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1 | ---
2 | output: github_document
3 | ---
4 |
5 |
6 |
7 | ```{r, include = FALSE}
8 | knitr::opts_chunk$set(
9 | collapse = TRUE,
10 | comment = "#>",
11 | fig.path = "man/figures/README-",
12 | out.width = "100%"
13 | )
14 | ```
15 |
16 | # BayesianWebs
17 |
18 |
19 | 
20 | [](https://github.com/Pakillo/BayesianWebs/actions/workflows/R-CMD-check.yaml)
21 | `r badger::badge_lifecycle("stable")`
22 | `r badger::badge_repostatus("Active")`
23 | [](https://hits.dwyl.com/Pakillo/BayesianWebs)
24 | [](https://hits.dwyl.com/Pakillo/BayesianWebs)
25 |
26 |
27 | The BayesianWebs R package facilitates modelling bipartite networks (like pollination, frugivory, or herbivory networks) using the Bayesian framework developed by [Young et al. (2021)](https://doi.org/10.1038/s41467-021-24149-x).
28 |
29 | Inferring the structure of bipartite networks from field (observational) data is a challenging task. Interaction data are hard to collect and require typically large sampling efforts, particularly to characterize infrequent interactions. Inferred network structure is highly sensitive to sampling design, effort, and completeness. Comparing networks from different studies without accounting for these sampling effects may lead to mistaken inferences.
30 |
31 | This package uses Bayesian modelling to infer the posterior probability of each pairwise interaction in bipartite networks, accounting for sampling completeness and the inherent stochasticity of field observation data.
32 |
33 | ## Installation
34 |
35 | You can install the development version of BayesianWebs from [GitHub](https://github.com/) with:
36 |
37 | ``` r
38 | # install.packages("remotes")
39 | remotes::install_github("Pakillo/BayesianWebs")
40 | ```
41 |
42 | The package requires a working installation of [Stan](https://mc-stan.org/). If you don't have `CmdStan` installed, after installing `BayesianWebs` run:
43 |
44 | ```r
45 | cmdstanr::install_cmdstan()
46 | ```
47 |
48 |
49 | ## Example
50 |
51 | ```{r example}
52 | library(BayesianWebs)
53 | ```
54 |
55 | Let's infer the structure of an example dataset from [Kaiser-Bunbury et al. 2017](https://doi.org/10.1038/nature21071) as in Young et al. 2021:
56 |
57 | ```{r}
58 | data(web)
59 | ```
60 |
61 | This is an adjacency matrix collecting the number of visits of 21 animals on 8 plants:
62 |
63 | ```{r}
64 | web
65 | ```
66 |
67 | ```{r}
68 | plot_counts_obs(web, sort = FALSE)
69 | ```
70 |
71 | First, prepare the data for modelling. Here we assume constant sampling effort among plants:
72 |
73 | ```{r}
74 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
75 | ```
76 |
77 | Now fit the model. There are several models available: the original model used in Young et al. (2021), a model that takes into account varying sampling effort among plants, and a model that allows for varying preferences among animals. The user can also provide a customised Stan model. See `fit_model()`.
78 |
79 | Here we run 4 parallel chains:
80 |
81 | ```{r}
82 | set.seed(1)
83 | options(mc.cores = 4)
84 | fit <- fit_model(dt, refresh = 0)
85 | ```
86 |
87 | Check model:
88 |
89 | ```{r}
90 | check_model(fit, data = dt)
91 | ```
92 |
93 | Get the posteriors:
94 |
95 | ```{r}
96 | post <- get_posterior(fit, data = dt)
97 | head(post)
98 | ```
99 |
100 | ```{r}
101 | plot_interaction_prob(post)
102 | ```
103 |
104 | Predict interaction counts:
105 |
106 | ```{r}
107 | pred.df <- predict_counts(fit, data = dt)
108 | plot_counts_pred(pred.df, sort = FALSE)
109 | ```
110 |
111 | Compare observed and predicted counts:
112 |
113 | ```{r}
114 | plot_counts_pred_obs(pred.df, data = dt)
115 | ```
116 |
117 | Plot residuals:
118 |
119 | ```{r}
120 | plot_residuals(pred.df, data = dt, sort = FALSE)
121 | ```
122 |
123 |
124 | ## Citation
125 |
126 | If you use `BayesianWebs` please cite it:
127 |
128 | ```{r comment=NA}
129 | citation("BayesianWebs")
130 | ```
131 |
132 |
133 |
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47 |
59 |
65 |
66 | An example bipartite network of 8 plant species and 21 pollinators, 39 | from Kaiser-Bunbury et al. 2017.
