From cbae9485845a02ce62674225db395501e84669b5 Mon Sep 17 00:00:00 2001 From: Francisco Rodriguez-Sanchez Date: Wed, 18 Feb 2026 17:01:55 +0100 Subject: [PATCH 1/3] delete old files --- .github/workflows/check-release.yaml | 35 -- .gitignore | 1 + DESCRIPTION | 2 +- README.Rmd | 2 + docs/404.html | 118 ------ docs/LICENSE.html | 285 ------------- ...mpleteness-of-the-recruitment-network.html | 258 ------------ docs/articles/The-RecruitNet-database.html | 156 ------- docs/articles/index.html | 91 ----- docs/authors.html | 127 ------ docs/bootstrap-toc.css | 60 --- docs/bootstrap-toc.js | 159 -------- docs/docsearch.css | 148 ------- docs/docsearch.js | 85 ---- docs/index.html | 203 --------- docs/link.svg | 12 - docs/news/index.html | 92 ----- docs/pkgdown.css | 384 ------------------ docs/pkgdown.js | 108 ----- docs/pkgdown.yml | 8 - docs/reference/CanopyCover.html | 104 ----- docs/reference/RN_to_matrix.html | 136 ------- docs/reference/RecruitNet.html | 104 ----- docs/reference/RecruitNetP-package.html | 107 ----- docs/reference/Rplot001.png | Bin 1011 -> 0 bytes docs/reference/comm_subset.html | 124 ------ docs/reference/comm_summary.html | 120 ------ docs/reference/comm_to_RN.html | 137 ------- docs/reference/download_RN.html | 127 ------ docs/reference/index.html | 126 ------ docs/reference/link_completeness.html | 142 ------- docs/reference/sign_net.html | 159 -------- docs/sitemap.xml | 60 --- 33 files changed, 4 insertions(+), 3776 deletions(-) delete mode 100644 .github/workflows/check-release.yaml delete mode 100644 docs/404.html delete mode 100644 docs/LICENSE.html delete mode 100644 docs/articles/Completeness-of-the-recruitment-network.html delete mode 100644 docs/articles/The-RecruitNet-database.html delete mode 100644 docs/articles/index.html delete mode 100644 docs/authors.html delete mode 100644 docs/bootstrap-toc.css delete mode 100644 docs/bootstrap-toc.js delete mode 100644 docs/docsearch.css delete mode 100644 docs/docsearch.js delete mode 100644 docs/index.html delete mode 100644 docs/link.svg delete mode 100644 docs/news/index.html delete mode 100644 docs/pkgdown.css delete mode 100644 docs/pkgdown.js delete mode 100644 docs/pkgdown.yml delete mode 100644 docs/reference/CanopyCover.html delete mode 100644 docs/reference/RN_to_matrix.html delete mode 100644 docs/reference/RecruitNet.html delete mode 100644 docs/reference/RecruitNetP-package.html delete mode 100644 docs/reference/Rplot001.png delete mode 100644 docs/reference/comm_subset.html delete mode 100644 docs/reference/comm_summary.html delete mode 100644 docs/reference/comm_to_RN.html delete mode 100644 docs/reference/download_RN.html delete mode 100644 docs/reference/index.html delete mode 100644 docs/reference/link_completeness.html delete mode 100644 docs/reference/sign_net.html delete mode 100644 docs/sitemap.xml diff --git a/.github/workflows/check-release.yaml b/.github/workflows/check-release.yaml deleted file mode 100644 index 7dc44bc..0000000 --- a/.github/workflows/check-release.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples -# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help -on: - push: - branches: [main, master] - pull_request: - branches: [main, master] - -name: R-CMD-check - -permissions: read-all - -jobs: - R-CMD-check: - timeout-minutes: 10 - runs-on: ubuntu-latest - env: - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - R_KEEP_PKG_SOURCE: yes - steps: - - uses: actions/checkout@v4 - - - uses: r-lib/actions/setup-r@v2 - with: - use-public-rspm: true - - - uses: r-lib/actions/setup-r-dependencies@v2 - with: - extra-packages: any::rcmdcheck - needs: check - - - uses: r-lib/actions/check-r-package@v2 - with: - upload-snapshots: true - build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")' diff --git a/.gitignore b/.gitignore index 565f2b6..9168bf8 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ .Rdata .httr-oauth .DS_Store +docs diff --git a/DESCRIPTION b/DESCRIPTION index ba97313..4e1576c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -19,7 +19,7 @@ License: GPL (>= 3) Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.2 -URL: https://github.com/EcologyR/RecruitNetP +URL: https://github.com/EcologyR/RecruitNetP, https://ecologyr.github.io/RecruitNetP/ BugReports: https://github.com/EcologyR/RecruitNetP/issues Depends: R (>= 4.1.0) diff --git a/README.Rmd b/README.Rmd index 8bc68fd..b1f010f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -22,6 +22,8 @@ knitr::opts_chunk$set( `r badger::badge_repostatus("WIP")` [![HitCount](https://hits.dwyl.com/EcologyR/RecruitNetP.svg?style=flat-square)](http://hits.dwyl.com/EcologyR/RecruitNetP) [![HitCount](https://hits.dwyl.com/EcologyR/RecruitNetP.svg?style=flat-square&show=unique)](http://hits.dwyl.com/EcologyR/RecruitNetP) +[![R-CMD-check](https://github.com/EcologyR/RecruitnetP/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/EcologyR/RecruitnetP/actions/workflows/R-CMD-check.yaml) +[![Codecov test coverage](https://codecov.io/gh/EcologyR/RecruitnetP/graph/badge.svg)](https://app.codecov.io/gh/EcologyR/RecruitnetP) The goal of RecruitNetP is to ... diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index 029222a..0000000 --- a/docs/404.html +++ /dev/null @@ -1,118 +0,0 @@ - - - - - - - -Page not found (404) • RecruitNetP - - - - - - - - - - - -
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Page not found (404)

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Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

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Site built with pkgdown 2.0.9.

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Version 3, 29 June 2007
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Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

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- -
- - - - - - - - diff --git a/docs/articles/Completeness-of-the-recruitment-network.html b/docs/articles/Completeness-of-the-recruitment-network.html deleted file mode 100644 index c667b4d..0000000 --- a/docs/articles/Completeness-of-the-recruitment-network.html +++ /dev/null @@ -1,258 +0,0 @@ - - - - - - - -Completeness of the recruitment network • RecruitNetP - - - - - - - - - - - - -
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Completeness of the recruitment network

- - - Source: vignettes/articles/Completeness-of-the-recruitment-network.Rmd - - -
- - - - -
-

Is my RN representative of the “true” RN of the local -community? -

-

The basic parameters of a RN are its order (number of nodes: -S) and size (number of links: l). The ratio -of the size to the square of order is the connectance of the -network: \(C = l/S^2\) (proportion of -the maximum possible number of links that are actually realized). An -important preliminary step before the analysis of the RN is determining -to which extent our sampling provides a good approximation to estimate -these basic parameters for the “real” RN.

-

It is well known in community ecology that estimates of S -depend on sampling effort, and there is ample literature on how to -determine the completeness of the list of species sampled in a local -assemblage (see Chao et al. 2020 and references therein). The same -problem affects the estimation of the number of links that can take -place in a local assemblage. Even if we scan a locality inch by inch, -some interactions may not be realized within the scanned area just by -chance. Besides, the frequency of some interactions may be too low to be -detected with a limited sampling effort, and some canopy species may -pose difficulties to searching beneath them (e.g. those covered by -spines or thorns). The studies that have assessed the completeness of -ecological networks (Goldwasser and Roughgarden 1997, Nielsen and -Bascompte 2007, Rivera-Hutinel et al. 2012, Costa et al. 2016, -Vizentin-Bugoni et al. 2016, Pulgar et al. 2017, Henriksen et al 2019) -agree in that the level of sampling effort needed for a good coverage of -the links present in a network is much larger than the effort needed to -obtain a good coverage of the number of species in the local assemblage. -This implies that C may often be underestimated and that other -properties of the network can also be affected. Networks inferred with -insufficient or biased data will lead to biased or totally wrong -conclusions. This was a fundamental flaw in the early studies on food -webs that slowed down the development of this field (Pascual & Dunne -2006).

-

Analytical procedures developed to estimate species richness can be -adapted for the study of interactions in ecological networks, simply -treating each link as if it was a “species” and its frequency as -equivalent to species abundance (Chacoff et al. 2012, Jordano 2016, Chiu -et al. 2023). Particularly interesting is the adaptation for the study -of interaction networks made by Chiu et al. (2023) of the methods for -the analysis of completeness developed by Chao et al. (2014, 2020) based -in Hill’s numbers (q). In the context of ecological networks, -we are mostly interested in the Hill’s numbers of order 0 and 1 as -applied to species and interactions counts. Hill’s number of order 0 -(q0) estimated with the whole sample is simply the observed -number of species or links (q0obs). But the observed -number underestimates the real number, so we need an estimate of the -real q0est, which can be obtained using -non-parametric estimators (e.g, Chao1, Chao2, ACE or ICE). Using the -non-parametric estimate as reference, we can estimate the completeness -of q0 as \(^0^C = -q0~obs~/q0~est~\). In turn, Hill’s number of order 1 -(q1) is the exponential of Shannon’s index of -diversity. Differently from q0, q1 incorporates -information on the relative frequencies. The completeness of -q1 (1C) is most often referred -to as “coverage”, and indicates the proportion of the total number of -individuals of any species, or of events of any interaction, occurring -the real community that belong to species or links that have already -been sampled.

-

The counts to estimate the number of species or links can be obtained -by means of different sampling schemes but, in general, there are two -types of counting: abundance counts and incidence counts (a.k.a. -occurrence or presence counts) (Gotelli and Colwell 2001). Cases -(defined as independent individuals of any species or as occurrences of -any link in different sampling units) are randomly sampled from the -whole set of cases in the local assemblage. In abundance counts, the -cases are counted individually, as when counting the number of recruits -of each species in a plot. Incidence counts are used when individual -cases cannot be clearly distinguished to be counted (e.g. when counting -clonal plants or corals) or when they cannot be considered as -independent samples (e.g. multiple individual ants of the same species -falling in a pit-fall trap). In these cases, we count the number of -times each species or link is detected across multiple samples, and -these samples are considered as randomly chosen “replicates” of the -local community. When estimating species richness, incidence-based -estimates are less sensitive to the spatial aggregation of individuals -than abundance-based estimates (Chazdon et al. 1998, Colwell et -al. 2004, Chao et al. 2014).

-

Incidence data have another interesting advantage over abundance data -in the context of networks: when each sample is sufficiently large to be -considered as a replicate of the local community, the stability of -network descriptors against sampling effort can be estimated (Nielsen -and Bascompte 2007, Costa et al. 2016, Pulgar et al. 2017). Basically, -if we sample canopy-recruit interactions in N plots, we can use -the following process:

-
    -
  1. Build the partial RN of each of the N plots.
    2. -
  2. Combine the partial RNs of k (see explanation below) -randomly chosen pairs of plots, obtain their metric and calculate the -mean and 95%CL. This is the value with sampling effort -n=2.
    3. -
  3. Repeat the process for all the levels of sampling effort up to -n=N-2 plots.
    4. -
-

The metrics for sampling effort of n=1 plot and for -n=N-1 plots can only be based on N different -values (one for each of the N plots or one per each of the -N possible combination of N-1 plots). However, the -number of random combinations (k) of n = 2, 3 … -N-2 partial RNs can be much larger than N -(i.e. combinations of N partial RNs taken n at a -time). Therefore, to obtain comparable confidence intervals across -levels of sampling effort, k must be constant in all levels. -The value of k should not be too small so that the confidence -intervals are obtained for a large number of random combinations. For -example, with N=20 plots, there are 20 possible combinations of -1 (or of N-1) plots, 190 possible combinations of 2 (or of -N-2) plots and 1140 possible combinations of 3 (or of -N-3) plots. In this example, if we set k = 100, then -we could estimate the effect of sampling effort for n in the -range between 2 and 18 plots.

-

This procedure allows determining whether the value of each network -metric stabilizes within the sampling effort of the survey.

-

To address the quality of a dataset for the study of recruitment -networks, we will determine (1) the completeness -(0C) and coverage (1C) of the -number of species and links detected, and (2) the stability of network -metrics to sampling effort.

-
-

1. Completeness and coverage of species and links. -

-

The following analyses apply to data collected using the Recruitment -Network protocol described in Alcantara et al. (2019), which involves -partial RNs sampled in multiple plots. Accordingly, to obtain -completeness and coverage estimates for a recruitment network, we will -use iNEXT package (Hsieh et al. 2016) for incidence -data.

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- - - -
- - - -
-

-

Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

-
- -
-

-

Site built with pkgdown 2.0.9.

-
- -
-
- - - - - - - - diff --git a/docs/articles/The-RecruitNet-database.html b/docs/articles/The-RecruitNet-database.html deleted file mode 100644 index 074dda4..0000000 --- a/docs/articles/The-RecruitNet-database.html +++ /dev/null @@ -1,156 +0,0 @@ - - - - - - - -The RecruitNet database • RecruitNetP - - - - - - - - - - - - -
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The RecruitNet database

- - - Source: vignettes/articles/The-RecruitNet-database.Rmd - - -
- - - - -
-

Introduction to the RecruitNet database. -

-

The RecruitNet is a dataset containing information on plant-plant -recruitment interactions in 143 local plant communities (Study sites) -from 23 countries across five continents, including temperate and -tropical ecosystems. Each network provides information on the number of -individuals of every “recruit” species recruiting in the proximity of -every “canopy” species or away from other plants (in so called “open” -interspaces). The data set includes more than 850000 recruiting -individuals involved in 87924 different canopy-recruit pairs among 3186 -vascular plant species. The cover of canopy species and open ground is -also provided. It was published as a data paper by Verdu et -al. (2023)1 and can be downloaded from:

-

https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecy.3923

-

1Verdú, M., Garrido, J. L., Alcantara, J. M., -Montesinos‐Navarro, A., Aguilar, S., Aizen, M. A., … & Zamora, R. -(2023). RecruitNet: A global database of plant recruitment networks. -Ecology 104(2):e3923.

-

The objectives of this script are: (1) extracting from RecruitNet -database the information from one of the study sites, and (2) define the -optional data structures required for different analyses. Before -starting, it is always a good idea to familiarize with the contentes of -the dataset by reading the Metadata file provided with the paper.

-

The RecruitNet contains two “csv” files: -RecruitNet.csv, with the bulk of data for -canopy-recruit interactions, and CanopyCover.csv with -data on the cover of each species in each study site.

-
-
- - - -
- - - -
-

-

Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

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- - - - - - - - diff --git a/docs/articles/index.html b/docs/articles/index.html deleted file mode 100644 index 26fc706..0000000 --- a/docs/articles/index.html +++ /dev/null @@ -1,91 +0,0 @@ - -Articles • RecruitNetP - - -
-
- - - -
-
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Articles

-
- -
-

All vignettes

-

- -
Completeness of the recruitment network
-
-
The RecruitNet database
-
-
-
-
- - -
-

Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

-
- -
-

Site built with pkgdown 2.0.9.

