 | color: rgb(40%, 40%, 40%);
58 | font-family: monospace;
59 | font-size: 1.2em; /* large; */
60 | }
61 |
62 | h4 {
63 | background: white;
64 | color: rgb(40%, 40%, 40%);
65 | font-family: monospace;
66 | font-style: italic;
67 | font-size: 1.2em; /* large; */
68 | }
69 |
70 | h5 {
71 | background: white;
72 | color: rgb(40%, 40%, 40%);
73 | font-family: monospace;
74 | }
75 |
76 | h6 {
77 | background: white;
78 | color: rgb(40%, 40%, 40%);
79 | font-family: monospace;
80 | font-style: italic;
81 | }
82 |
83 | img.toplogo {
84 | width: 4em;
85 | vertical-align: middle;
86 | }
87 |
88 | img.arrow {
89 | width: 30px;
90 | height: 30px;
91 | border: 0;
92 | }
93 |
94 | span.acronym {
95 | font-size: small;
96 | }
97 |
98 | span.env {
99 | font-family: monospace;
100 | }
101 |
102 | span.file {
103 | font-family: monospace;
104 | }
105 |
106 | span.option{
107 | font-family: monospace;
108 | }
109 |
110 | span.pkg {
111 | font-weight: bold;
112 | }
113 |
114 | span.samp{
115 | font-family: monospace;
116 | }
117 |
118 | div.vignettes a:hover {
119 | background: rgb(85%, 85%, 85%);
120 | }
121 |
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/revdep/problems.md:
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1 | *Wow, no problems at all. :)*
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/tests/spelling.R:
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1 | if(requireNamespace('spelling', quietly = TRUE))
2 | spelling::spell_check_test(vignettes = TRUE, error = FALSE,
3 | skip_on_cran = TRUE)
4 |
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/tests/testthat.R:
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1 | library(testthat)
2 | library(nestedLogit)
3 |
4 | test_check("nestedLogit")
5 |
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/tests/testthat/test-Effect-method.R:
--------------------------------------------------------------------------------
1 | # test that Effect.nestedLogit() correctly computes effects and their SEs
2 |
3 | data(Womenlf, package = "carData")
4 |
5 | m <- nestedLogit(partic ~ hincome + children,
6 | logits(work=dichotomy("not.work", c("parttime", "fulltime")),
7 | full=dichotomy("parttime", "fulltime")),
8 | data=Womenlf)
9 |
10 | eff.1 <- Effect("children", m)
11 | new <- eff.1$x
12 | new$hincome <- mean(Womenlf$hincome)
13 | pred.1 <- predict(m, newdata=new)
14 | test_that("Effects are computed correctly for a numeric predictor", {
15 | expect_equal(as.vector(eff.1$prob),
16 | as.vector(as.matrix(pred.1$p)))
17 | })
18 | test_that("Effect SEs are computed correctly for a numeric predictor", {
19 | expect_equal(as.vector(eff.1$se.prob),
20 | as.vector(as.matrix(pred.1$se.p)))
21 | })
22 |
23 | Womenlf$kids <- with(Womenlf, ifelse(children == "present", 1, 0))
24 | mm <- update(m, . ~ . - children + kids, data=Womenlf)
25 | eff.2 <- Effect("hincome", m)
26 | new <- eff.2$x
27 | new$kids <- mean(Womenlf$kids)
28 | pred.2 <- predict(mm, newdata=new)
29 | test_that("Effects are computed correctly for a factor predictor", {
30 | expect_equal(as.vector(eff.2$prob),
31 | as.vector(as.matrix(pred.2$p)))
32 | })
33 | test_that("Effect SEs are computed correctly for a factor predictor", {