40 |
29 |
67 |
68 |
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70 |
71 |
72 |
73 |
74 |
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116 | async function searchFuse(query, callback) {
117 | await fuse;
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119 | var items;
120 | if (!fuse) {
121 | items = [];
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126 | .filter((x) => x.score <= 0.75)
127 | .map((x) => x.item);
128 | if (items.length === 0) {
129 | items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}];
130 | }
131 | }
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133 | }
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135 | {
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140 | if (s.title == s.what) {
141 | return `${s.dir} >
30 |
56 |
57 |
58 |
31 |
32 |
35 |
36 |
37 | BayesianWebs: Bayesian Modelling of Bipartite Networks
33 | Source:R/BayesianWebs-package.R
34 | BayesianWebs-package.Rd
38 |
41 |
42 |
43 | Bayesian modelling of bipartite network structure, following the approach of Young et al. doi:10.1038/s41467-021-24149-x 39 | .
40 |
44 |
48 |
49 | See also
45 |Useful links:
- 46 |
Report bugs at https://github.com/Pakillo/BayesianWebs/issues
47 |
50 |
53 |
54 | Author
51 |Maintainer: Francisco Rodriguez-Sanchez f.rodriguez.sanc@gmail.com (ORCID)
52 | ${s.title}
`;
142 | } else if (s.previous_headings == "") {
143 | return `${s.dir} > ${s.title}
> ${s.what}`;
144 | } else {
145 | return `${s.dir} > ${s.title}
> ${s.previous_headings} > ${s.what}`;
146 | }
147 | },
148 | },
149 | },
150 | ]).on('autocomplete:selected', function(event, s) {
151 | window.location.href = s.path + "?q=" + q + "#" + s.id;
152 | });
153 | });
154 | })(window.jQuery || window.$)
155 |
156 | document.addEventListener('keydown', function(event) {
157 | // Check if the pressed key is '/'
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1 |
2 |
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124 |
125 |
126 |
29 |
30 |
32 |
33 | Package index
31 |
40 |
41 |
42 |
43 |
44 |
123 | -
45 |
46 |
check_model()47 | 48 |
49 | - Check fitted model 50 |
-
51 |
52 |
fit_model()53 | 54 |
55 | - Fit model 56 |
-
57 |
58 |
get_posterior()59 | 60 |
61 | - Get posterior values 62 |
-
63 |
64 |
get_seed()65 | 66 |
67 | - Get seed used to fit a model 68 |
-
69 |
70 |
plot_counts_obs()71 | 72 |
73 | - Plot heatmap of observed counts 74 |
-
75 |
76 |
plot_counts_pred()77 | 78 |
79 | - Plot heatmap of predicted counts 80 |
-
81 |
82 |
plot_counts_pred_obs()83 | 84 |
85 | - Plot predicted versus observed counts 86 |
-
87 |
88 |
plot_interaction_prob()89 | 90 |
91 | - Plot heatmap of interaction probabilities 92 |
-
93 |
94 |
plot_prior()95 | 96 |
97 | - Plot prior distribution for r (preference) parameter 98 |
-
99 |
100 |
plot_residuals()101 | 102 |
103 | - Plot heatmap of residuals 104 |
-
105 |
106 |
predict_counts()107 | 108 |
109 | - Predict interaction counts 110 |
-
111 |
112 |
prepare_data()113 | 114 |
115 | - Prepare the data for modelling 116 |
-
117 |
118 |
web119 | 120 |
121 | - Plant-pollinator network 122 |
27 |
80 |
81 |
82 |
83 |
84 |
85 |
86 |
87 |
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1 |
2 |
28 |
34 |
35 |
69 |
70 |
71 |
36 |
38 |
39 |
43 |
44 |
52 |
56 |
57 | Get seed used to fit a model
37 |
58 |
66 |
67 | Examples
59 |if (FALSE) { # interactive()
60 | data(web)
61 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
62 | fit <- fit_model(dt, refresh = 0)
63 | get_seed(fit)
64 | }
65 |
27 |
87 |
88 |
89 |
90 |
91 |
92 |
93 |
94 |
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1 |
2 |
28 |
76 |
77 |
78 |
29 |
30 |
33 |
34 |
35 | Plot heatmap of observed counts
31 | Source:R/plot_counts_obs.R
32 | plot_counts_obs.Rd
36 |
38 |
39 |
43 |
44 | Plot heatmap of observed counts
37 |
45 |
58 |
62 |
63 | Arguments
46 | 47 | 48 |- mat 49 |
A matrix with count data reporting interaction frequency 50 | (e.g. visits to flowers, number of fruits consumed per plant or species). 51 | Plants must be in rows, Animals must be in columns.