-
- -
- - - - - - - - diff --git a/docs/authors.html b/docs/authors.html deleted file mode 100644 index 0ec12df..0000000 --- a/docs/authors.html +++ /dev/null @@ -1,127 +0,0 @@ - -Authors and Citation • RecruitNetP - - -
-
- - - -
-
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Authors and Citation

-
- - -
  • -

    Alicia Montesinos-Navarro. Author, copyright holder. -

    -
    • -
  • -

    José Luis Garrido. Author, copyright holder. -

    -
    • -
  • -

    Miguel Verdú. Author, copyright holder. -

    -
    • -
  • -

    Julio M. Alcántara. Author, copyright holder. -

    -
    • -
  • -

    Francisco Rodriguez-Sanchez. Author, maintainer, copyright holder. -

    -
    • -
-
-
-

Citation

- Source: DESCRIPTION -
-
- - -

Montesinos-Navarro A, Garrido J, Verdú M, Alcántara J, Rodriguez-Sanchez F (2024). -RecruitNetP: Analysis of Recruitment Networks. -R package version 0.0.0.9000, https://github.com/EcologyR/RecruitNetP. -

- 
@Manual{,
-  title = {RecruitNetP: Analysis of Recruitment Networks},
-  author = {Alicia Montesinos-Navarro and José Luis Garrido and Miguel Verdú and Julio M. Alcántara and Francisco Rodriguez-Sanchez},
-  year = {2024},
-  note = {R package version 0.0.0.9000},
-  url = {https://github.com/EcologyR/RecruitNetP},
-}
- -
- -
- - - -
-

Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

-
- -
-

Site built with pkgdown 2.0.9.

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- $("#search-input").focus(); - } - }); - - $(document).ready(function() { - // do keyword highlighting - /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ - var mark = function() { - - var referrer = document.URL ; - var paramKey = "q" ; - - if (referrer.indexOf("?") !== -1) { - var qs = referrer.substr(referrer.indexOf('?') + 1); - var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index d4b1ac6..0000000 --- a/docs/index.html +++ /dev/null @@ -1,203 +0,0 @@ - - - - - - - -Analysis of Recruitment Networks • RecruitNetP - - - - - - - - - - - - -
    -
    - - - - -
    -
    -
    -

    RecruitnetP -

    - - -

    The goal of RecruitNetP is to …

    -
    -

    Installation -

    -
    -# install.packages("remotes")
-remotes::install_github("EcologyR/RecruitNetP")
    -
    -
    -

    Example -

    -

    This is a basic example which shows you how to solve a common problem:

    -
    -
    -

    Citation -

    -

    If using this package, please cite it:

    -
    citation("RecruitNetP")
-Warning in citation("RecruitNetP"): could not determine year for 'RecruitNetP'
-from package DESCRIPTION file
-To cite package 'RecruitNetP' in publications use:
-
-  Montesinos-Navarro A, Garrido J, Verdú M, Alcántara J,
-  Rodriguez-Sanchez F (????). _RecruitNetP: Analysis of Recruitment
-  Networks_. R package version 0.0.0.9000,
-  <https://github.com/EcologyR/RecruitNetP>.
-
-A BibTeX entry for LaTeX users is
-
-  @Manual{,
-    title = {RecruitNetP: Analysis of Recruitment Networks},
-    author = {Alicia Montesinos-Navarro and José Luis Garrido and Miguel Verdú and Julio M. Alcántara and Francisco Rodriguez-Sanchez},
-    note = {R package version 0.0.0.9000},
-    url = {https://github.com/EcologyR/RecruitNetP},
-  }
    -
    -
    -

    Funding -

    -

    The development of this software has been funded by Fondo Europeo de Desarrollo Regional (FEDER) and Consejería de Transformación Económica, Industria, Conocimiento y Universidades of Junta de Andalucía (proyecto US-1381388 led by Francisco Rodríguez Sánchez, Universidad de Sevilla).

    -

    -
    -
    -
    - - -
    - - -
    -

    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    -

    Site built with pkgdown 2.0.9.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad827..0000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/news/index.html b/docs/news/index.html deleted file mode 100644 index 5298a2f..0000000 --- a/docs/news/index.html +++ /dev/null @@ -1,92 +0,0 @@ - -Changelog • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    Changelog

    - Source: NEWS.md -
    - -
    -

    RecruitNetP 0.0.0.9000

    -
    • Added a NEWS.md file to track changes to the package.
      • -
    -
    - - - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/pkgdown.css b/docs/pkgdown.css deleted file mode 100644 index 80ea5b8..0000000 --- a/docs/pkgdown.css +++ /dev/null @@ -1,384 +0,0 @@ -/* Sticky footer */ - -/** - * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ - * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css - * - * .Site -> body > .container - * .Site-content -> body > .container .row - * .footer -> footer - * - * Key idea seems to be to ensure that .container and __all its parents__ - * have height set to 100% - * - */ - -html, body { - height: 100%; -} - -body { - position: relative; -} - -body > .container { - display: flex; - height: 100%; - flex-direction: column; -} - -body > .container .row { - flex: 1 0 auto; -} - -footer { - margin-top: 45px; - padding: 35px 0 36px; - border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; 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- font-size: 1.35rem; -} - -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 15px; -} - -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 15px; - font-weight: 500; - font-size: 1.35rem; -} - -/* orcid ------------------------------------------------------------------- */ - -.orcid { - font-size: 16px; - color: #A6CE39; - /* margins are required by official ORCID trademark and display guidelines */ - margin-left:4px; - margin-right:4px; - vertical-align: middle; -} - -/* Reference index & topics ----------------------------------------------- */ - -.ref-index th {font-weight: normal;} - -.ref-index td {vertical-align: top; min-width: 100px} -.ref-index .icon {width: 40px;} -.ref-index .alias {width: 40%;} -.ref-index-icons .alias {width: calc(40% - 40px);} -.ref-index .title {width: 60%;} - -.ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px} -.ref-arguments .name {width: 20%;} -.ref-arguments .desc {width: 80%;} - -/* Nice scrolling for wide elements --------------------------------------- */ - -table { - display: block; - overflow: auto; -} - -/* Syntax highlighting ---------------------------------------------------- */ - -pre, code, pre code { - background-color: #f8f8f8; - color: #333; -} -pre, pre code { - white-space: pre-wrap; - word-break: break-all; - overflow-wrap: break-word; -} - -pre { - border: 1px solid #eee; -} - -pre .img, pre .r-plt { - margin: 5px 0; -} - -pre .img img, pre .r-plt img { - background-color: #fff; -} - -code a, pre a { - color: #375f84; -} - -a.sourceLine:hover { - text-decoration: none; -} - -.fl {color: #1514b5;} -.fu {color: #000000;} /* function */ -.ch,.st {color: #036a07;} /* string */ -.kw {color: #264D66;} /* keyword */ -.co {color: #888888;} /* comment */ - -.error {font-weight: bolder;} -.warning {font-weight: bolder;} - -/* Clipboard --------------------------*/ - -.hasCopyButton { - position: relative; -} - -.btn-copy-ex { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.hasCopyButton:hover button.btn-copy-ex { - visibility: visible; -} - -/* headroom.js ------------------------ */ - -.headroom { - will-change: transform; - transition: transform 200ms linear; -} -.headroom--pinned { - transform: translateY(0%); -} -.headroom--unpinned { - transform: translateY(-100%); -} - -/* mark.js ----------------------------*/ - -mark { - background-color: rgba(255, 255, 51, 0.5); - border-bottom: 2px solid rgba(255, 153, 51, 0.3); - padding: 1px; -} - -/* vertical spacing after htmlwidgets */ -.html-widget { - margin-bottom: 10px; -} - -/* fontawesome ------------------------ */ - -.fab { - font-family: "Font Awesome 5 Brands" !important; -} - -/* don't display links in code chunks when printing */ -/* source: https://stackoverflow.com/a/10781533 */ -@media print { - code a:link:after, code a:visited:after { - content: ""; - } -} - -/* Section anchors --------------------------------- - Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 -*/ - -div.csl-bib-body { } -div.csl-entry { - clear: both; -} -.hanging-indent div.csl-entry { - margin-left:2em; - text-indent:-2em; -} -div.csl-left-margin { - min-width:2em; - float:left; -} -div.csl-right-inline { - margin-left:2em; - padding-left:1em; -} -div.csl-indent { - margin-left: 2em; -} diff --git a/docs/pkgdown.js b/docs/pkgdown.js deleted file mode 100644 index 6f0eee4..0000000 --- a/docs/pkgdown.js +++ /dev/null @@ -1,108 +0,0 @@ -/* http://gregfranko.com/blog/jquery-best-practices/ */ -(function($) { - $(function() { - - $('.navbar-fixed-top').headroom(); - - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); - }); - - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $("div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index 995aca8..0000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,8 +0,0 @@ -pandoc: 3.1.11 -pkgdown: 2.0.9 -pkgdown_sha: ~ -articles: - Completeness-of-the-recruitment-network: Completeness-of-the-recruitment-network.html - The-RecruitNet-database: The-RecruitNet-database.html -last_built: 2024-07-11T17:45Z - diff --git a/docs/reference/CanopyCover.html b/docs/reference/CanopyCover.html deleted file mode 100644 index 28dfe30..0000000 --- a/docs/reference/CanopyCover.html +++ /dev/null @@ -1,104 +0,0 @@ - -CanopyCover — CanopyCover • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    CanopyCover

    - Source: R/datasets.R - -
    - -
    -

    Canopy cover of plant-plant interactions global database

    -
    - -
    - 
    CanopyCover
    -
    - -
    -

    Format

    -

    An object of class data.frame with 26812 rows and 7 columns.

    -
    -
    -

    Source

    -

    https://doi.org/10.1002/ecy.3923

    -
    - -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/RN_to_matrix.html b/docs/reference/RN_to_matrix.html deleted file mode 100644 index 11a69b0..0000000 --- a/docs/reference/RN_to_matrix.html +++ /dev/null @@ -1,136 +0,0 @@ - -Function RN_matrixForm — RN_to_matrix • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    Function RN_matrixForm

    - Source: R/RN_to_matrix.R - -
    - -
    -

    For some analyses, the data must be stored as a matrix rather than data frame. -The next function takes a RN stored as data frame and transforms it into a matrix. -The output matrix can be used input for bipartite package.

    -
    - -
    - 
    RN_to_matrix(RNdata = NULL, weight = NULL)
    -
    - -
    -

    Arguments

    -
    RNdata
    -

    A data frame generated with comm_subset() or a data.frame -containing columns with all the information needed for the basic analysis of -recruitment networks: canopy species (canopy), recruit species (recruit), -cover of the canopy (cj) and recruit (ci), -and columns with possible weighting variables.

    - - -
    weight
    -

    The name of the column of the data frame to be used as weight -variable for the links.

    - -
    -
    -

    Value

    - - -

    A matrix object with the species names as row (recruit species) and -column names (canopy species), and cells indicating the chosen weight for -each interaction. For example, the output of function local_RN provides the -number of recruits of species i interacting with canopy species j (fij), -the number of plots (i.e. incidence) where the interaction has been found (Tij), -and the binary presence/absence of the interaction in the whole study site (Pij). -Can be used as bipartite input.

    -
    - -
    -

    Examples

    - 
    data(RecruitNet)
-data(CanopyCover)
-Ventisquero_RNc <- comm_to_RN(RecruitNet, CanopyCover, site = "Ventisquero")
-Ventisquero_matrix <- RN_to_matrix(Ventisquero_RNc, weight = "fij")
-Ventisquero_matrix <- RN_to_matrix(Ventisquero_RNc, weight = "Tij")
-
    -
    -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/RecruitNet.html b/docs/reference/RecruitNet.html deleted file mode 100644 index e8e75e9..0000000 --- a/docs/reference/RecruitNet.html +++ /dev/null @@ -1,104 +0,0 @@ - -RecruitNet — RecruitNet • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    RecruitNet

    - Source: R/datasets.R - -
    - -
    -

    Plant-plant interactions global database

    -
    - -
    - 
    RecruitNet
    -
    - -
    -

    Format

    -

    An object of class data.frame with 135210 rows and 26 columns.

    -
    -
    -

    Source

    -

    https://doi.org/10.1002/ecy.3923

    -
    - -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/RecruitNetP-package.html b/docs/reference/RecruitNetP-package.html deleted file mode 100644 index ad80df5..0000000 --- a/docs/reference/RecruitNetP-package.html +++ /dev/null @@ -1,107 +0,0 @@ - -RecruitNetP: Analysis of Recruitment Networks — RecruitNetP-package • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    RecruitNetP: Analysis of Recruitment Networks

    - Source: R/RecruitnetP-package.R - -
    - -
    -

    Analyze recruitment networks and patterns of plant-plant interactions.

    -
    - - - -
    -

    Author

    -

    Maintainer: Francisco Rodriguez-Sanchez f.rodriguez.sanc@gmail.com (ORCID) [copyright holder]

    -

    Authors:

    - -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png deleted file mode 100644 index 17a358060aed2a86950757bbd25c6f92c08c458f..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1011 zcmeAS@N?(olHy`uVBq!ia0y~yV0-|=9Be?5+AI5}0x7m6Z+90U4Fo@(ch>_c&H|6f zVg?3oArNM~bhqvg0|WD9PZ!6KiaBo&GBN^{G%5UFpXcEKVvd5*5Eu=C0SJK)8A6*F U7`aXvEC5;V>FVdQ&MBb@00SN#Z2$lO diff --git a/docs/reference/comm_subset.html b/docs/reference/comm_subset.html deleted file mode 100644 index d703c53..0000000 --- a/docs/reference/comm_subset.html +++ /dev/null @@ -1,124 +0,0 @@ - -Subset sites — comm_subset • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    Subset sites

    - Source: R/comm_subset.R - -
    - -
    -

    This function extracts the information of a given local community and -stores it in a separate data frame. It can also extract more than one site.

    -
    - -
    - 
    comm_subset(dataset = NULL, site = NULL)
    -
    - -
    -

    Arguments

    -
    dataset
    -

    A data frame containing recruitment data.

    - - -
    site
    -

    character. Name of a study site within the dataset or -a character vector with the names of several study sites.

    - -
    -
    -

    Value

    - - -

    A data frame containing all the data from the desired community/ies

    -
    - -
    -

    Examples

    - 
    data(RecruitNet)
-
-Ventisquero <- comm_subset(RecruitNet, site = "Ventisquero")
-
-TwoSites <- comm_subset(RecruitNet, c("Ventisquero","Agadir"))
-
    -
    -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/comm_summary.html b/docs/reference/comm_summary.html deleted file mode 100644 index c48d2f7..0000000 --- a/docs/reference/comm_summary.html +++ /dev/null @@ -1,120 +0,0 @@ - -Summarise metadata of a community — comm_summary • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    Summarise metadata of a community

    - Source: R/comm_summary.R - -
    - -
    -

    This function provides a summary of the characteristics (i.e. metadata) of -a local community.