34 | expect_equal(as.vector(eff.2$se.prob),
35 | as.vector(as.matrix(pred.2$se.p)))
36 | })
37 |
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/tests/testthat/test-fitted-probs.R:
--------------------------------------------------------------------------------
1 | # test that nestedLogit()/predict.nestedLogit() correctly compute fitted probabilities
2 |
3 | m2 <- nestedLogit(degree ~ parentdeg + year,
4 | continuationLogits(c("l.t.highschool", "highschool",
5 | "college", "graduate")),
6 | data=GSS)
7 |
8 | new <- expand.grid(parentdeg=c("l.t.highschool", "highschool",
9 | "college", "graduate"),
10 | year=1972:2016)
11 | pred.GSS <- as.matrix(predict(m2, new)$p)
12 |
13 | p.m2.1 <- predict(models(m2, 1), newdata=new, type="response")
14 | p.m2.2 <- predict(models(m2, 2), newdata=new, type="response")
15 | p.m2.3 <- predict(models(m2, 3), newdata=new, type="response")
16 |
17 | test_that("fitted probabilities computed correctly, test 1", {
18 | expect_equal(1 - p.m2.1, pred.GSS[, "l.t.highschool"])
19 | })
20 |
21 | test_that("fitted probabilities computed correctly, test 2", {
22 | expect_equal(p.m2.1*(1 - p.m2.2), pred.GSS[, "highschool"])
23 | })
24 |
25 | test_that("fitted probabilities computed correctly, test 3", {
26 | expect_equal(p.m2.1*p.m2.2*(1 - p.m2.3), pred.GSS[, "college"])
27 | })
28 |
29 | test_that("fitted probabilities computed correctly, test 4", {
30 | expect_equal(p.m2.1*p.m2.2*p.m2.3, pred.GSS[, "graduate"])
31 | })
32 |
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/tests/testthat/test-model-update.R:
--------------------------------------------------------------------------------
1 | # test that update.nested() works properly
2 |
3 | data(Womenlf, package = "carData")
4 |
5 | m1 <- nestedLogit(partic ~ hincome,
6 | logits(work=dichotomy("not.work", c("parttime", "fulltime")),
7 | full=dichotomy("parttime", "fulltime")),
8 | data=Womenlf)
9 |
10 | m2 <- nestedLogit(partic ~ hincome + children,
11 | logits(work=dichotomy("not.work", c("parttime", "fulltime")),
12 | full=dichotomy("parttime", "fulltime")),
13 | data=Womenlf)
14 |
15 | m2a <- update(m1, . ~ . + children)
16 | test_that("update.nested() works correctly, formula arg", {
17 | expect_equal(coef(m2), coef(m2a))
18 | })
19 |
20 | Ontario <- Womenlf[Womenlf$region == "Ontario", ]
21 | m3 <- nestedLogit(partic ~ hincome + children,
22 | logits(work=dichotomy("not.work", c("parttime", "fulltime")),
23 | full=dichotomy("parttime", "fulltime")),
24 | data=Ontario)
25 | m3a <- update(m2, data=Ontario)
26 | test_that("update.nested() works correctly, data arg", {
27 | expect_equal(coef(m3), coef(m3a))
28 | })
29 |
30 | m4 <- nestedLogit(partic ~ hincome + children,
31 | logits(work=dichotomy("not.work", c("parttime", "fulltime")),
32 | full=dichotomy("parttime", "fulltime")),
33 | data=Womenlf,
34 | contrasts=list(children=contr.sum))
35 | m4a <- update(m2, contrasts=list(children=contr.sum))
36 | test_that("update.nested() works correctly, contrasts arg", {
37 | expect_equal(coef(m4), coef(m4a))
38 | })
39 |
40 | m5 <- nestedLogit(partic ~ hincome + children,
41 | logits(full=dichotomy(c("not.work", "parttime"), "fulltime"),
42 | part=dichotomy("not.work", "parttime")),
43 | data=Womenlf)