52 |
53 |
54 | - ... 55 |
Further arguments for
network.tools::plot_web_heatmap().
56 |
57 |
64 |
73 |
74 | Examples
65 |data(web)
66 | plot_counts_obs(web)
67 | 
68 | plot_counts_obs(web, sort = FALSE)
69 | 
70 | plot_counts_obs(web, zero.na = FALSE, sort = FALSE)
71 | 
72 |
27 |
84 |
85 |
86 |
87 |
88 |
89 |
90 |
91 |
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1 |
2 |
28 |
34 |
35 |
73 |
74 |
75 |
36 |
38 |
39 |
43 |
44 | Check fitted model
37 |
45 |
56 |
60 |
61 | Arguments
46 | 47 | 48 |- fit 49 |
A fitted model, as obtained from
fit_model().
50 |
51 |
52 | - data 53 |
Data list (from
prepare_data()).
54 |
55 |
62 |
70 |
71 | Examples
63 |if (FALSE) { # interactive()
64 | data(web)
65 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
66 | fit <- fit_model(dt, refresh = 0)
67 | check_model(fit, data = data)
68 | }
69 |
29 |
87 |
88 |
89 |
90 |
91 |
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93 |
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1 |
2 |
30 |
36 |
37 |
76 |
77 |
78 |
38 |
41 |
42 |
46 |
47 |
59 |
63 |
64 | Generate the posterior predictive distribution of counts for every pairwise 39 | interaction.
40 |
65 |
73 |
74 | Examples
66 |if (FALSE) { # interactive()
67 | data(web)
68 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
69 | fit <- fit_model(dt, refresh = 0)
70 | predict_counts(fit, dt)
71 | }
72 |
27 |
82 |
83 |
84 |
85 |
86 |
87 |
88 |
89 |
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1 |
2 |
28 |
71 |
72 |
73 |
29 |
30 |
33 |
34 |
35 | Plot heatmap of interaction probabilities
31 | Source:R/plot_interaction_prob.R
32 | plot_interaction_prob.Rd
36 |
38 |
39 |
43 |
44 | Plot a heatmap of average interaction probabilities
37 |
45 |
53 |
57 |
58 | Arguments
46 | 47 | 48 |- post 49 |
Data frame containing the posterior probabilities, as generated 50 | from
get_posterior().
51 |
52 |
59 |
68 |
69 | Examples
60 |if (FALSE) { # interactive()
61 | data(web)
62 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
63 | fit <- fit_model(dt, refresh = 0)
64 | post <- get_posterior(fit, dt)
65 | plot_interaction_prob(post)
66 | }
67 |
27 |
87 |
88 |
89 |
90 |
91 |
92 |
93 |
94 |
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1 |
2 |
28 |
76 |
77 |
78 |
29 |
30 |
33 |
34 |
35 | Plot heatmap of predicted counts
31 | Source:R/plot_counts_pred.R
32 | plot_counts_pred.Rd
36 |
38 |
39 |
43 |
44 | Plot heatmap of predicted counts
37 |
45 |
57 |
61 |
62 | Arguments
46 | 47 | 48 |- pred.df 49 |
A data frame containing the predicted counts, as generated by 50 |
predict_counts()
51 |
52 |
53 | - ... 54 |
Further arguments for
network.tools::plot_web_heatmap().