    -
    - -
    - 
    comm_summary(dataset = NULL)
    -
    - -
    -

    Arguments

    -
    dataset
    -

    A data frame for a single site (or a group of sites). -For example, a data frame created with comm_subset(). This function relies on -the variable names used in the original RecruitNet database, so it cannot be used -with other datasets unless they follow exactly the ordering and the names used in RecruitNet.

    - -
    -
    -

    Value

    - - -

    A data frame with summary information of the local community/ies.

    -
    - -
    -

    Examples

    - 
    All_sites_details <- comm_summary(RecruitNet)
-Ventisquero <- comm_subset(RecruitNet, site = "Ventisquero")
-Ventisquero_details <- comm_summary(Ventisquero)
-
    -
    -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/comm_to_RN.html b/docs/reference/comm_to_RN.html deleted file mode 100644 index 2642ff1..0000000 --- a/docs/reference/comm_to_RN.html +++ /dev/null @@ -1,137 +0,0 @@ - -Merge cover data with interaction data for a given site — comm_to_RN • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    Merge cover data with interaction data for a given site

    - Source: R/comm_to_RN.R - -
    - -
    -

    This function returns a data frame that merges the information from the -recruitment network and species cover for a local community. -It is a shortcut to directly obtain the data for a local community -from the RecruitNet dataset.

    -
    - -
    - 
    comm_to_RN(RN_data = NULL, cover_data = NULL, site = NULL)
    -
    - -
    -

    Arguments

    -
    RN_data
    -

    data set where recruitment networks are stored. -Usually, it will be "RecruitNet" (or the name assigned to the dataset when it was imported).

    - - -
    cover_data
    -

    data set where cover data is stored. -Usually, it will be "CanopyCover" (or the name assigned to the dataset -"CanopyCover.csv" when it was imported).

    - - -
    site
    -

    name of the study site.

    - -
    -
    -

    Value

    - - -

    A data.frame containing 7 columns with all the information needed -for the basic analysis of recruitment networks and canopy-recruit interactions: -canopy species (Canopy), recruit species (Recruit), recruitment frequency (fij), -Tij = TODO, -Pij = TODO, -cover of the canopy (cj) and cover of the recruit (ci).

    -
    - -
    -

    Examples

    - 
    data(RecruitNet)
-data(CanopyCover)
-Ventisquero_RNc <- comm_to_RN(RecruitNet, CanopyCover, site = "Ventisquero")
-
    -
    -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/download_RN.html b/docs/reference/download_RN.html deleted file mode 100644 index 83889f6..0000000 --- a/docs/reference/download_RN.html +++ /dev/null @@ -1,127 +0,0 @@ - -Download RecruitNet database — download_RN • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    Download RecruitNet database

    - Source: R/download_RN.R - -
    - -
    -

    Download latest version of the RecruitNet database (Verdú et al. 2023, -doi:10.1002/ecy.3923 -).

    -
    - -
    - 
    download_RN(path = getwd(), destfile = "RN.zip", unzip = TRUE)
    -
    - -
    -

    Arguments

    -
    path
    -

    character. Path to folder where the RecruitNet database should be saved.

    - - -
    destfile
    -

    character. Name of the zip file to be saved.

    - - -
    unzip
    -

    Logical. Uncompress the zip file? Default is TRUE.

    - -
    -
    -

    Value

    - - -

    A zip file or two CSV files, depending if unzip is TRUE.

    -
    - -
    -

    Examples

    - 
    if (FALSE) { # interactive()
-download_RN()
-}
-
    -
    -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/index.html b/docs/reference/index.html deleted file mode 100644 index 757e14a..0000000 --- a/docs/reference/index.html +++ /dev/null @@ -1,126 +0,0 @@ - -Function reference • RecruitNetP - - -
    -
    - - - -
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    Reference

    -
    - - - - - - - - - - - - - - - - - - - - -
    -

    All functions

    -

    -
    -

    CanopyCover

    -

    CanopyCover

    -

    RN_to_matrix()

    -

    Function RN_matrixForm

    -

    RecruitNet

    -

    RecruitNet

    -

    comm_subset()

    -

    Subset sites

    -

    comm_summary()

    -

    Summarise metadata of a community

    -

    comm_to_RN()

    -

    Merge cover data with interaction data for a given site

    -

    download_RN()

    -

    Download RecruitNet database

    -

    link_completeness()

    -

    Link_completeness

    -

    sign_net()

    -

    Association significance testing

    - - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/link_completeness.html b/docs/reference/link_completeness.html deleted file mode 100644 index dd58df9..0000000 --- a/docs/reference/link_completeness.html +++ /dev/null @@ -1,142 +0,0 @@ - -Link_completeness — link_completeness • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    Link_completeness

    - Source: R/link_completeness.R - -
    - -
    -

    Observed and estimated number of links, and link completeness. -Methods based in Chao et al. (2014) as implemented in R package iNEXT.

    -
    - -
    - 
    link_completeness(
-  dataset = NULL,
-  site = NULL,
-  type = c("incidence", "abundance")
-)
    -
    - -
    -

    Arguments

    -
    dataset
    -

    The name of the data set where recruitment networks are stored. -Usually, it will be "RecruitNet" (or the name assigned to the data set when -it was imported). At least, the data set must contain columns named -"Study_site", "Plot", "Canopy", "Recruit" and "Frequency". -In data sets containing a single site or a single plot, -the corresponding columns must be included anyway.

    - - -
    site
    -

    The name of a study site.

    - - -
    type
    -

    Can take two values: "incidence" or "abundance". -Estimates based on incidence data are recommended, but can only be obtained -if your data set is structured in multiple plots. Alternatively, for data -collected in a single plot, estimates can be based only on abundance data.

    - -
    -
    -

    Value

    - - -

    A data frame TODO

    -
    - -
    -

    Examples

    - 
    if (FALSE) { # interactive()
-
-link_completeness(RecruitNet, "Ventisquero", "incidence")
-link_completeness(RecruitNet, "Laxe", "abundance")
-link_completeness(RecruitNet, "Laxe", "incidence") # Issues a warning
-link_completeness(RecruitNet, "LosReyes", "abundance")
-link_completeness(RecruitNet, "LosReyes", "incidence") #Issues an error
-}
-
    -
    -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/sign_net.html b/docs/reference/sign_net.html deleted file mode 100644 index c2a19ce..0000000 --- a/docs/reference/sign_net.html +++ /dev/null @@ -1,159 +0,0 @@ - -Association significance testing — sign_net • RecruitNetP - - -
    -
    - - - -
    -
    -
    -

    Association significance testing

    - Source: R/sign_net.R - -
    - -
    -

    Generate a list with four elements, a data base with the significance tests -of association of each interaction (in rows) and three matrices of interaction -of positive, negative and neutral interactions, from a study site (for a single sites)

    -
    - -
    - 
    sign_net(inter_data, cover_data, site, type)
    -
    - -
    -

    Arguments

    -
    inter_data
    -

    data frame with at least four columns: -Study_site (unique name of the study site), -Recruit (species of the recruit), -Canopy (species of the canopy, or "Open"), and -Frequency (number of recruits of that species observed under that canopy species -in any plot, with "Open" representing recruits observed without a canopy species).

    - - -
    cover_data
    -

    data frame with at least five columns: -Study_site (unique name of the study site), -Plot (unique name of each plot within a study site), -Canopy (species of the canopy), -Cover (percentage of cover of that species in that plot), and -Sampled_distance_or_area (total area of that plot or length in the case of transects).

    - - -
    site
    -

    is the name of the Study_site

    - - -
    type
    -

    is a character that identifies which hypothesis is being tested with three options:

    1. "by_pairwise_interaction": whether the number of recuits of each recruit species under each canopy species is significantly different from -the number observed in the open, considering the percentage of cover of that canopy species and open area (i.e.bare ground), respectively.

      2. -

    2. "by_recruit_sp": whether the number of recuits of each recruit species under any canopy species (all together) is significantly different from -the number observed in the open, considering the percentage of cover of all canopy species together and open area (i.e.bare ground), respectively.

      3. -

    3. "by_canopy_sp": whether the number of recuits of any recruit species (all together) under a given canopy species is significantly different from -the number observed in the open, considering the percentage of cover of that canopy species and open area (i.e.bare ground), respectively

      4. -
    - -
    -
    -

    Value

    - - -

    a list with four elements. The first element data frame with the -same structure as the input with three additional columns: -int_p (p-value of the binomial test of association), -int_sign (the sign of the association, being Posive (or Negative) if the -association is stronger (or weaker) than expected by the percentage cover -of Canopy and Open, and Neutral if there is not enought power to conduct the test, -stdres (standarized resdual quantifying the difference between the observed -and expected values), and -testability (indicating whether the sample size allow to conduct or not the test -being Non-testable those rows in which int_sign = Neutral). -The second, third and fourth elements of teh list are the matrices of only positive, -negative and neutral ( testable) associations based on the database generated in the first element of the list.

    -
    - -
    -

    Examples

    - 
    if (FALSE) { # interactive()
-
-sign_vest <- sign_net(RecruitNet, CanopyCover, "Ventisquero", "by_pairwise_interaction" )
-}
-
    -
    -
    - -
    - - -
    -

    Developed by Alicia Montesinos-Navarro, José Luis Garrido, Miguel Verdú, Julio M. Alcántara, Francisco Rodriguez-Sanchez.

    -
    - -
    -

    Site built with pkgdown 2.0.9.