44 | m5a <- update(m2, dichotomies=logits(
45 | full=dichotomy(c("not.work", "parttime"), "fulltime"),
46 | part=dichotomy("not.work", "parttime")))
47 | test_that("update.nested() works correctly, dichotomies arg", {
48 | expect_equal(coef(m5), coef(m5a))
49 | })
50 |
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/tests/testthat/test-probs-SEs.R:
--------------------------------------------------------------------------------
1 | # test that predict.nestedLogit() correctly computes SEs of fitted probabilities
2 |
3 | m.gss <- nestedLogit(degree ~ parentdeg + year,
4 | continuationLogits(c("l.t.highschool", "highschool",
5 | "college", "graduate")),
6 | data=GSS)
7 |
8 | new <- expand.grid(parentdeg=c("l.t.highschool", "highschool",
9 | "college", "graduate"),
10 | year=seq(1972, 2016, length=5))
11 | pred.gss <- predict(m.gss, new)
12 |
13 | GSS$y1 <- with(GSS, ifelse(degree == "l.t.highschool", 0, 1))
14 | GSS$y2 <- with(GSS, ifelse(y1 == 0, NA,
15 | ifelse(degree == "highschool", 0, 1)))
16 | GSS$y3 <- with(GSS, ifelse(degree == "college", 0,
17 | ifelse(degree == "graduate", 1, NA)))
18 | m1 <- glm(y1 ~ parentdeg + year, data=GSS, family=binomial)
19 | m2 <- glm(y2 ~ parentdeg + year, data=GSS, family=binomial)
20 | m3 <- glm(y3 ~ parentdeg + year, data=GSS, family=binomial)
21 |
22 | pr1 <- predict(m1, newdata=new, type="response", se.fit=TRUE)
23 | p1 <- pr1$fit
24 | v1 <- (pr1$se.fit)^2
25 |
26 | pr2 <- predict(m2, newdata=new, type="response", se.fit=TRUE)
27 | p2 <- pr2$fit
28 | v2 <- (pr2$se.fit)^2
29 |
30 | pr3 <- predict(m3, newdata=new, type="response", se.fit=TRUE)
31 | p3 <- pr3$fit
32 | v3 <- (pr3$se.fit)^2
33 |
34 | se.p.a <- sqrt(v1)
35 | se.p.b <- sqrt((1 - p2)^2*v1 + p1^2*v2)
36 | se.p.c <- sqrt((p2*(1 - p3))^2*v1 + (p1*(1 - p3))^2*v2 + (p1*p2)^2*v3)
37 | se.p.d <- sqrt((p2*p3)^2*v1 + (p1*p3)^2*v2 + (p1*p2)^2*v3)
38 |
39 | test_that("SEs of fitted probabilities computed correctly", {
40 | expect_equal(as.vector(cbind(se.p.a, se.p.b, se.p.c, se.p.d)),
41 | as.vector(as.matrix(pred.gss$se.p)))
42 | })
43 |
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2 | *.R
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1 | @Manual{R-base,
2 | title = {R: A Language and Environment for Statistical Computing},
3 | author = {{R Core Team}},
4 | organization = {R Foundation for Statistical Computing},
5 | address = {Vienna, Austria},
6 | year = {2023},
7 | url = {https://www.R-project.org/},
8 | }
9 |
10 | @Manual{R-broom,
11 | title = {broom: Convert Statistical Objects into Tidy Tibbles},
12 | author = {David Robinson and Alex Hayes and Simon Couch},
13 | year = {2023},
14 | note = {R package version 1.0.5},
15 | url = {https://CRAN.R-project.org/package=broom},
16 | }
17 |
18 | @Manual{R-car,
19 | title = {car: Companion to Applied Regression},
20 | author = {John Fox and Sanford Weisberg and Brad Price},
21 | year = {2023},
22 | note = {R package version 3.1-2},
23 | url = {https://CRAN.R-project.org/package=car},
24 | }
25 |
26 | @Manual{R-carData,
27 | title = {carData: Companion to Applied Regression Data Sets},
28 | author = {John Fox and Sanford Weisberg and Brad Price},
29 | year = {2022},
30 | note = {R package version 3.0-5},