55 |
56 |
63 |
73 |
74 | Examples
64 |if (FALSE) { # interactive()
65 | data(web)
66 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
67 | fit <- fit_model(dt, refresh = 0)
68 | pred.df <- predict_counts(fit, dt)
69 | plot_counts_pred(pred.df)
70 | plot_counts_pred(pred.df, sort = FALSE)
71 | }
72 |
27 |
91 |
92 |
93 |
94 |
95 |
96 |
97 |
98 |
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1 |
2 |
28 |
34 |
35 |
80 |
81 |
82 |
36 |
38 |
39 |
43 |
44 | Plot heatmap of residuals (observed - predicted counts).
37 |
45 |
61 |
65 |
66 | Arguments
46 | 47 | 48 |- pred.df 49 |
A data frame containing the predicted counts, as generated by 50 |
predict_counts()
51 |
52 |
53 | - data 54 |
Data list (from
prepare_data())
55 |
56 |
57 | - ... 58 |
Further arguments for
network.tools::plot_web_heatmap().
59 |
60 |
67 |
77 |
78 | Examples
68 |if (FALSE) { # interactive()
69 | data(web)
70 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
71 | fit <- fit_model(dt, refresh = 0)
72 | pred.df <- predict_counts(fit, dt)
73 | plot_residuals(pred.df, dt)
74 | plot_residuals(pred.df, dt, sort = FALSE)
75 | }
76 |
27 |
103 |
104 |
105 |
106 |
107 |
108 |
109 |
110 |
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1 |
2 |
3 |
4 | # BayesianWebs
5 |
6 |
7 |
8 | 
9 | [](https://github.com/Pakillo/BayesianWebs/actions/workflows/R-CMD-check.yaml)
10 | [](https://lifecycle.r-lib.org/articles/stages.html#stable)
11 | [](https://www.repostatus.org/#active)
14 | [](https://hits.dwyl.com/Pakillo/BayesianWebs)
15 | [](https://hits.dwyl.com/Pakillo/BayesianWebs)
16 |
17 |
18 | The BayesianWebs R package facilitates modelling bipartite networks
19 | (like pollination, frugivory, or herbivory networks) using the Bayesian
20 | framework developed by [Young et
21 | al. (2021)](https://doi.org/10.1038/s41467-021-24149-x).
22 |
23 | Inferring the structure of bipartite networks from field (observational)
24 | data is a challenging task. Interaction data are hard to collect and
25 | require typically large sampling efforts, particularly to characterize
26 | infrequent interactions. Inferred network structure is highly sensitive
27 | to sampling design, effort, and completeness. Comparing networks from
28 | different studies without accounting for these sampling effects may lead
29 | to mistaken inferences.
30 |
31 | This package uses Bayesian modelling to infer the posterior probability
32 | of each pairwise interaction in bipartite networks, accounting for
33 | sampling completeness and the inherent stochasticity of field
34 | observation data.
35 |
36 | ## Installation
37 |
38 | You can install the development version of BayesianWebs from
39 | [GitHub](https://github.com/) with:
40 |
41 | ``` r
42 | # install.packages("remotes")
43 | remotes::install_github("Pakillo/BayesianWebs")
44 | ```
45 |
46 | The package requires a working installation of
47 | [Stan](https://mc-stan.org/). If you don’t have `CmdStan` installed,
48 | after installing `BayesianWebs` run:
49 |
50 | ``` r
51 | cmdstanr::install_cmdstan()
52 | ```
53 |
54 | ## Example
55 |
56 | ``` r
57 | library(BayesianWebs)
58 | ```
59 |
60 | Let’s infer the structure of an example dataset from [Kaiser-Bunbury et
61 | al. 2017](https://doi.org/10.1038/nature21071) as in Young et al. 2021:
62 |
63 | ``` r
64 | data(web)
65 | ```
66 |
67 | This is an adjacency matrix collecting the number of visits of 21
68 | animals on 8 plants:
69 |
70 | ``` r
71 | web
72 | #> A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
73 | #> P1 0 0 3 1 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0
74 | #> P2 0 0 12 0 0 0 0 0 0 0 0 0 0 8 0 1 0 0 0 0 1
75 | #> P3 0 0 0 0 1 0 7 0 0 0 0 0 0 2 0 0 5 3 0 0 0
76 | #> P4 0 0 2 0 3 0 0 1 0 0 0 0 0 30 0 0 0 0 0 0 0
77 | #> P5 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0
78 | #> P6 0 0 0 4 1 0 1 0 0 0 1 5 2 0 0 0 0 0 0 0 0
79 | #> P7 3 0 33 0 0 2 6 0 2 0 0 0 0 27 0 0 0 0 0 0 2
80 | #> P8 0 1 1 1 2 0 11 0 0 6 0 0 0 1 2 0 0 0 0 1 0
81 | ```
82 |
83 | ``` r
84 | plot_counts_obs(web, sort = FALSE)
85 | ```
86 |
87 |
28 |
34 |
35 |
92 |
93 |
94 |
36 |
38 |
39 | Fit model
37 |
40 |
47 |
48 |
49 | Usage
41 |fit_model(
42 | data = NULL,
43 | model = c("sampling_effort", "Young2021", "varying_preferences"),
44 | beta = 0.01,
45 | ...