    -
    - -
    - - - - - - - - diff --git a/docs/sitemap.xml b/docs/sitemap.xml deleted file mode 100644 index 2ba91c7..0000000 --- a/docs/sitemap.xml +++ /dev/null @@ -1,60 +0,0 @@ - - - - /404.html - - - /LICENSE.html - - - /articles/Completeness-of-the-recruitment-network.html - - - /articles/The-RecruitNet-database.html - - - /articles/index.html - - - /authors.html - - - /index.html - - - /news/index.html - - - /reference/CanopyCover.html - - - /reference/RN_to_matrix.html - - - /reference/RecruitNet.html - - - /reference/RecruitNetP-package.html - - - /reference/comm_subset.html - - - /reference/comm_summary.html - - - /reference/comm_to_RN.html - - - /reference/download_RN.html - - - /reference/index.html - - - /reference/link_completeness.html - - - /reference/sign_net.html - - From b488042976a992cbfd17f93ebdd9d0429de269a3 Mon Sep 17 00:00:00 2001 From: Francisco Rodriguez-Sanchez Date: Wed, 18 Feb 2026 17:02:56 +0100 Subject: [PATCH 2/3] update GHA --- .github/workflows/R-CMD-check.yaml | 51 +++++++++++++++++++++++ .github/workflows/pkgdown.yaml | 49 ++++++++++++++++++++++ .github/workflows/test-coverage.yaml | 62 ++++++++++++++++++++++++++++ _pkgdown.yml | 4 ++ 4 files changed, 166 insertions(+) create mode 100644 .github/workflows/R-CMD-check.yaml create mode 100644 .github/workflows/pkgdown.yaml create mode 100644 .github/workflows/test-coverage.yaml create mode 100644 _pkgdown.yml diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml new file mode 100644 index 0000000..562fe0f --- /dev/null +++ b/.github/workflows/R-CMD-check.yaml @@ -0,0 +1,51 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + +name: R-CMD-check.yaml + +permissions: read-all + +jobs: + R-CMD-check: + runs-on: ${{ matrix.config.os }} + + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + + strategy: + fail-fast: false + matrix: + config: + - {os: macos-latest, r: 'release'} + - {os: windows-latest, r: 'release'} + - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} + - {os: ubuntu-latest, r: 'release'} + - {os: ubuntu-latest, r: 'oldrel-1'} + + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + R_KEEP_PKG_SOURCE: yes + + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::rcmdcheck + needs: check + + - uses: r-lib/actions/check-r-package@v2 + with: + upload-snapshots: true + build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")' diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml new file mode 100644 index 0000000..bfc9f4d --- /dev/null +++ b/.github/workflows/pkgdown.yaml @@ -0,0 +1,49 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + release: + types: [published] + workflow_dispatch: + +name: pkgdown.yaml + +permissions: read-all + +jobs: + pkgdown: + runs-on: ubuntu-latest + # Only restrict concurrency for non-PR jobs + concurrency: + group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + permissions: + contents: write + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::pkgdown, local::. + needs: website + + - name: Build site + run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) + shell: Rscript {0} + + - name: Deploy to GitHub pages 🚀 + if: github.event_name != 'pull_request' + uses: JamesIves/github-pages-deploy-action@v4.5.0 + with: + clean: false + branch: gh-pages + folder: docs diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml new file mode 100644 index 0000000..0ab748d --- /dev/null +++ b/.github/workflows/test-coverage.yaml @@ -0,0 +1,62 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + +name: test-coverage.yaml + +permissions: read-all + +jobs: + test-coverage: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::covr, any::xml2 + needs: coverage + + - name: Test coverage + run: | + cov <- covr::package_coverage( + quiet = FALSE, + clean = FALSE, + install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package") + ) + print(cov) + covr::to_cobertura(cov) + shell: Rscript {0} + + - uses: codecov/codecov-action@v5 + with: + # Fail if error if not on PR, or if on PR and token is given + fail_ci_if_error: ${{ github.event_name != 'pull_request' || secrets.CODECOV_TOKEN }} + files: ./cobertura.xml + plugins: noop + disable_search: true + token: ${{ secrets.CODECOV_TOKEN }} + + - name: Show testthat output + if: always() + run: | + ## -------------------------------------------------------------------- + find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true + shell: bash + + - name: Upload test results + if: failure() + uses: actions/upload-artifact@v4 + with: + name: coverage-test-failures + path: ${{ runner.temp }}/package diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..0a33daf --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,4 @@ +url: https://ecologyr.github.io/RecruitNetP/ +template: + bootstrap: 5 + From d87b685c78e8ca6b9950134d40cee88144401e19 Mon Sep 17 00:00:00 2001 From: Francisco Rodriguez-Sanchez Date: Wed, 18 Feb 2026 17:04:45 +0100 Subject: [PATCH 3/3] delete old files --- R/RN_to_matrix.R | 45 ------- R/RecruitnetP-package.R | 6 - R/aggr_RN.R | 47 ------- R/aggr_cover.R | 26 ---- R/associndex.R | 123 ------------------ R/canopy_level.R | 82 ------------ R/chisq.test2.R | 155 ----------------------- R/comm_summary.R | 109 ---------------- R/comm_to_RN.R | 36 ------ R/datasets.R | 13 -- R/funtopol.R | 147 ---------------------- R/globals.R | 4 - R/link_completeness.R | 129 ------------------- R/merge_RN_cover.R | 66 ---------- R/plot_to_site.R | 159 ----------------------- R/pre_asocindex.R | 131 ------------------- R/recruit_level.R | 64 ---------- R/sig_test.R | 138 -------------------- R/sign_net.R | 103 --------------- R/site_level.R | 91 -------------- man/CanopyCover.Rd | 19 --- man/RN_to_matrix.Rd | 39 ------ man/RecruitNet.Rd | 19 --- man/RecruitNetP-package.Rd | 31 ----- man/associndex.Rd | 76 ----------- man/comm_subset.Rd | 28 ----- man/comm_summary.Rd | 26 ---- man/comm_to_RN.Rd | 37 ------ man/download_RN.Rd | 27 ---- man/funtopol.Rd | 56 --------- man/link_completeness.Rd | 44 ------- man/sign_net.Rd | 60 --------- tests/testthat/test-RN_to_matrix.R | 161 ------------------------ tests/testthat/test-comm_summary.R | 31 ----- tests/testthat/test-comm_to_RN.R | 35 ------ tests/testthat/test-link_completeness.R | 23 ---- 36 files changed, 2386 deletions(-) delete mode 100644 R/RN_to_matrix.R delete mode 100644 R/RecruitnetP-package.R delete mode 100644 R/aggr_RN.R delete mode 100644 R/aggr_cover.R delete mode 100644 R/associndex.R delete mode 100644 R/canopy_level.R delete mode 100644 R/chisq.test2.R delete mode 100644 R/comm_summary.R delete mode 100644 R/comm_to_RN.R delete mode 100644 R/datasets.R delete mode 100644 R/funtopol.R delete mode 100644 R/globals.R delete mode 100644 R/link_completeness.R delete mode 100644 R/merge_RN_cover.R delete mode 100644 R/plot_to_site.R delete mode 100644 R/pre_asocindex.R delete mode 100644 R/recruit_level.R delete mode 100644 R/sig_test.R delete mode 100644 R/sign_net.R delete mode 100644 R/site_level.R delete mode 100644 man/CanopyCover.Rd delete mode 100644 man/RN_to_matrix.Rd delete mode 100644 man/RecruitNet.Rd delete mode 100644 man/RecruitNetP-package.Rd delete mode 100644 man/associndex.Rd delete mode 100644 man/comm_subset.Rd delete mode 100644 man/comm_summary.Rd delete mode 100644 man/comm_to_RN.Rd delete mode 100644 man/download_RN.Rd delete mode 100644 man/funtopol.Rd delete mode 100644 man/link_completeness.Rd delete mode 100644 man/sign_net.Rd delete mode 100644 tests/testthat/test-RN_to_matrix.R delete mode 100644 tests/testthat/test-comm_summary.R delete mode 100644 tests/testthat/test-comm_to_RN.R delete mode 100644 tests/testthat/test-link_completeness.R diff --git a/R/RN_to_matrix.R b/R/RN_to_matrix.R deleted file mode 100644 index 3567700..0000000 --- a/R/RN_to_matrix.R +++ /dev/null @@ -1,45 +0,0 @@ -#' Function RN_matrixForm -#' -#' For some analyses, the data must be stored as a matrix rather than data frame. -#' The next function takes a RN stored as data frame and transforms it into a matrix. -#' The output matrix can be used input for bipartite package. -#' -#' @param RNdata A data frame generated with [comm_subset()] or a data.frame -#' containing columns with all the information needed for the basic analysis of -#' recruitment networks: canopy species (canopy), recruit species (recruit), -#' cover of the canopy (cj) and recruit (ci), -#' and columns with possible weighting variables. -#' -#' @param weight The name of the column of the data frame to be used as weight -#' variable for the links. -#' -#' @return A matrix object with the species names as row (recruit species) and -#' column names (canopy species), and cells indicating the chosen weight for -#' each interaction. For example, the output of function local_RN provides the -#' number of recruits of species i interacting with canopy species j (fij), -#' the number of plots (i.e. incidence) where the interaction has been found (Tij), -#' and the binary presence/absence of the interaction in the whole study site (Pij). -#' Can be used as bipartite input. -#' -#' @export -#' -#' @examples -#' data(RecruitNet) -#' data(CanopyCover) -#' Ventisquero_RNc <- comm_to_RN(RecruitNet, CanopyCover, site = "Ventisquero") -#' Ventisquero_matrix <- RN_to_matrix(Ventisquero_RNc, weight = "fij") -#' Ventisquero_matrix <- RN_to_matrix(Ventisquero_RNc, weight = "Tij") - -RN_to_matrix <- function(RNdata = NULL, weight = NULL){ - - list_Canopy <- sort(unique(RNdata$Canopy)) - list_Recruit <- sort(unique(RNdata$Recruit)) - Num_canopy <- length(list_Canopy) - Num_recruit <- length(list_Recruit) - RNmat <- RNdata[[weight]] - dim(RNmat) <- c(Num_recruit, Num_canopy) - colnames(RNmat) <- list_Canopy - rownames(RNmat) <- list_Recruit - return(RNmat) - -} diff --git a/R/RecruitnetP-package.R b/R/RecruitnetP-package.R deleted file mode 100644 index a65cf64..0000000 --- a/R/RecruitnetP-package.R +++ /dev/null @@ -1,6 +0,0 @@ -#' @keywords internal -"_PACKAGE" - -## usethis namespace: start -## usethis namespace: end -NULL diff --git a/R/aggr_RN.R b/R/aggr_RN.R deleted file mode 100644 index 5a62d83..0000000 --- a/R/aggr_RN.R +++ /dev/null @@ -1,47 +0,0 @@ -#' Prepare recruitment data -#' -#' @description -#' -#' This function makes a data frame with the basic data needed to build the -#' recruitment network: the identity of canopy and recruit species, and several -#' possible weighting variables for the interactions: fij is the number of -#' recruits of species i interacting with canopy species j summed across plots, -#' Tij is the number of plots (i.e. incidence) where the interaction has been found, -#' and Pij is the presence/absence of the interaction in the whole study site. -#' -#' The output contains all possible pairs of interacting species, -#' including those that were not observed, which are assigned weight = 0. -#' -#' @param raw_RN A data frame containing, at least, the columns: -#' "Canopy", "Recruit" and "Frequency". -#' -#' @return A data frame containing the data to build a recruitment network -#' of a local community: "canopy", "recruit", "fij", "Tij" and "Pij". -#' -#' @noRd -#' -#' @examples -#' Ventisquero_raw <- comm_subset(RecruitNet, site = "Ventisquero") -#' Ventisquero_RN <- aggr_RN(Ventisquero_raw) - -aggr_RN <- function(raw_RN) { - - # Sum the number of recruits per interaction across plots - RN <- stats::aggregate(Frequency ~ Canopy*Recruit, data = raw_RN, FUN = sum) - colnames(RN) <- c("Canopy", "Recruit", "fij") - RN$Tij <- stats::aggregate(Frequency ~ Canopy*Recruit, data=raw_RN, FUN = NROW)[[3]] - RN$Pij <- ifelse(RN$Tij==0,0,1) - RN$Canopy <- gsub("[[:space:]]", "_", RN$Canopy) - RN$Recruit <- gsub("[[:space:]]", "_", RN$Recruit) - - # Incorporate the unobserved interactions - species_list <- unique(c(RN$Canopy, RN$Recruit)) - df <- expand.grid(Canopy = species_list, Recruit = species_list) - RN <- merge(df, RN, all = TRUE) - RN[is.na(RN)] <- 0 - RN$Canopy <- as.character(RN$Canopy) - RN$Recruit <- as.character(RN$Recruit) - RN <- RN[which(RN$Recruit!="Open"),] # Remove Open from the Recruit species - return(RN) - -} diff --git a/R/aggr_cover.R b/R/aggr_cover.R deleted file mode 100644 index b1385c3..0000000 --- a/R/aggr_cover.R +++ /dev/null @@ -1,26 +0,0 @@ -#' Calculate aggregated species cover -#' -#' Calculate the aggregated (i.e. summed) cover of each species across -#' all the plots from a local community. -#' -#' @param dataset character. Name of the data set where cover data are stored. -#' Usually, it will be "CanopyCover" (or the name assigned to the -#' dataset "CanopyCover.csv" when it was imported). -#' @param site character. Name of a study site. -#' -#' @return A data frame containing the total cover of each species in a local community, -#' aggregated across plots. It has two columns: "Canopy", with the name of each species (irrespective -#' of being acting as canopies or recruits); and "abundance". -#' -#' @noRd -#' -#' @examples -#' Ventisquero_cover <- aggr_cover(CanopyCover, site = "Ventisquero") - -aggr_cover <- function(dataset, site) { - cover <- dataset[which(dataset$Study_site == site), ] - cover$abundance <- (cover$Cover / 100) * cover$Sampled_distance_or_area - cover <- stats::aggregate(abundance ~ Canopy, data = cover, sum) - return(cover) - -} diff --git a/R/associndex.R b/R/associndex.R deleted file mode 100644 index 691804b..0000000 --- a/R/associndex.R +++ /dev/null @@ -1,123 +0,0 @@ - - -#' Calculate interaction strength indices -#' -#' @param int_data data frame with at least four columns: -#' - Study_site (unique name of the study site), -#' - Recruit (species of the recruit), -#' - Canopy (species of the canopy, or "Open"), and -#' - Frequency (number of recruits of that species observed under that -#' canopy species in any plot, with "Open" representing recruits observed -#' without a canopy species). -#' If available, the dimensions of the study plots can be also included as -#' variables PlotdimX and PlotdimY. -#' @param cover_data data frame with at least five columns: -#' - Study_site (unique name of the study site), -#' - Plot (unique name of each plot within a study site), -#' - Canopy (species of the canopy), -#' - Cover (percentage of cover of that species in that plot), and -#' - Sampled_distance_or_area (total area of the plot). -#' @param area_sampled Total area surveyed in the study site (in m2). -#' @param thr_dens Maximum density considered as reasonable. For example, if a canopy species is rare -#' (e.g. 0.01 m2) but there is one sapling growing beneath, we would estimate a