31 | url = {https://CRAN.R-project.org/package=carData},
32 | }
33 |
34 | @Manual{R-dplyr,
35 | title = {dplyr: A Grammar of Data Manipulation},
36 | author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller and Davis Vaughan},
37 | year = {2023},
38 | note = {R package version 1.1.2},
39 | url = {https://CRAN.R-project.org/package=dplyr},
40 | }
41 |
42 | @Manual{R-effects,
43 | title = {effects: Effect Displays for Linear, Generalized Linear, and Other Models},
44 | author = {John Fox and Sanford Weisberg and Brad Price and Michael Friendly and Jangman Hong},
45 | year = {2022},
46 | note = {R package version 4.2-2},
47 | url = {https://CRAN.R-project.org/package=effects},
48 | }
49 |
50 | @Manual{R-geomtextpath,
51 | title = {geomtextpath: Curved Text in ggplot2},
52 | author = {Allan Cameron and Teun {van den Brand}},
53 | year = {2022},
54 | note = {R package version 0.1.1},
55 | url = {https://allancameron.github.io/geomtextpath/},
56 | }
57 |
58 | @Manual{R-ggeffects,
59 | title = {ggeffects: Create Tidy Data Frames of Marginal Effects for ggplot from
60 | Model Outputs},
61 | author = {Daniel Lüdecke},
62 | year = {2023},
63 | note = {R package version 1.2.3},
64 | url = {https://strengejacke.github.io/ggeffects/},
65 | }
66 |
67 | @Manual{R-ggplot2,
68 | title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics},
69 | author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo and Hiroaki Yutani and Dewey Dunnington},
70 | year = {2023},
71 | note = {R package version 3.4.2},
72 | url = {https://CRAN.R-project.org/package=ggplot2},
73 | }
74 |
75 | @Manual{R-knitr,
76 | title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
77 | author = {Yihui Xie},
78 | year = {2023},
79 | note = {R package version 1.43},
80 | url = {https://yihui.org/knitr/},
81 | }
82 |
83 | @Manual{R-nestedLogit,
84 | title = {nestedLogit: Nested Dichotomy Logistic Regression Models},
85 | author = {John Fox and Michael Friendly},
86 | year = {2023},
87 | note = {R package version 0.3.2},
88 | url = {https://github.com/friendly/nestedLogit},
89 | }
90 |
91 | @Manual{R-nnet,
92 | title = {nnet: Feed-Forward Neural Networks and Multinomial Log-Linear Models},
93 | author = {Brian Ripley},
94 | year = {2023},
95 | note = {R package version 7.3-19},
96 | url = {http://www.stats.ox.ac.uk/pub/MASS4/},
97 | }
98 |
99 | @Book{car2019,
100 | title = {An {R} Companion to Applied Regression},
101 | edition = {Third},
102 | author = {John Fox and Sanford Weisberg},
103 | year = {2019},
104 | publisher = {Sage},
105 | address = {Thousand Oaks {CA}},
106 | url = {https://socialsciences.mcmaster.ca/jfox/Books/Companion/},
107 | }
108 |
109 | @Book{effects2019,
110 | title = {An R Companion to Applied Regression},
111 | edition = {3rd},
112 | author = {John Fox and Sanford Weisberg},
113 | year = {2019},
114 | publisher = {Sage},
115 | address = {Thousand Oaks CA},
116 | url = {https://socialsciences.mcmaster.ca/jfox/Books/Companion/index.html},
117 | }
118 |
119 | @Article{effects2018,
120 | title = {Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals},
121 | author = {John Fox and Sanford Weisberg},
122 | journal = {Journal of Statistical Software},
123 | year = {2018},