46 | )
50 |
76 | Arguments
51 | 52 | 53 |- data 54 |
A named list containing the required data, as obtained from 55 |
prepare_data().
56 |
57 |
58 | - model 59 |
character. One of "Young2021", "sampling_effort", or 60 | "varying_preferences", or a path to a file describing the Stan model in case 61 | you want to use a modified Stan model.
62 |
63 |
64 | - beta 65 |
Rate of exponential prior on
r(preference) parameter. 66 | Default beta is 0.01. Increase it if you have large count numbers 67 | (can examine the resultant prior usingplot_prior()).
68 |
69 |
70 | - ... 71 |
Further arguments for
cmdstanr::sample(), likeiter_warmup, 72 |iter_sampling, orthin, among others. It is recommended to increase the 73 | number of iterations (e.g. iter_sampling = 10000).
74 |
75 |
77 |
80 |
81 | Value
78 |A fitted model (cmdstanr::CmdStanMCMC() object).
82 |
89 |
90 | Examples
83 |if (FALSE) { # interactive()
84 | data(web)
85 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
86 | fit <- fit_model(dt)
87 | }
88 | 
88 |
89 | First, prepare the data for modelling. Here we assume constant sampling
90 | effort among plants:
91 |
92 | ``` r
93 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
94 | ```
95 |
96 | Now fit the model. There are several models available: the original
97 | model used in Young et al. (2021), a model that takes into account
98 | varying sampling effort among plants, and a model that allows for
99 | varying preferences among animals. The user can also provide a
100 | customised Stan model. See `fit_model()`.
101 |
102 | Here we run 4 parallel chains:
103 |
104 | ``` r
105 | set.seed(1)
106 | options(mc.cores = 4)
107 | fit <- fit_model(dt, refresh = 0)
108 | #> Running MCMC with 4 parallel chains...
109 | #>
110 | #> Chain 1 finished in 2.9 seconds.
111 | #> Chain 4 finished in 2.9 seconds.
112 | #> Chain 2 finished in 3.0 seconds.
113 | #> Chain 3 finished in 3.1 seconds.
114 | #>
115 | #> All 4 chains finished successfully.
116 | #> Mean chain execution time: 2.9 seconds.
117 | #> Total execution time: 3.4 seconds.
118 | ```
119 |
120 | Check model:
121 |
122 | ``` r
123 | check_model(fit, data = dt)
124 | #> Processing csv files: /tmp/Rtmpq9vAYS/sampling_effort-202412172000-1-400f5e.csv, /tmp/Rtmpq9vAYS/sampling_effort-202412172000-2-400f5e.csv, /tmp/Rtmpq9vAYS/sampling_effort-202412172000-3-400f5e.csv, /tmp/Rtmpq9vAYS/sampling_effort-202412172000-4-400f5e.csv
125 | #>
126 | #> Checking sampler transitions treedepth.
127 | #> Treedepth satisfactory for all transitions.
128 | #>
129 | #> Checking sampler transitions for divergences.
130 | #> No divergent transitions found.
131 | #>
132 | #> Checking E-BFMI - sampler transitions HMC potential energy.
133 | #> E-BFMI satisfactory.
134 | #>
135 | #> Effective sample size satisfactory.
136 | #>
137 | #> Split R-hat values satisfactory all parameters.
138 | #>
139 | #> Processing complete, no problems detected.