density of 100 saplings/m2 -#' for this interaction, what is likely an overestimate because it seems reasonable that increasing the -#' effort under this canopy species we would obtain a much lower density estimate. The threshold is set -#' by default to 100 recruits/m2. -#' -#' @return data frame with indices of pair-wise interaction strength and the data neeeded for their estimation. -#' Each row corresponds to each pair-wise interaction, and contains the following columns: -#' Recruit: recruit species. -#' Canopy: canopy species. -#' inter_ID: a unique identifier for each pair-wise interaction, combining Study_site, Recruit, and Canopy species. -#' Study_site: name of the study site. -#' Canopy_Freq: number of recruits found in the vicinity of the canopy species. -#' Open_Freq: number of recruits found in open spaces. -#' Canopy_cover: percentage of the total area sampled in the study site occupied by the canopy species. -#' Open_cover: percentage of the total area sampled in the study site occupied by Open spaces. -#' Freq_tot: sum of Canopy_Freq and Open_Freq (total number of recruits of the recruit species in the area sampled -#' in the study site. -#' RecrDens_canopy: density of recruits under the canopy species (recruits/m2). -#' RecrDens_open: density of recruits in open spaces (recruits/m2). -#' Max_Recr_Density: maximum recruitment density between under the canopy species vs in open spaces. -#' max_Recr: maximum recruitment density of the recruit species in the study site. -#' Ns: Normalized Neighbour Suitability index (Mingo, 2014). -#' NintC: commutative symmetry intensity index (Díaz-Sierra et al., 2017) -#' NintA:additive symmetry intensity index (Díaz-Sierra et al., 2017) -#' RII: Relative Interaction Index (Armas et al., 2004). -#' -#' @export -#' -#' @examples -#' data(RecruitNet) -#' data(CanopyCover) -#' int_data <- comm_subset(RecruitNet, site = "Ventisquero") -#' cover_data <- CanopyCover[CanopyCover$Study_site == "Ventisquero", ] -#' Ventisquero_int <- associndex(int_data, cover_data) -#' - -associndex <- function(int_data = NULL, - cover_data = NULL, - area_sampled = NULL, - thr_dens = 100) { - - if (is.null(area_sampled) & !"PlotdimX" %in% names(int_data)) { - stop("ERROR: sampled area cannot be calculated with the data available. You must enter a value in area_sampled or add variables PlotdimX and PlotdimY in your data.") - } - - if (is.null(area_sampled)) { - area_sampled = comm_summary(int_data)["Area sampled (m2)"][1,1] - } - - threshold_density <- thr_dens - - # Assemble the data - db_inter <- pre_asocindex(int_data, cover_data) - - # Incorporate density of recruitment (recruits/m2) under each canopy species and in open. - db_inter$RecrDens_canopy <- db_inter$Canopy_Freq/((db_inter$Canopy_cover/100)*area_sampled) - db_inter$RecrDens_open <- db_inter$Open_Freq/((db_inter$Open_cover/100)*area_sampled) - - # remove the interactions with estimated density above the threshold. - db_inter <- db_inter[which(db_inter$RecrDens_canopy 0) { - db_inter <- db_inter[-which(db_inter$Recruit %in% lack_dens), ] - } else { - db_inter - } - - db_inter$max_Recr <- db_inter$Recruit - for (i in 1:(dim(db_inter)[1])) { - db_inter$max_Recr[i] <- replace( - db_inter$max_Recr[i], - match(Recr_list, db_inter$max_Recr[i]), - max_rd$Max_Recr_Density[match(db_inter$max_Recr[i], max_rd$Recruit)] - ) - } - - db_inter <- utils::type.convert(db_inter, as.is = TRUE) - - # Calculate indices Ns, NintC, NintA and RII - db_inter$Ns <- (db_inter$RecrDens_canopy - db_inter$RecrDens_open)/db_inter$max_Recr - db_inter$NintC <- 2*(db_inter$RecrDens_canopy - db_inter$RecrDens_open)/((db_inter$RecrDens_canopy + db_inter$RecrDens_open)+abs(db_inter$RecrDens_canopy-db_inter$RecrDens_open)) - db_inter$NintA <- 2*(db_inter$RecrDens_canopy - db_inter$RecrDens_open)/((db_inter$RecrDens_open) + abs(db_inter$RecrDens_canopy-db_inter$RecrDens_open)) - db_inter$RII <- (db_inter$RecrDens_canopy - db_inter$RecrDens_open)/(db_inter$RecrDens_canopy + db_inter$RecrDens_open) - - removed <- names(db_inter) %in% c("Frequency") - db_inter <- db_inter[!removed] - return(db_inter) - -} diff --git a/R/canopy_level.R b/R/canopy_level.R deleted file mode 100644 index 562057f..0000000 --- a/R/canopy_level.R +++ /dev/null @@ -1,82 +0,0 @@ -#' Calculate the number of recruits under each canopy species -#' -#' Calculate in separate columns the total number of recruits (of any species) -#' under each species of canopy and in the open and its respective percentage -#' of cover (either for multiple or a single site). -#' -#' @param inter_data data frame with at least four columns: Study_site -#' (unique name of the study site), Recruit (species of the recruit), -#' Canopy (species of the canopy, or "Open"), and Frequency (number of recruits -#' of that species observed under that canopy species in any plot, -#' with "Open" representing recruits observed without a canopy species). -#' -#' @param cover_data data frame with at least five columns: -#' Study_site (unique name of the study site), -#' Plot (unique name of each plot within a study site), -#' Canopy (species of the canopy), -#' Cover (percentage of cover of that species in that plot), and -#' Sampled_distance_or_area (total area of that plot or length in the case of transects). -#' -#' @return data frame with one row per Canopy species in each site and nine columns: -#' inter_ID (a unique identifier for each pair-wise interaction, -#' combining Study_site, Recruit, and Canopy species), -#' Recruit(recruit species), -#' Canopy (canopy speices), -#' Study_site (name of the study site), -#' Canopy_Freq and Open_Freq with the number of recuits observed under that -#' canopy species or in the Open, respectively and -#' Canopy_cover and Open_cover, with the percentage of the total area sampled -#' in the Study_site occupied by that canopy species and Open respectively. -#' Freq_tot is the sum of Canopy_Freq and Open_Freq (total number of recruits of -#' that speices observed in the area sampled in the Study_site). -#' -#' @noRd -#' -#' @examples -#' db_sp_can <- canopy_level (RecruitNet, CanopyCover) - -canopy_level <- function(inter_data = RecruitNet, - cover_data = CanopyCover -) { - - options(dplyr.summarise.inform = FALSE) - - data <- inter_data - dbcover <- cover_data - - db_inter <- pre_asocindex(data, dbcover) - - db_sp_can <- data.frame( - db_inter |> - dplyr::group_by(Study_site, Canopy) |> - dplyr::summarise( - Canopy_Freq = sum(Canopy_Freq, na.rm = TRUE), - Open_cover = unique(Open_cover, na.rm = TRUE), - Canopy_cover = unique(Canopy_cover, na.rm = TRUE) - ) - ) - - Rec_open <- data.frame( - db_inter |> - dplyr::group_by(Study_site, Recruit) |> - dplyr::summarise(Open_Freq = unique(Open_Freq, na.rm = TRUE)) - ) - - Rec_open <- data.frame( - Rec_open |> - dplyr::group_by(Study_site) |> - dplyr::summarise(Open_Freq = sum(Open_Freq, na.rm = TRUE))) - - - db_sp_can <- merge(db_sp_can, Rec_open, by = "Study_site") - - db_sp_can <- db_sp_can[, c("Study_site" , - "Canopy" , - "Canopy_Freq", - "Open_Freq", - "Open_cover", - "Canopy_cover")] - - return(db_sp_can) - -} diff --git a/R/chisq.test2.R b/R/chisq.test2.R deleted file mode 100644 index c79cf85..0000000 --- a/R/chisq.test2.R +++ /dev/null @@ -1,155 +0,0 @@ -#' @param x a numeric vector or matrix. `x` and `y` can also both be factors. -#' @param y a numeric vector; ignored if `x` is a matrix. `x` and `y` can also both be factors. -#' @param correct a logical indicating whether to apply continuity correction in the case of a 2x2 table. Default is TRUE. -#' @param p a vector of probabilities of the same length as `x`, used for goodness-of-fit tests. Default is rep(1/length(x), length(x)). -#' @param rescale.p a logical indicating whether to rescale `p` to sum to 1. Default is FALSE. -#' @param simulate.p.value a logical indicating whether to compute p-values by Monte Carlo simulation. Default is FALSE. -#' @param B an integer specifying the number of replicates used in the Monte Carlo test. Default is 2000. -#' -#' @return A list with class "htest". -#' @noRd -#' @details -#' The original function chisq.test performs a chi-squared test of the null hypothesis that the -#' proportions in a one-way table (goodness-of-fit) or the counts in a two-way -#' table (contingency) follow a specified distribution. -#' chisq.test2 is a modified version of chisq.test function that optimices the use of its argument simulated.p.value =T -#' This version uses tabulate instead of table to create frequency counts -#' -#' @examples -#' ## Goodness-of-fit test -#' chisq.test(c(4, 6, 17, 16, 8, 9), p = c(1, 1, 1, 1, 1, 1)/6) - - -chisq.test2 <-function (x, y = NULL, correct = TRUE, p = rep(1/length(x), length(x)), - rescale.p = FALSE, simulate.p.value = FALSE, B = 2000) -{ - DNAME <- deparse(substitute(x)) - if (is.data.frame(x)) - x <- as.matrix(x) - if (is.matrix(x)) { - if (min(dim(x)) == 1L) - x <- as.vector(x) - } - if (!is.matrix(x) && !is.null(y)) { - if (length(x) != length(y)) - stop("'x' and 'y' must have the same length") - DNAME2 <- deparse(substitute(y)) - xname <- if (length(DNAME) > 1L || nchar(DNAME, "w") > - 30) - "" - else DNAME - yname <- if (length(DNAME2) > 1L || nchar(DNAME2, "w") > - 30) - "" - else DNAME2 - OK <- stats::complete.cases(x, y) - x <- factor(x[OK]) - y <- factor(y[OK]) - if ((nlevels(x) < 2L) || (nlevels(y) < 2L)) - stop("'x' and 'y' must have at least 2 levels") - x <- table(x, y) - names(dimnames(x)) <- c(xname, yname) - DNAME <- paste(paste(DNAME, collapse = "\n"), "and", - paste(DNAME2, collapse = "\n")) - } - if (any(x < 0) || anyNA(x)) - stop("all entries of 'x' must be nonnegative and finite") - if ((n <- sum(x)) == 0) - stop("at least one entry of 'x' must be positive") - if (simulate.p.value) { - setMETH <- function() METHOD <<- paste(METHOD, "with simulated p-value\n\t (based on", - B, "replicates)") - almost.1 <- 1 - 64 * .Machine$double.eps - } - if (is.matrix(x)) { - METHOD <- "Pearson's Chi-squared test" - nr <- as.integer(nrow(x)) - nc <- as.integer(ncol(x)) - if (is.na(nr) || is.na(nc) || is.na(nr * nc)) - stop("invalid nrow(x) or ncol(x)", domain = NA) - sr <- rowSums(x) - sc <- colSums(x) - E <- outer(sr, sc)/n - v <- function(r, c, n) c * r * (n - r) * (n - c)/n^3 - V <- outer(sr, sc, v, n) - dimnames(E) <- dimnames(x) - if (simulate.p.value && all(sr > 0) && all(sc > 0)) { - setMETH() - tmp <- .Call(C_chisq_sim, sr, sc, B, E) - STATISTIC <- sum(sort((x - E)^2/E, decreasing = TRUE)) - PARAMETER <- NA - PVAL <- (1 + sum(tmp >= almost.1 * STATISTIC))/(B + - 1) - } - else { - if (simulate.p.value) - warning("cannot compute simulated p-value with zero marginals") - if (correct && nrow(x) == 2L && ncol(x) == 2L) { - YATES <- min(0.5, abs(x - E)) - if (YATES > 0) - METHOD <- paste(METHOD, "with Yates' continuity correction") - } - else YATES <- 0 - STATISTIC <- sum((abs(x - E) - YATES)^2/E) - PARAMETER <- (nr - 1L) * (nc - 1L) - PVAL <- stats::pchisq(STATISTIC, PARAMETER, lower.tail = FALSE) - } - } - else { - if (length(dim(x)) > 2L) - stop("invalid 'x'") - if (length(x) == 1L) - stop("'x' must at least have 2 elements") - if (length(x) != length(p)) - stop("'x' and 'p' must have the same number of elements") - if (any(p < 0)) - stop("probabilities must be non-negative.") - if (abs(sum(p) - 1) > sqrt(.Machine$double.eps)) { - if (rescale.p) - p <- p/sum(p) - else stop("probabilities must sum to 1.") - } - METHOD <- "Chi-squared test for given probabilities" - E <- n * p - V <- n * p * (1 - p) - STATISTIC <- sum((x - E)^2/E) - names(E) <- names(x) - # if (simulate.p.value) { - # setMETH() - # nx <- length(x) - # sm <- matrix(sample.int(nx, B * n, TRUE, prob = p), - # nrow = n) - # ss <- apply(sm, 2L, function(x, E, k) { - # sum((table(factor(x, levels = 1L:k)) - E)^2/E) - # }, E = E, k = nx) - # PARAMETER <- NA - # PVAL <- (1 + sum(ss >= almost.1 * STATISTIC))/(B + - # 1) - # } - - if (simulate.p.value) { - setMETH() - nx <- length(x) - sm <- matrix(sample.int(nx, B * n, TRUE, prob = p), nrow = n) - ss <- apply(sm, 2L, function(col) { - freq_table <- tabulate(col, nbins = nx) - sum((freq_table - E)^2 / E) - }) - PARAMETER <- NA - PVAL <- (1 + sum(ss >= almost.1 * STATISTIC)) / (B + 1) - } - - else { - PARAMETER <- length(x) - 1 - PVAL <- stats::pchisq(STATISTIC, PARAMETER, lower.tail = FALSE) - } - } - names(STATISTIC) <- "X-squared" - names(PARAMETER) <- "df" - if (any(E < 5) && is.finite(PARAMETER)) - warning("Chi-squared approximation may be incorrect") - structure(list(statistic = STATISTIC, parameter = PARAMETER, - p.value = PVAL, method = METHOD, data.name = DNAME, observed = x, - expected = E, residuals = (x - E)/sqrt(E), stdres = (x - - E)/sqrt(V)), class = "htest") -} diff --git a/R/comm_summary.R b/R/comm_summary.R deleted file mode 100644 index b3cdc47..0000000 --- a/R/comm_summary.R +++ /dev/null @@ -1,109 +0,0 @@ -#' Summarise metadata of a community -#' -#' This function provides a summary of the characteristics (i.e. metadata) of -#' a local community. -#' -#' @param dataset A data frame for a single site (or a group of sites). -#' For example, a data frame created with [comm_subset()]. This function relies on -#' the variable names used in the original RecruitNet database, so it cannot be used -#' with other datasets unless they follow exactly the ordering and the names used in RecruitNet. -#' -#' -#' @return A data frame with summary information of the local community/ies. -#' -#' @export -#' -#' @examples -#' All_sites_details <- comm_summary(RecruitNet) -#' Ventisquero <- comm_subset(RecruitNet, site = "Ventisquero") -#' Ventisquero_details <- comm_summary(Ventisquero) - - -comm_summary <- function(dataset = NULL) { - - dfList <- list() - - for (i in 1:length(unique(dataset$Study_site))) { - data <- comm_subset(dataset, unique(dataset$Study_site)[i]) - - # Plots data - n_plots <- length(unique(data$Plot)) - plotDim <- data$PlotdimX[1] * data$PlotdimY[1] - areaSampled <- plotDim * n_plots - - # Nodes data - isOpen <- ifelse(is.element('Open', data$Standardized_Canopy) == TRUE, "Yes", "No") - n_nodes <- length(unique(c( - data$Standardized_Canopy, data$Standardized_Recruit - ))) - n_sp <- ifelse(isOpen == "Yes", n_nodes - 1, n_nodes) - num_woody <- length(which(unique(data.frame( - c(data$LifeHabit_Canopy, data$LifeHabit_Recruit), - c(data$Standardized_Canopy, data$Standardized_Recruit) - ), margin = 1) == "W")) - num_herbs <- length(which(unique(data.frame( - c(data$LifeHabit_Canopy, data$LifeHabit_Recruit), - c(data$Standardized_Canopy, data$Standardized_Recruit) - ), margin = 1) == "H")) - num_others <- n_sp - num_woody - num_herbs - - # Function output - df <- data.frame( - c( - data$Study_site[1], - data$Country[1], - data$Latitude[1], - data$Longitude[1], - data$Sampling_date[1], - data$Site_responsible[1], - data$Biome[1], - data$Vegetation_type[1], - data$Community[1], - data$Successional_stage[1], - data$Disturbance[1], - data$Sampling_method[1], - n_plots, - plotDim, - areaSampled, - n_sp, - isOpen, - num_woody, - num_herbs, - num_others - ) - ) - colnames(df) <- c("Value") - rownames(df) <- c( - "Local Community", - "Country", - "Latitude", - "Longitude", - "Year of sampling", - "Site responsible", - "Biome", - "Vegetation", - "Plant Community", - "Successional stage", - "Disturbance", - "Sampling method", - "Number of plots", - "Plot area (m2)", - "Area sampled (m2)", - "Number of plant species", - "Contains Open node", - "Number of woody species", - "Number of herb species", - "Number of other types" - ) - - dfList[[i]] <- t(df) - - } - - dfAll <- as.data.frame(do.call(rbind, dfList)) - - dfAll <- utils::type.convert(dfAll, as.is = TRUE) - - return(dfAll) - -} diff --git a/R/comm_to_RN.R b/R/comm_to_RN.R deleted file mode 100644 index a854d49..0000000 --- a/R/comm_to_RN.R +++ /dev/null @@ -1,36 +0,0 @@ -#' Merge cover data with interaction data for a given site -#' -#' This function returns a data frame that merges the information from the -#' recruitment network and species cover for a local community. -#' It is a shortcut to directly obtain the data for a local community -#' from the RecruitNet dataset. -#' -#' @param RN_data data set where recruitment networks are stored. -#' Usually, it will be "RecruitNet" (or the name assigned to the dataset when it was imported). -#' -#' @param cover_data data set where cover data is stored. -#' Usually, it will be "CanopyCover" (or the name assigned to the dataset -#' "CanopyCover.csv" when it was imported). -#' -#' @param site name of the study site. -#' -#' @return A data.frame containing 7 columns with all the information needed -#' for the basic analysis of recruitment networks and canopy-recruit interactions: -#' canopy species (Canopy), recruit species (Recruit), recruitment frequency (fij), -#' Tij = TODO, -#' Pij = TODO, -#' cover of the canopy (cj) and cover of the recruit (ci). -#' -#' @export -#' -#' @examples -#' data(RecruitNet) -#' data(CanopyCover) -#' Ventisquero_RNc <- comm_to_RN(RecruitNet, CanopyCover, site = "Ventisquero") - -comm_to_RN <- function(RN_data = NULL, cover_data = NULL, site = NULL) { - - merge_RN_cover(aggr_RN(comm_subset(RN_data, site)), - aggr_cover(cover_data, site)) - -} diff --git a/R/datasets.R b/R/datasets.R deleted file mode 100644 index 1e0f4d1..0000000 --- a/R/datasets.R +++ /dev/null @@ -1,13 +0,0 @@ -#' RecruitNet -#' -#' Plant-plant interactions global database -#' -#' @source https://doi.org/10.1002/ecy.3923 -"RecruitNet" - -#' CanopyCover -#' -#' Canopy cover of plant-plant interactions global database -#' -#' @source https://doi.org/10.1002/ecy.3923 -"CanopyCover" diff --git a/R/funtopol.R b/R/funtopol.R deleted file mode 100644 index 8836ef4..0000000 --- a/R/funtopol.R +++ /dev/null @@ -1,147 +0,0 @@ - - -#' Functional topology of a recruitment network. -#' -#' @description -#' Alcantara & Rey (2012) derived a qualitative way to infer potential species -#' persistence from their position in the RNs based on a combination of non-negative -#' matrix theory and graph theory. Basically, directed unipartite graphs can be -#' unambiguously dissected into 'SCCs'. SCCs are the largest possible subgroups of -#' nodes connected so that all the nodes in a subgroup can be reached from all others -#' following the directions of the links. In the case of RNs, we can define five -#' types of SCCs which play different functional roles: -#' -#' - The "core" of the network is the SCC formed by the largest number of species. -#' All species in the core must recruit, at least, in the vicinity of another core -#' species, and allow the recruitment of at least another core species. -#' - “Satellites” are non-core species that can be reached from some core species, -#' following the direction of the arrows. For example, a satellite species is one -#' that recruits in the vicinity of some core species but that does not show -#' recruitment of any species in its vicinity. -#' - "Disturbance-dependent transients" are species that can be reached from the open -#' node but not from core or satellite species (i.e. for example, species that only -#' recruit away from established plants). -#' - "Strict transients" are species that cannot be reached from any other node (i.e. -#' those that do not recruit in the studied local assemblage). -#' -#' Assuming that the dynamics of the system is linear and time-invariant (LTI dynamics), -#' like in Markov models (Horn, 1975, Siles et al., 2008), only core and satellite -#' species will persist in equilibrium in the absence of disturbance. When the dynamics -#' are non-linear, this result cannot be guaranteed to hold, but simulations have shown -#' that the probability of persistence is higher and the time to extinction is longer -#' for core and satellite than for transient species (Alcantara et al., 2017). The sum -#' of core and satellite species is a qualitative approximation to the number of species -#' that can potentially persist. -#' -#' @param int_data Data frame with columns named "Canopy" and "Recruit" identifying -#' observed interactions (i.e. interactions with frequency > 0). -#' -#' @return The function returns two outputs: a data frame with the numeric summary of the -#' functional structure and a list containing the lists of species of each functional -#' type (core, satellite, strict transients and disturbance-dependent transients). -#' -#' @export -#' -#' @examples -#' data(RecruitNet) -#' int_data <- comm_subset(RecruitNet, site = "Ventisquero") -#' Ventisquero_funtopol <- funtopol(int_data) -#' Ventisquero_funtopol$Descriptors -#' Ventisquero_funtopol$Functional_classification - - -funtopol <- function(int_data){ - - if (!"Canopy" %in% names(int_data)) stop("ERROR: your interactions data lacks a column named: Canopy") - if (!"Recruit" %in% names(int_data)) stop("ERROR: your interactions data lacks a column named: Recruit") - if (!"Open" %in% int_data$Canopy) stop("ERROR: your data does not contain a node named Open or it is spelled differently.") - - int_data <- int_data[c("Canopy", "Recruit")] - g <- igraph::graph_from_data_frame(int_data, directed = TRUE) - g <- igraph::simplify(g, remove.multiple = TRUE, remove.loops = FALSE) - NEdges <- igraph::gsize(g) - NNodes <- igraph::gorder(g) - CDirected <- NEdges/(NNodes*(NNodes - 1)) - SCCs <- igraph::components(g, mode = "strong") - numSCCs <- SCCs$no - numNTSCCs <- sum(SCCs$csize > 1) - coreSize <- max(SCCs$csize) - SCC_memb <- SCCs$membership - SCC_memb <- as.data.frame(SCC_memb) - SCC_subgraphs <- igraph::decompose(g, mode = "strong") # Makes a subgraph of each SCC - IDcore <- match(coreSize, SCCs$csize) # locates the position of the core in the list of SCCs - MembersCore <- igraph::V(SCC_subgraphs[[IDcore]])$name # List of the species in the core - IDOpen <- SCC_memb$SCC_memb[match("Open", row.names(SCC_memb))] # Locate the position of the "open" node in the list of SCCs - outReachFromOpen <- names(igraph::subcomponent(g, "Open", "out")[-1]) # List of nodes reachable from the "open" - outReachFromCore <- vector("list", coreSize) # List of nodes reachable from core nodes - for (i in 1:coreSize) { - outReachFromCore[[i]] <- igraph::subcomponent(g, MembersCore[i], mode = "out") - } - a <- unlist(outReachFromCore) - a <- unique(names(a)) - MembersSatellites <- setdiff(a, MembersCore) - MembersTransients <- setdiff(igraph::V(g)$name,c(MembersCore,MembersSatellites)) - MembersTransients <- MembersTransients[!MembersTransients == "Open"] - MembersStrictTransients <- setdiff(MembersTransients, outReachFromOpen) - MembersDdTransients <- setdiff(MembersTransients, MembersStrictTransients) - numSat <- length(MembersSatellites) - numTransAll <- length(MembersTransients) - numDdTrans <- length(MembersDdTransients) - numStrictTrans <- length(MembersStrictTransients) - propCore <- coreSize/(NNodes - 1) - propSat <- numSat/(NNodes - 1) - propTrans <- numTransAll/(NNodes - 1) - propStrTrans <- numStrictTrans/(NNodes - 1) - propDdTrans <- numDdTrans/(NNodes - 1) - persistence <- propCore + propSat - - # Function output - - df <- data.frame( - c(NNodes, - NEdges, - CDirected, - numNTSCCs, - coreSize, - propCore, - numSat, - propSat, - numDdTrans, - propDdTrans, - numStrictTrans, - propStrTrans, - persistence) - ) - colnames(df) <- c("Value") - rownames(df) <- c( - "Num. nodes", - "Num. edges", - "Connectance", - "Num. non-trivial SCCs", - "Num. core species", - "Prop. core species", - "Num. satellite species", - "Prop. satellite species", - "Num. disturbance-dependent transients", - "Prop. disturbance-dependent transients", - "Num. strict transients", - "Prop. strict transients", - "Qualitative Persistence") - - classif <- list( - MembersSatellites, - MembersCore, - MembersStrictTransients, - MembersDdTransients) - classif <- stats::setNames(classif, - c("Satellites", - "Core", - "Strict_transients", - "Disturbance_dependent_transients") - ) - - outputs <- list("Descriptors" = df, "Functional_classification" = classif) - - return(outputs) - -} diff --git a/R/globals.R b/R/globals.R deleted file mode 100644 index 3bf1975..0000000 --- a/R/globals.R +++ /dev/null @@ -1,4 +0,0 @@ -utils::globalVariables(c("C_chisq_sim", "Canopy", "CanopyCover", "Canopy_Freq", - "Canopy_cover", "Canopy_cover_abs", "Frequency", - "Open_Freq", "Open_cover", "Recruit", "RecruitNet", - "Sampled_distance_or_area", "Study_site", "inter_ID")) diff --git a/R/link_completeness.R b/R/link_completeness.R deleted file mode 100644 index fbfee13..0000000 --- a/R/link_completeness.R +++ /dev/null @@ -1,129 +0,0 @@ -#' Link_completeness -#' -#' Observed and estimated number of links, and link completeness. -#' Methods based in Chao et al. (2014) as implemented in R package iNEXT. -#' -#' @param dataset The name of the data set where recruitment networks are stored. -#' Usually, it will be "RecruitNet" (or the name assigned to the data set when -#' it was imported). At least, the data set must contain columns named -#' "Study_site", "Plot", "Canopy", "Recruit" and "Frequency". -#' In data sets containing a single site or a single plot, -#' the corresponding columns must be included anyway. -#' -#' @param site The name of a study site. -#' @param type Can take two values: "incidence" or "abundance". -#' Estimates based on incidence data are recommended, but can only be obtained -#' if your data set is structured in multiple plots. Alternatively, for data -#' collected in a single plot, estimates can be based only on abundance data. -#' -#' @export -#' -#' @return A data frame TODO -#' -#' @examplesIf interactive() -#' -#' link_completeness(RecruitNet, "Ventisquero", "incidence") -#' link_completeness(RecruitNet, "Laxe", "abundance") -#' link_completeness(RecruitNet, "Laxe", "incidence") # Issues a warning -#' link_completeness(RecruitNet, "LosReyes", "abundance") -#' link_completeness(RecruitNet, "LosReyes", "incidence") #Issues an error - - -link_completeness <- function(dataset = NULL, - site = NULL, - type = c("incidence", "abundance")) { - - stopifnot( - c("Study_site", - "Plot", - "Canopy", - "Recruit", - "Frequency" - ) %in% names(dataset)) - - stopifnot(is.character(site)) - stopifnot(length(site) == 1) - - type <- match.arg(type) - - - data_raw <- data.frame(dataset[dataset$Study_site %in% site, ]) - data_RN <- aggr_RN(data_raw) - - # Completeness based on incidence data. - - if (type == "incidence") { - netRaw <- data.frame(cbind(data_raw$Plot, paste(data_raw$Canopy, data_raw$Recruit))) - colnames(netRaw) <- c("Plot", "Pair") - nPlots <- length(unique(netRaw$Plot)) - - # Check points. - - if (nPlots == 1) - stop( - "ERROR: your data is not structured in multiple plots. Incidence approach cannot be used. Try the abundance approach." - ) - if (nPlots < 10) - warning( - "WARNING: your are using the incidence approach with very few plots. Consider using the abundance approach if appropriate." - ) - - # Combine the lists of canopy and recruit species to obtain the total list of species sampled. - - a1 <- split(netRaw[-1], f = netRaw[1]) - a2 <- lapply(a1, unique) - a3 <- unlist(unlist(a2, recursive = FALSE, use.names = FALSE)) - - # Table showing the incidence of each species in the study site - - a4 <- table(a3) - linkIncidence <- as.data.frame(a4) - colnames(linkIncidence) <- c("Pair", "Incidence") - - # Incidence list to be passed to iNEXT - - data_iNEXT <- c(nPlots, sort(linkIncidence$Incidence, decreasing = TRUE)) - - # Call to iNEXT to obtain completeness values - - out <- iNEXT::iNEXT( - data_iNEXT, - q = c(0, 1), - datatype = "incidence_freq", - se = FALSE, - size = nPlots - ) - Lobs <- out$AsyEst[1, 1] - Lest <- out$AsyEst[1, 2] - Lest_LCL <- out$AsyEst[1, 4] - Lest_UCL <- out$AsyEst[1, 5] - Cq0_L <- Lobs / Lest - Cq1_L <- out$DataInfo[1, 5] - df <- data.frame(c(Lobs, Lest, Cq0_L, Cq1_L)) - colnames(df) <- c("Incidence based estimate") - rownames(df) <- c("Lobs", - "Lest", - "Completeness Links (q=0)", - "Coverage Links (q=1)") - } - - # Completeness based on abundance or frequency of recruits. - - if (type == "abundance") { - # Call to iNEXT to obtain completeness values - - out <- iNEXT::iNEXT(data_RN$fij[which(data_RN$fij > 0)], q = 0, datatype = "abundance") - Lobs <- out$AsyEst[1, 1] - Lest <- out$AsyEst[1, 2] - Cq0_L <- Lobs / Lest - Cq1_L <- out$DataInfo[1, 4] - df <- data.frame(c(Lobs, Lest, Cq0_L, Cq1_L)) - colnames(df) <- c("Abundance based estimate") - rownames(df) <- c("Lobs", - "Lest", - "Completeness of links (q=0)", - "Coverage of links (q=1)") - } - - return(df) -} diff --git a/R/merge_RN_cover.R b/R/merge_RN_cover.R deleted file mode 100644 index ccac748..0000000 --- a/R/merge_RN_cover.R +++ /dev/null @@ -1,66 +0,0 @@ -#' Merge cover data with interaction data -#' -#' This function makes a data frame that merges the information from the recruitment network -#' and species cover for a local community. -#' -#' @param RN_data A data frame created with local_comm, containing all the -#' available data from a local community. -#' @param cover_data data set where cover data are stored. Usually, it will be "CanopyCover" -#' (or the name assigned to the dataset "CanopyCover.csv" when it was imported). -#' -#' @return A data.frame containing 5 columns with all the information needed -#' for the basic analysis of recruitment networks and canopy-recruit interactions: -#' canopy species (canopy), recruit species (recruit), recruitment frequency (fij), -#' cover of the canopy (cj) and cover of the recruit (ci). -#' -#' @noRd -#' -#' @examples -#' Ventisquero_raw <- comm_subset(RecruitNet, site = "Ventisquero") -#' Ventisquero_RN <- aggr_RN(Ventisquero_raw) -#' Ventisquero_cover <- aggr_cover(CanopyCover, site = "Ventisquero") -#' Ventisquero_RNc <- merge_RN_cover(Ventisquero_RN, Ventisquero_cover) - -merge_RN_cover <- function(RN_data, cover_data) { - - # Find species present in RN but that lack data on cover. - cover_list <- sort(unique(cover_data$Canopy)) - RN_list <- sort(unique(c(RN_data$Canopy, RN_data$Recruit))) - lack_cover <- setdiff(RN_list, cover_list) - - # Remove species lacking cover from RN - RNc <- if (length(which(RN_data$Recruit %in% lack_cover)) > 0) { - RNc <- RN_data[-which(RN_data$Recruit %in% lack_cover), ] - } else { - RN_data - } - RNc <- if (length(which(RNc$Canopy %in% lack_cover)) > 0) { - RNc <- RNc[-which(RNc$Canopy %in% lack_cover), ] - } else { - RNc - } - - # Add variables with the cover of the canopy (cj) and recruit (ci) species - RNc$cj <- RNc$Canopy - RNc$ci <- RNc$Recruit - for (i in 1:dim(RNc)[1]) { - RNc$cj[i] <- as.numeric(replace( - RNc$Canopy[i], - match(RN_list, RNc$Canopy[i]), - cover_data$abundance[match(RNc$Canopy[i], cover_data$Canopy)] - )) - } - - for (i in 1:dim(RNc)[1]) { - RNc$ci[i] <- as.numeric(replace( - RNc$Recruit[i], - match(RN_list, RNc$Recruit[i]), - cover_data$abundance[match(RNc$Recruit[i], cover_data$Canopy)] - )) - } - - RNc <- utils::type.convert(RNc, as.is = TRUE) - - return(RNc) - -} diff --git a/R/plot_to_site.R b/R/plot_to_site.R deleted file mode 100644 index 7c21c34..0000000 --- a/R/plot_to_site.R +++ /dev/null @@ -1,159 +0,0 @@ -#' TODO: title -#' -#' Convert plots into community data per site (either for a single or multiple sites) -#' -#' @param inter_data data frame with at least four columns: -#' Study_site (unique name of the study site), -#' Recruit (species of the recruit), Canopy (species of the canopy, or "Open"), -#' and Frequency (number of recruits of that species observed under that -#' canopy species in any plot, with "Open" representing recruits observed -#' without a canopy species). -#' -#' @param cover_data data frame with at least five columns: -#' Study_site (unique name of the study site), -#' Plot (unique name of each plot within a study site), -#' Canopy (species of the canopy), -#' Cover (percentage of cover of that species in that plot), and -#' Sampled_distance_or_area (total area of that plot or length in the case of transects). -#' -#' @return a list with two elements. The first element is a data frame with -#' the same structure as the input inter_data, but with two additional columns: -#' inter_ID (a unique identifier for each pair-wise interaction, -#' combining Study_site, Recruit, and Canopy species) and -#' Freq (the number of recruits of that species under that canopy species in -#' the entire study site). -#' The second element is a data frame with one row per canopy species in -#' each Study_site, including two new variables: -#' Canopy_cover (the percentage of cover of that species in the entire -#' sampled area of the Study_site) and mycover_sp (a unique identifier -#' for each canopy species in each Study_site, combining Study_site and Canopy species). -#' -#' @noRd -#' -#' @examples -#' all_nets <- plot_to_site(RecruitNet, CanopyCover) - - - -plot_to_site <- function(inter_data = RecruitNet, - cover_data = CanopyCover - ) { - data <- inter_data - dbcover <- cover_data - data$inter_ID <- paste(data$Study_site, data$Recruit, data$Canopy, sep = "_") - - options(dplyr.summarise.inform = FALSE) - - data$inter_ID <- paste(data$Study_site, data$Recruit, data$Canopy, sep = "_") - dbcover2 <- droplevels(dbcover[!is.na(dbcover$Cover), ]) - rmnets <- setdiff(dbcover$Study_site, dbcover2$Study_site) - - #Those study_site that does not - #have information about the canopy of any of its species will be removed - - dbcover <- dbcover[dbcover$Study_site %in% setdiff(dbcover$Study_site, rmnets), ] - - - data <- data[data$Study_site %in% setdiff(data$Study_site, rmnets), ] - - - #check if the pruning of the study sites, if required, have worked properly i.e. now all the study sites have at least one canopy species - # with information about its cover (if not stop the process) - - if (!(length(setdiff(dbcover$Study_site, data$Study_site)) == 0 | - length(setdiff(data$Study_site, dbcover$Study_site)) == 0)) - stop("Study_site with complete information in the two arguments x,y does not match") - - ############### - - dbcover$Canopy_cover_abs <- with (dbcover, (Cover * Sampled_distance_or_area) / - 100) - - surfN <- data.frame( - dbcover |> - dplyr::group_by(Study_site, Canopy) |> - dplyr::summarise(Canopy_cover_abs = sum(Canopy_cover_abs, na.rm = TRUE)) - ) - - - site_surf <- data.frame( - unique(dbcover[, c("Study_site", "Plot", "Sampled_distance_or_area")]) |> - dplyr::group_by(Study_site) |> - dplyr::summarise(Plot_sup = sum(Sampled_distance_or_area)) - ) - - - - Canopy_all <- merge(surfN, site_surf, by = "Study_site") - Canopy_all$Canopy_cover <- with(Canopy_all, (Canopy_cover_abs * 100) / Plot_sup) - Canopy_all <- Canopy_all[, c("Study_site", "Canopy", "Canopy_cover")] - - Canopy_all[Canopy_all$Canopy_cover == 0, "Canopy_cover"] <- "NA" - - Canopy_all$Canopy_cover <- as.numeric(Canopy_all$Canopy_cover) - data$mynet_sp <- paste(data$Study_site, data$Canopy, sep = "-") - Canopy_all$mycover_sp <- paste(Canopy_all$Study_site, Canopy_all$Canopy, sep = "-") - - Canopy_all <- Canopy_all[!is.na(Canopy_all$Canopy_cover), ] - - - rmnets_anyNA <- unique(data[data$mynet_sp %in% setdiff(unique(data$mynet_sp), (Canopy_all$mycover_sp)), "Study_site"]) - - dbcover <- droplevels(dbcover[dbcover$Study_site %in% setdiff(dbcover$Study_site, rmnets_anyNA), ]) - data <- droplevels(data[data$Study_site %in% setdiff(data$Study_site, rmnets_anyNA), ]) - Canopy_all <- droplevels(Canopy_all[Canopy_all$Study_site %in% setdiff(Canopy_all$Study_site, rmnets_anyNA), ]) - - - inter <- data.frame( - data |> - dplyr::group_by(Study_site, Recruit, Canopy, inter_ID) |> - dplyr::summarise(inter_ID = unique(inter_ID), Freq = sum(Frequency)) - ) - - - for (i in 1:length(unique(inter$Study_site))) - - { - mysite <- inter[inter$Study_site == unique(inter$Study_site)[i], ] - #if(!"Open"%in%unique(mysite$Canopy)) - - myadj <- data[data$Study_site == unique(Canopy_all$Study_site)[i], ] - - Freq <- data.frame(myadj |> - dplyr::group_by(inter_ID) |> - dplyr::summarise(Freq = sum(Frequency))) - - alledge <- merge(Freq, unique(myadj[, c("Study_site", "inter_ID", "Canopy", "Recruit")]), by = - "inter_ID") - myadj <- reshape2::dcast(data = alledge[, c("Recruit", "Canopy", "Freq")], Recruit ~ - Canopy, value.var = "Freq") - myadj2 <- as.matrix(myadj[, -1]) - colnames(myadj2) <- colnames(myadj)[-1] - rownames(myadj2) <- myadj[, 1] - myadj <- myadj2 - myadj2 <- rm - myadj[is.na(myadj)] <- 0 - mycovs <- Canopy_all[Canopy_all$Study_site == unique(Canopy_all$Study_site)[i], ] - myadj <- myadj[, colnames(myadj) %in% mycovs$Canopy] - - - - if (length(colnames(myadj)[which(colnames(myadj) == "Open")]) < 1) { - myadj <- cbind(myadj, rep(0, dim(myadj)[1])) - colnames(myadj)[dim(myadj)[2]] <- "Open" - } - - - } - - - Canopy_all <- Canopy_all |> dplyr::arrange(Study_site) - inter <- inter |> dplyr::arrange(Study_site) - - out <- list() - inter$Frequency <- inter$Freq - out[[1]] <- inter - out[[2]] <- Canopy_all - - return(out) -} diff --git a/R/pre_asocindex.R b/R/pre_asocindex.R deleted file mode 100644 index 712b5e5..0000000 --- a/R/pre_asocindex.R +++ /dev/null @@ -1,131 +0,0 @@ -#' TODO: title -#' -#' Calculate in separate columns the number of recuits of each species under -#' each species of canopy and in the open and its respective percentage of -#' cover (either for multiple or a single site) -#' -#' @param inter_data data frame with at least four columns: -#' Study_site (unique name of the study site), -#' Recruit (species of the recruit), -#' Canopy (species of the canopy, or "Open"), and -#' Frequency (number of recruits of that species observed under that -#' canopy species in any plot, with "Open" representing recruits observed without a canopy species). -#' -#' @param cover_data data frame with at least five columns: -#' Study_site (unique name of the study site), -#' Plot (unique name of each plot within a study site), -#' Canopy (species of the canopy), -#' Cover (percentage of cover of that species in that plot), and -#' Sampled_distance_or_area (total area of that plot or length in the case of transects). -#' -#' @return data frame with one row for each pair-wise interaction -#' (Recruit speices-Canopy species) in each site, and nine columns: -#' inter_ID (a unique identifier for each pair-wise interaction, -#' combining Study_site, Recruit, and Canopy species), -#' Recruit(recruit species), Canopy (canopy speices), -#' Study_site (name of the study site), -#' Canopy_Freq and Open_Freq with the number of recuits observed -#' under that canopy species or in the Open, respectively and -#' Canopy_cover and Open_cover, with the percentage of the total area sampled -#' in the Study_site occupied by that canopy species and Open respectively. -#' Freq_tot is the sum of Canopy_Freq and Open_Freq (total number of recruits -#' of that speices observed in the area sampled in the Study_site). -#' -#' @noRd -#' -#' @examples -#' pre_index_all <- pre_asocindex(RecruitNet, CanopyCover) - -pre_asocindex <- function(inter_data = RecruitNet, - cover_data = CanopyCover - ) { - - data <- inter_data - dbcover <- cover_data - - com <- plot_to_site(data, dbcover) - - inter <- com[[1]] - Canopy_all <- com[[2]] - - adjlist <- list() - coverlist <- list() - edgelist <- list() - net <- NULL - NorecrOpen <- NULL - for (z in 1:length(unique(Canopy_all$Study_site))) - { - net[z] <- unique(Canopy_all$Study_site)[z] - mycovs <- Canopy_all[Canopy_all$Study_site == unique(Canopy_all$Study_site)[z], ] - alledge <- inter[inter$Study_site == unique(Canopy_all$Study_site)[z], ] - myadj <- data.frame(reshape2::dcast(data = alledge[, c("Recruit", "Canopy", "Freq")], Recruit ~ Canopy, value.var = - "Freq")) - myadj2 <- as.matrix(myadj[, -1]) - colnames(myadj2) <- colnames(myadj)[-1] - rownames(myadj2) <- myadj[, 1] - myadj <- myadj2 - myadj[is.na(myadj)] <- 0 - - #select only those canopies from whcih there is recruit under and cover data( ver & adj mat) - myadj <- myadj[, colnames(myadj) %in% mycovs$Canopy] - - #Add a canopy called Open if there is no recruits in Open in that community - if (length(colnames(myadj)[which(colnames(myadj) == "Open")]) < 1) { - NorecrOpen[z] <- unique(Canopy_all$Study_site)[z] - myadj <- cbind(myadj, rep(0, dim(myadj)[1])) - colnames(myadj)[dim(myadj)[2]] <- "Open" - } - mycovs <- mycovs[mycovs$Canopy %in% colnames(myadj), ] - #order the matrix - myadj <- myadj[, c(mycovs$Canopy[-which(mycovs$Canopy == "Open")], "Open")] - rownames(mycovs) <- mycovs$Canopy - mycovs <- mycovs[colnames(myadj), ] - - - #expand the database to include the interactions zero in the inetarction matris ( i.e. non-observed interactions) - - obs_inter <- data.frame(expand.grid(dimnames(provideDimnames(myadj)))[1:2], as.vector(as.matrix(myadj))) - - colnames(obs_inter)[which(colnames(obs_inter) == "Var1")] <- "Recruit" - colnames(obs_inter)[which(colnames(obs_inter) == "Var2")] <- "Canopy" - colnames(obs_inter)[which(colnames(obs_inter) == "as.vector.as.matrix.myadj..")] <- "Freq" - - obs_inter$Study_site <- rep(paste(net[[z]]), dim(obs_inter)[1]) - obs_inter$inter_ID <- - paste(obs_inter$Study_site, - obs_inter$Recruit, - obs_inter$Canopy, - sep = "_") - - alledge <- obs_inter[, c("inter_ID", "Freq" , "Study_site", "Canopy", "Recruit")] - #prepare the database output - openedge <- alledge[alledge$Canopy == "Open", ] - myedge <- alledge[alledge$Canopy != "Open", ] - - # add Canopy_cover - myedge <- merge(myedge, mycovs[, c("Canopy", "Canopy_cover")], by = "Canopy") - #add Open cover - myedge$Open_cover <- rep(mycovs[mycovs$Canopy == "Open", "Canopy_cover"], dim(myedge)[1]) - - #add number or recruits in open per recruit sp - - myedge <- merge(myedge, openedge[, c("Recruit", "Freq")], by = "Recruit", all.x = T) - colnames(myedge)[which(colnames(myedge) == "Freq.x")] <- "Canopy_Freq" - colnames(myedge)[which(colnames(myedge) == "Freq.y")] <- "Open_Freq" - myedge[is.na(myedge$Open_Freq), "Open_Freq"] <- 0 - - myedge <- myedge[which(myedge$Canopy_Freq + myedge$Open_Freq > 0), ] - - adjlist[[z]] <- myadj - coverlist[[z]] <- mycovs - edgelist[[z]] <- myedge - - } - - db_inter <- data.frame(do.call("rbind", edgelist)) - - db_inter$Freq_tot <- db_inter$Canopy_Freq + db_inter$Open_Freq - db_inter$Frequency <- db_inter$Canopy_Freq - return(db_inter) - -} diff --git a/R/recruit_level.R b/R/recruit_level.R deleted file mode 100644 index 2e3c1ca..0000000 --- a/R/recruit_level.R +++ /dev/null @@ -1,64 +0,0 @@ -#' Calculate the number of recruits under any canopy species -#' -#' Calculate in separate columns the total number of recruits of each species -#' under any species of canopy and in the open and its respective percentage -#' of cover of vegetation and Open (either for multiple or a single site) -#' -#' @param inter_data data frame with at least four columns: -#' Study_site (unique name of the study site), -#' Recruit (species of the recruit), -#' Canopy (species of the canopy, or "Open"), and -#' Frequency (number of recruits of that species observed under -#' that canopy species in any plot, with "Open" representing recruits observed -#' without a canopy species). -#' -#' @param cover_data data frame with at least five columns: -#' Study_site (unique name of the study site), -#' Plot (unique name of each plot within a study site), -#' Canopy (species of the canopy), -#' Cover (percentage of cover of that species in that plot), and -#' Sampled_distance_or_area (total area of that plot or length in the case of transects). -#' -#' @return data frame with one row per Rercuit species in each site and nine columns: -#' inter_ID (a unique identifier for each pair-wise interaction, -#' combining Study_site, Recruit, and Canopy species), -#' Recruit(recruit species), -#' Canopy (canopy speices), -#' Study_site (name of the study