124 | volume = {87},
125 | number = {9},
126 | pages = {1--27},
127 | doi = {10.18637/jss.v087.i09},
128 | }
129 |
130 | @Article{effects2003,
131 | title = {Effect Displays in {R} for Generalised Linear Models},
132 | author = {John Fox},
133 | journal = {Journal of Statistical Software},
134 | year = {2003},
135 | volume = {8},
136 | number = {15},
137 | pages = {1--27},
138 | doi = {10.18637/jss.v008.i15},
139 | }
140 |
141 | @Article{effects2009,
142 | title = {Effect Displays in {R} for Multinomial and Proportional-Odds Logit Models: Extensions to the {effects} Package},
143 | author = {John Fox and Jangman Hong},
144 | journal = {Journal of Statistical Software},
145 | year = {2009},
146 | volume = {32},
147 | number = {1},
148 | pages = {1--24},
149 | doi = {10.18637/jss.v032.i01},
150 | }
151 |
152 | @Article{ggeffects2018,
153 | title = {ggeffects: Tidy Data Frames of Marginal Effects from Regression Models.},
154 | volume = {3},
155 | doi = {10.21105/joss.00772},
156 | number = {26},
157 | journal = {Journal of Open Source Software},
158 | author = {Daniel Lüdecke},
159 | year = {2018},
160 | pages = {772},
161 | }
162 |
163 | @Book{ggplot22016,
164 | author = {Hadley Wickham},
165 | title = {ggplot2: Elegant Graphics for Data Analysis},
166 | publisher = {Springer-Verlag New York},
167 | year = {2016},
168 | isbn = {978-3-319-24277-4},
169 | url = {https://ggplot2.tidyverse.org},
170 | }
171 |
172 | @Book{knitr2015,
173 | title = {Dynamic Documents with {R} and knitr},
174 | author = {Yihui Xie},
175 | publisher = {Chapman and Hall/CRC},
176 | address = {Boca Raton, Florida},
177 | year = {2015},
178 | edition = {2nd},
179 | note = {ISBN 978-1498716963},
180 | url = {https://yihui.org/knitr/},
181 | }
182 |
183 | @InCollection{knitr2014,
184 | booktitle = {Implementing Reproducible Computational Research},
185 | editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng},
186 | title = {knitr: A Comprehensive Tool for Reproducible Research in {R}},
187 | author = {Yihui Xie},
188 | publisher = {Chapman and Hall/CRC},
189 | year = {2014},
190 | note = {ISBN 978-1466561595},
191 | }
192 |
193 | @Book{nnet2002,
194 | title = {Modern Applied Statistics with S},
195 | author = {W. N. Venables and B. D. Ripley},
196 | publisher = {Springer},
197 | edition = {Fourth},
198 | address = {New York},
199 | year = {2002},
200 | note = {ISBN 0-387-95457-0},
201 | url = {https://www.stats.ox.ac.uk/pub/MASS4/},
202 | }
203 |
204 |
--------------------------------------------------------------------------------
/vignettes/references.bib:
--------------------------------------------------------------------------------
1 | @Misc{Atkinson-etal:1984,
2 | author = {Atkinson, Tom and Blishen, Bernard R. and Ornstein, Michael D. and Stevenson, H. Michael},
3 | title = {{Quality of Canadian Life: Social Change in Canada}, 1977},
4 | year = {1984},
5 | doi = {10.3886/ICPSR07879.V1},
6 | language = {en},
7 | publisher = {ICPSR - Interuniversity Consortium for Political and Social Research},
8 | }
9 |
10 | @book{Fienberg:80,
11 | Address = {Cambridge, MA},
12 | Author = {S. E. Fienberg},
13 | Edition = {Second edition},
14 | Isbn = {0-262-06071-X},
15 | Lccn = {QA 278.F53 1980},