140 | ```
141 |
142 | 
143 |
144 | Get the posteriors:
145 |
146 | ``` r
147 | post <- get_posterior(fit, data = dt)
148 | head(post)
149 | #> # A tibble: 6 × 11
150 | #> # Groups: Plant, Animal [6]
151 | #> Plant Animal .chain .iteration .draw connectance preference plant.abund
152 | #> 
167 |
168 | Predict interaction counts:
169 |
170 | ``` r
171 | pred.df <- predict_counts(fit, data = dt)
172 | plot_counts_pred(pred.df, sort = FALSE)
173 | ```
174 |
175 | 
176 |
177 | Compare observed and predicted counts:
178 |
179 | ``` r
180 | plot_counts_pred_obs(pred.df, data = dt)
181 | ```
182 |
183 | 
184 |
185 | Plot residuals:
186 |
187 | ``` r
188 | plot_residuals(pred.df, data = dt, sort = FALSE)
189 | ```
190 |
191 | 
192 |
193 | ## Citation
194 |
195 | If you use `BayesianWebs` please cite it:
196 |
197 | ``` r
198 | citation("BayesianWebs")
199 | If you use BayesianWebs, please cite both Young et al. (2021) and the
200 | package as:
201 |
202 | Young J, Valdovinos F, Newman M (2021). "Reconstruction of
203 | plant–pollinator networks from observational data." _Nature
204 | Communications_, *12*, 3911. doi:10.1038/s41467-021-24149-x
205 |
27 |
103 |
104 |
105 |
106 |
107 |
108 |
109 |
110 |
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1 | /*!
2 | * clipboard.js v2.0.11
3 | * https://clipboardjs.com/
4 | *
5 | * Licensed MIT © Zeno Rocha
6 | */
7 | !function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1
28 |
34 |
35 |
92 |
93 |
94 |
36 |
38 |
39 |
43 |
44 | Prepare the data for modelling
37 |
45 |
59 |
63 |
64 | Arguments
46 | 47 | 48 |- mat 49 |
An integer matrix containing quantitative (not qualitative or binary) 50 | count data con interaction frequency (e.g. visits to flowers, number of fruits 51 | consumed per plant or species). Plants must be in rows, Animals must be in columns.
52 |
53 |
54 | - sampl.eff 55 |
A numeric vector with the sampling effort (e.g. observation hours) 56 | spent on each plant.
57 |
58 |
65 |
89 |
90 | Examples
66 |data(web)
67 | prepare_data(web, sampl.eff = rep(20, nrow(web)))
68 | #> $M
69 | #> A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
70 | #> P1 0 0 3 1 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0
71 | #> P2 0 0 12 0 0 0 0 0 0 0 0 0 0 8 0 1 0 0 0 0 1
72 | #> P3 0 0 0 0 1 0 7 0 0 0 0 0 0 2 0 0 5 3 0 0 0
73 | #> P4 0 0 2 0 3 0 0 1 0 0 0 0 0 30 0 0 0 0 0 0 0
74 | #> P5 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0
75 | #> P6 0 0 0 4 1 0 1 0 0 0 1 5 2 0 0 0 0 0 0 0 0
76 | #> P7 3 0 33 0 0 2 6 0 2 0 0 0 0 27 0 0 0 0 0 0 2
77 | #> P8 0 1 1 1 2 0 11 0 0 6 0 0 0 1 2 0 0 0 0 1 0
78 | #>
79 | #> $n_p
80 | #> [1] 8
81 | #>
82 | #> $n_a
83 | #> [1] 21
84 | #>
85 | #> $C
86 | #> [1] 20 20 20 20 20 20 20 20
87 | #>
88 |
35 |
105 |
106 |
107 |
108 |
109 |
110 |
111 |
112 |
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/docs/reference/get_posterior.html:
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1 |
2 |
36 |
94 |
95 |
96 |
37 |
38 |
41 |
42 |
43 | Plot prior distribution for r (preference) parameter
39 | Source:R/plot_prior.R
40 | plot_prior.Rd
44 |
50 |
51 |
55 |
56 | The r (preference) parameter in Young et al. model takes a prior
45 | exponential distribution with rate = beta. Use this function to
46 | visualise the prior distribution of r given the chosen beta.
47 | Alternatively, if providing the fitted model, a plot comparing the
48 | prior versus posterior preference(s) will be produced.
57 |
72 |
76 |
77 | Arguments
58 | 59 | 60 |- beta 61 |
A number > 0. Rate of the exponential distribution.
62 |
63 |
64 | - fit 65 |
A fitted model, as obtained from
fit_model().
66 |
67 |
68 | - data 69 |
Data list (from
prepare_data()).
70 |
71 |
78 |
91 |
92 | Examples
79 |if (FALSE) { # interactive()
80 | ## Providing value for beta
81 | plot_prior(beta = 0.01)
82 | plot_prior(beta = 0.001)
83 |
84 | ## Providing fitted model
85 | data(web)
86 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
87 | fit <- fit_model(dt, refresh = 0)
88 | plot_prior(fit = fit, data = dt)
89 | }
90 |
27 |
100 |
101 |
102 |
103 |
104 |
105 |
106 |
107 |
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/docs/reference/plot_counts_pred_obs.html:
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1 |
2 |
28 |
34 |
35 |
89 |
90 |
91 |
36 |
38 |
39 | Get posterior values
37 |
40 |
47 |
48 |
49 | Usage
41 |get_posterior(
42 | fit = NULL,
43 | data = NULL,
44 | param = c("all", "connectance", "preference", "plant.abund", "animal.abund",
45 | "int.prob", "link")
46 | )
50 |
65 |
69 |
70 | Arguments
51 | 52 | 53 |- fit 54 |
Fitted model (from
fit_model())
55 |
56 |
57 | - data 58 |
Data list (from
prepare_data())
59 |
60 |
61 | - param 62 |
character. Name of the parameter to retrieve the posterior samples.
63 |
64 |
71 |
86 |
87 | Examples
72 |if (FALSE) { # interactive()
73 | data(web)
74 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
75 | fit <- fit_model(dt, refresh = 0)
76 | get_posterior(fit, dt, param = "connectance")
77 |
78 | int.prob <- get_posterior(fit, dt, param = "int.prob")
79 | int.prob
80 | int.prob |> tidybayes::mean_qi() # mean edge probability
81 |
82 | # all posteriors
83 | get_posterior(fit, dt, param = "all")
84 | }
85 |
27 |
109 |
110 |
111 |
112 |
113 |
114 |
115 |
116 |
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28 |
98 |
99 |
100 |
29 |
30 |
33 |
34 |
35 | Plot predicted versus observed counts
31 | Source:R/plot_counts_pred_obs.R
32 | plot_counts_pred_obs.Rd
36 |
38 |
39 | Plot predicted versus observed counts
37 |
40 |
48 |
49 |
50 | Usage
41 |plot_counts_pred_obs(
42 | pred.df = NULL,
43 | data = NULL,
44 | byplant = FALSE,
45 | width = 0.95,
46 | ...
47 | )
51 |
77 |
81 |
82 | Arguments
52 | 53 | 54 |- pred.df 55 |
A data frame containing the predicted counts, as generated by 56 |
predict_counts()
57 |
58 |
59 | - data 60 |
Data list (from
prepare_data())
61 |
62 |
63 | - byplant 64 |
Logical. If TRUE, show predicted and observed counts per plant 65 | (using
ggplot2::facet_wrap()). If FALSE, show all interactions in the same plot.
66 |
67 |
68 | - width 69 |
width of the credible interval (default = 0.95).
70 |
71 |
72 | - ... 73 |
Further arguments to be passed to
ggplot2::facet_wrap()if 74 |byplant = TRUE, or totidybayes::geom_pointinterval()ifbyplant = FALSE.
75 |
76 |
83 |
95 |
96 | Examples
84 |if (FALSE) { # interactive()
85 | data(web)
86 | dt <- prepare_data(mat = web, sampl.eff = rep(20, nrow(web)))
87 | fit <- fit_model(dt, refresh = 0)
88 | pred.df <- predict_counts(fit, dt)
89 | plot_counts_pred_obs(pred.df, dt)
90 | plot_counts_pred_obs(pred.df, dt, fatten_point = 3)
91 | plot_counts_pred_obs(pred.df, dt, byplant = TRUE)
92 | plot_counts_pred_obs(pred.df, dt, byplant = TRUE, scale = "free")
93 | }
94 |