16 | Publisher = {MIT Press},
17 | Title = {The Analysis of Cross-Classified Categorical Data},
18 | Year = {1980}}
19 |
20 | @article{Fox:03:effects,
21 | Author = {Fox, John},
22 | Journal = {Journal of Statistical Software},
23 | Number = 15,
24 | Pages = {1--27},
25 | Title = {Effect displays in {R} for generalized linear models},
26 | Volume = 8,
27 | Year = {2003}
28 | }
29 |
30 | @book{Fox:2016:ARA,
31 | author = {Fox, John},
32 | title = {Applied Regression Analysis and Generalized Linear Models},
33 | publisher = {SAGE},
34 | address = {Thousand Oaks {CA}},
35 | isbn = {1452205663},
36 | year = {2016},
37 | edition = {Third edition},
38 | language = {eng},
39 | lccn = {2014959146},
40 | url = {https://www.john-fox.ca/AppliedRegression/index.html}
41 | }
42 |
43 | @book{FoxWeisberg:2011,
44 | Address = {Thousand Oaks {CA}},
45 | Author = {John Fox and Sandford Weisberg},
46 | Edition = {Second edition},
47 | Publisher = {Sage},
48 | Title = {An {R} Companion to Applied Regression},
49 | Url = {https://www.john-fox.ca/Companion/index.html},
50 | Year = {2011},
51 | Bdsk-Url-1 = {https://www.john-fox.ca/Companion/index.html}}
52 |
53 | @book{FriendlyMeyer:2016:DDAR,
54 | Author = {Michael Friendly and Meyer, David},
55 | Title = {Discrete Data Analysis with {R}: Visualization and Modeling Techniques for Categorical and Count Data},
56 | Address = {Boca Raton, FL},
57 | Isbn = {978-1-4987-2583-5},
58 | Publisher = {Chapman \& Hall/CRC},
59 | Year = {2016}
60 | }
61 |
62 | @Book{FoxWeisberg:2019:CAR,
63 | title = {An {R} Companion to Applied Regression},
64 | edition = {Third edition},
65 | author = {John Fox and Sanford Weisberg},
66 | year = {2019},
67 | publisher = {Sage},
68 | address = {Thousand Oaks {CA}},
69 | url = {https://www.john-fox.ca/Companion/index.html},
70 | }
71 |
72 | @Book{Fox:1984:LSM,
73 | title="Linear Statistical Models and Related Methods",
74 | author={John Fox},
75 | year={1984},
76 | publisher={Wiley},
77 | address={New York}
78 | }
79 |
80 | @Manual{R-equatiomatic,
81 | title = {equatiomatic: Transform Models into LaTeX Equations},
82 | author = {Daniel Anderson and Andrew Heiss and Jay Sumners},
83 | year = {2023},
84 | note = {https://github.com/datalorax/equatiomatic},
85 | }
86 |
87 | @Book{Fox:2021:Primer,
88 | title = {A Mathematical Primer for Social Statistics},
89 | edition = {Second edition},
90 | author = {John Fox},
91 | year = {2021},
92 | publisher = {Sage},
93 | address = {Thousand Oaks {CA}},
94 | url = {https://www.john-fox.ca/MathPrimer/index.html},
95 | }
96 |
97 |
--------------------------------------------------------------------------------
Vignettes and other documentation
9 | ![[R logo]](/doc/html/Rlogo.svg)
10 |
11 | 12 |
13 | ![[Top]](/doc/html/up.jpg)
14 |
15 |
14 | Vignettes from package 'nestedLogit'
16 || nestedLogit::nestedLogit | 24 |Nested-dichotomies logistic regression models | 25 |HTML | 26 |source | 27 |R code | |
| nestedLogit::plotting-ggplot | 29 |Plotting nestedLogit models with ggplot2 | 30 |HTML | 31 |source | 32 |R code | |
| nestedLogit::standard-errors | 34 |Standard Errors of Fitted Category Probabilities by the Delta Method for the Nested Logit Model | 35 |HTML | 36 |source | 37 |