site), -#' Canopy_Freq and Open_Freq with the number of recuits observed -#' under that canopy species or in the Open, respectively and Canopy_cover and -#' Open_cover, with the percentage of the total area sampled in the Study_site -#' occupied by that canopy species and Open respectively. -#' Freq_tot is the sum of Canopy_Freq and Open_Freq (total number of recruits -#' of that speices observed in the area sampled in the Study_site). -#' -#' @noRd -#' -#' @examples -#' db_sp_rec <- recruit_level (RecruitNet, CanopyCover) - -recruit_level <- function(inter_data = RecruitNet, - cover_data = CanopyCover - ) { - - options(dplyr.summarise.inform = FALSE) - - data <- inter_data - dbcover <- cover_data - - db_inter <- pre_asocindex(data, dbcover) - - db_sp_rec <- data.frame( - db_inter |> - dplyr::group_by(Study_site, Recruit) |> - dplyr::summarise( - Canopy_Freq = sum(Canopy_Freq, na.rm = TRUE), - Open_Freq = unique(Open_Freq, na.rm = TRUE), - Open_cover = unique(Open_cover, na.rm = TRUE), - Canopy_cover = 100 - unique(Open_cover, na.rm = TRUE) - ) - ) - - return(db_sp_rec) - -} diff --git a/R/sig_test.R b/R/sig_test.R deleted file mode 100644 index 972a7a6..0000000 --- a/R/sig_test.R +++ /dev/null @@ -1,138 +0,0 @@ -#' Calculate significance test of association -#' -#' @description -#' Calculate a significance tests of association for each row. -#' Rows can be a pair-wise interaction (Recruit species-Canopy species interaction), -#' the number of recruits observed under any canopy species (Recruit species) -#' or the number of Recruits of any given speies under a given canopy species -#' (Canopy species) -#' -#' @param db_inter data frame obtained as the output of the functions -#' pre_asocindex, recruit_level,canopy_level, or com_level -#' @param iteration number of iterations -#' @param threshold minimun number of recruits to perform the binomial test -#' -#' @return data frame with the same structure as the input with three -#' additional columns:int_p (p-value of the binomial test of association), -#' int_sign (the sign of the association, being Posive (or Negative) if the -#' association is stronger (or weaker) than expected by the percentage cover -#' of Canopy and Open, and Neutral if there is not enougth power to conduct -#' the test, stdres (standarized resdual quantifying the difference between -#' the observed and expected values), and testability (indicating whether -#' the sample size allow to conduct or not the test being Non-testable those -#' rows in which int_sign = Neutral) -#' -#' @noRd -#' -#' @examples -#' Ventisquero_RN <- comm_subset(RecruitNet, "Ventisquero") -#' Ventisquero_cover <- comm_subset(CanopyCover, "Ventisquero") -#' sig_test(site_level(Ventisquero_RN, Ventisquero_cover)) # overall -#' -#' sig_test(pre_asocindex(Ventisquero_RN, Ventisquero_cover)) # per species - -sig_test <- function(data_freq=db_inter, - iteration = 100, - threshold = 5) { - - db_inter<-data_freq - - - db_inter$int_p <- rep(NA, dim(db_inter)[1]) - db_inter$int_sign <- rep(NA, dim(db_inter)[1]) - - for (i in 1:dim(db_inter)[1]) { - if (sum(db_inter$Canopy_Freq[i] + db_inter$Open_Freq[i]) < 100000) { - test <- - chisq.test2( - c(db_inter$Canopy_Freq[i], db_inter$Open_Freq[i]), - p = c(db_inter$Canopy_cover[i], db_inter$Open_cover[i]), - rescale.p = TRUE, - simulate.p.value = TRUE - ) - db_inter$int_p[i] <- test$p.value - db_inter$stdres[i] <- test$stdres[1] - db_inter$int_sign[i] <- - ifelse( - test$p.value <= 0.05 & - test$stdres[1] > 0, - "Positive", - ifelse( - test$p.value <= 0.05 & test$stdres[1] < 0, - "Negative", - "Neutral" - ) - ) - } - - if (sum(db_inter$Canopy_Freq[i] + db_inter$Open_Freq[i]) >= 100000) { - test <- - chisq.test2( - c(db_inter$Canopy_Freq[i], db_inter$Open_Freq[i]), - p = c(db_inter$Canopy_cover[i], db_inter$Open_cover[i]), - rescale.p = TRUE, - simulate.p.value = FALSE - ) - db_inter$int_p[i] <- test$p.value - db_inter$stdres[i] <- test$stdres[1] - db_inter$int_sign[i] <- - ifelse( - test$p.value <= 0.05 & - test$stdres[1] > 0, - "Positive", - ifelse( - test$p.value <= 0.05 & test$stdres[1] < 0, - "Negative", - "Neutral" - ) - ) - } - - } - - - db_inter$testability <- rep("NA", dim(db_inter)[1]) - - for (i in 1:dim(db_inter)[1]) - { - if (db_inter[i, ]$int_sign != "Neutral") { - db_inter[i, ]$testability <- db_inter[i, ]$int_sign - } - - if (db_inter[i, ]$int_sign == "Neutral" & - sum(db_inter[i, ]$Canopy_Freq + db_inter[i, ]$Open_Freq) > 5) { - db_inter[i, ]$testability <- db_inter[i, ]$int_sign - } - - if (db_inter[i, ]$int_sign == "Neutral" & - sum(db_inter[i, ]$Canopy_Freq + db_inter[i, ]$Open_Freq) < 6) { - n <- sum(db_inter[i, ]$Canopy_Freq + db_inter[i, ]$Open_Freq) - fcan <- - ifelse(db_inter[i, ]$Canopy_cover <= db_inter[i, ]$Open_cover, n, 0) - fopen <- - ifelse(db_inter[i, ]$Open_cover < db_inter[i, ]$Canopy_cover, n, 0) - - db_inter[i, ]$testability <- - ifelse(length(which( - replicate( - iteration, - chisq.test2( - c(fcan, fopen), - p = c(db_inter$Canopy_cover[i], db_inter$Open_cover[i]), - rescale.p = T, - simulate.p = T - )$p.value - ) < 0.05 - )) > threshold, "testable", "non_testable") - } - - db_inter[db_inter$testability == "testable", "testability"] <- - "Neutral" - } - - - - return(db_inter) - - -} diff --git a/R/sign_net.R b/R/sign_net.R deleted file mode 100644 index 6dd6168..0000000 --- a/R/sign_net.R +++ /dev/null @@ -1,103 +0,0 @@ -#' Association significance testing -#' -#' Generate a list with four elements, a data base with the significance tests -#' of association of each interaction (in rows) and three matrices of interaction -#' of positive, negative and neutral interactions, from a study site (for a single sites) -#' -#' @param inter_data data frame with at least four columns: -#' Study_site (unique name of the study site), -#' Recruit (species of the recruit), -#' Canopy (species of the canopy, or "Open"), and -#' Frequency (number of recruits of that species observed under that canopy species -#' in any plot, with "Open" representing recruits observed without a canopy species). -#' -#' @param cover_data data frame with at least five columns: -#' Study_site (unique name of the study site), -#' Plot (unique name of each plot within a study site), -#' Canopy (species of the canopy), -#' Cover (percentage of cover of that species in that plot), and -#' Sampled_distance_or_area (total area of that plot or length in the case of transects). -#' -#' @param site is the name of the Study_site -#' -#' @param type is a character that identifies which hypothesis is being tested with three options: -#' 1) "by_pairwise_interaction": whether the number of recuits of each recruit species under each canopy species is significantly different from -#' the number observed in the open, considering the percentage of cover of that canopy species and open area (i.e.bare ground), respectively. -#' 2) "by_recruit_sp": whether the number of recuits of each recruit species under any canopy species (all together) is significantly different from -#' the number observed in the open, considering the percentage of cover of all canopy species together and open area (i.e.bare ground), respectively. -#' 3) "by_canopy_sp": whether the number of recuits of any recruit species (all together) under a given canopy species is significantly different from -#' the number observed in the open, considering the percentage of cover of that canopy species and open area (i.e.bare ground), respectively -#' -#' @return a list with four elements. The first element data frame with the -#' same structure as the input with three additional columns: -#' int_p (p-value of the binomial test of association), -#' int_sign (the sign of the association, being Posive (or Negative) if the -#' association is stronger (or weaker) than expected by the percentage cover -#' of Canopy and Open, and Neutral if there is not enought power to conduct the test, -#' stdres (standarized resdual quantifying the difference between the observed -#' and expected values), and -#' testability (indicating whether the sample size allow to conduct or not the test -#' being Non-testable those rows in which int_sign = Neutral). -#' The second, third and fourth elements of teh list are the matrices of only positive, -#' negative and neutral ( testable) associations based on the database generated in the first element of the list. -#' -#' @export -#' -#' @examplesIf interactive() -#' -#' sign_vest <- sign_net(RecruitNet, CanopyCover, "Ventisquero", "by_pairwise_interaction" ) - - -sign_net <- function(inter_data, cover_data, site, type) { - - inter <- droplevels(RecruitNet[RecruitNet$Study_site == site, ]) - cover <- droplevels(CanopyCover[CanopyCover$Study_site == site, ]) - site <- unique(inter$Study_site) - -if (type == "by_pairwise_interaction") { pre_index_all <- pre_asocindex(inter, cover)} -if (type == "by_recruit_sp") {pre_index_all <- recruit_level(inter, cover)} -if (type == "by_canopy_sp") {pre_index_all <- canopy_level(inter, cover)} - - - pre_index_all$Frequency <- pre_index_all$Canopy_Freq - - db_inter <- sig_test(pre_index_all) - - db_inter$fij <- db_inter$Canopy_Freq - - pos <- droplevels(db_inter[db_inter$testability == "Positive", ]) - neg <- droplevels(db_inter[db_inter$testability == "Negative", ]) - neu <- droplevels(db_inter[db_inter$testability == "Neutral", ]) - - cov_pos <- droplevels(cover[cover$Canopy %in% unique(pos$Canopy), ]) - cov_neg <- droplevels(cover[cover$Canopy %in% unique(neg$Canopy), ]) - cov_neu <- droplevels(cover[cover$Canopy %in% unique(neu$Canopy), ]) - - posRN <- comm_to_RN(pos, cov_pos, unique(pos$Study_site)) - negRN <- comm_to_RN(neg, cov_pos, unique(neg$Study_site)) - neuRN <- comm_to_RN(neu, cov_pos, unique(neu$Study_site)) - - pos_net <- RN_to_matrix(posRN[, c("Canopy", "Recruit", "fij")]) - pos_net <- pos_net[rowSums(pos_net) > 0, colSums(pos_net) > 0] - - neg_net <- RN_to_matrix(negRN[, c("Canopy", "Recruit", "fij")]) - neg_net <- neg_net[rowSums(neg_net) > 0, colSums(neg_net) > 0] - - neu_net <- RN_to_matrix(neuRN[, c("Canopy", "Recruit", "fij")]) - neu_net <- neu_net[rowSums(neu_net) > 0, colSums(neu_net) > 0] - - - sign_networks <- list() - - sign_networks[[1]] <- db_inter - sign_networks[[2]] <- pos_net - sign_networks[[3]] <- neg_net - sign_networks[[4]] <- neu_net - - names(sign_networks)[1] <- "All_interactions_db" - names(sign_networks)[2] <- "Positive_interactions" - names(sign_networks)[3] <- "Negative_interactions" - names(sign_networks)[4] <- "Neutral_interactions" - - return(sign_networks) -} diff --git a/R/site_level.R b/R/site_level.R deleted file mode 100644 index fcac079..0000000 --- a/R/site_level.R +++ /dev/null @@ -1,91 +0,0 @@ -#' TODO: title -#' -#' Calculate in separate columns the total number of recruits (of any species) -#' under any species of canopy and in the open and its respective percentage of -#' cover (either for multiple or a single site). -#' -#' @param inter_data data frame with at least four columns: -#' Study_site (unique name of the study site), -#' Recruit (species of the recruit), -#' Canopy (species of the canopy, or "Open"), and -#' Frequency (number of recruits of that species observed under that canopy species -#' in any plot, with "Open" representing recruits observed without a canopy species). -#' -#' @param cover_data data frame with at least five columns: -#' Study_site (unique name of the study site), -#' Plot (unique name of each plot within a study site), -#' Canopy (species of the canopy), -#' Cover (percentage of cover of that species in that plot), and -#' Sampled_distance_or_area (total area of that plot or length in the case of transects). -#' -#' @return data frame with one row per site and nine columns: -#' inter_ID (a unique identifier for each pair-wise interaction, -#' combining Study_site, Recruit, and Canopy species), -#' Recruit(recruit species), Canopy (canopy speices), -#' Study_site (name of the study site), -#' Canopy_Freq and Open_Freq with the number of recuits observed under -#' that canopy species or in the Open, respectively and -#' Canopy_cover and Open_cover, with the percentage of the total area sampled -#' in the Study_site occupied by that canopy species and Open respectively. -#' Freq_tot is the sum of Canopy_Freq and Open_Freq (total number of recruits -#' of that speices observed in the area sampled in the Study_site). -#' -#' @noRd -#' -#' @examples -#' db_sp_com <- site_level (RecruitNet, CanopyCover) - - -site_level <- function(inter_data = RecruitNet, - cover_data = CanopyCover) { - - options(dplyr.summarise.inform = FALSE) - - data <- inter_data - dbcover <- cover_data - - db_inter <- pre_asocindex(data, dbcover) - - db_sp_can <- data.frame( - db_inter |> - dplyr::group_by(Study_site, Canopy) |> - dplyr::summarise( - Canopy_Freq = sum(Canopy_Freq, na.rm = TRUE), - Open_cover = unique(Open_cover, na.rm = TRUE), - Canopy_cover = unique(Canopy_cover, na.rm = TRUE) - ) - ) - - Rec_open <- data.frame( - db_inter |> - dplyr::group_by(Study_site, Recruit) |> - dplyr::summarise(Open_Freq = unique(Open_Freq, na.rm = TRUE)) - ) - - Rec_open <- data.frame(Rec_open |> - dplyr::group_by(Study_site) |> - dplyr::summarise(Open_Freq = sum(Open_Freq, na.rm = TRUE))) - - db_sp_can <- merge(db_sp_can, Rec_open, by = "Study_site") - - db_sp_can <- db_sp_can[, c("Study_site" , - "Canopy" , - "Canopy_Freq", - "Open_Freq", - "Open_cover", - "Canopy_cover")] - - db_com <- data.frame( - db_sp_can |> - dplyr::group_by(Study_site) |> - dplyr::summarise( - Canopy_Freq = sum(Canopy_Freq, na.rm = TRUE), - Open_Freq = unique(Open_Freq, na.rm = TRUE), - Open_cover = unique(Open_cover, na.rm = TRUE), - Canopy_cover = 100 - unique(Open_cover, na.rm = TRUE) - ) - ) - - return(db_com) - -} diff --git a/man/CanopyCover.Rd b/man/CanopyCover.Rd deleted file mode 100644 index aebafe7..0000000 --- a/man/CanopyCover.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/datasets.R -\docType{data} -\name{CanopyCover} -\alias{CanopyCover} -\title{CanopyCover} -\format{ -An object of class \code{data.frame} with 26812 rows and 7 columns. -} -\source{ -https://doi.org/10.1002/ecy.3923 -} -\usage{ -CanopyCover -} -\description{ -Canopy cover of plant-plant interactions global database -} -\keyword{datasets} diff --git a/man/RN_to_matrix.Rd b/man/RN_to_matrix.Rd deleted file mode 100644 index 6dd6598..0000000 --- a/man/RN_to_matrix.Rd +++ /dev/null @@ -1,39 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RN_to_matrix.R -\name{RN_to_matrix} -\alias{RN_to_matrix} -\title{Function RN_matrixForm} -\usage{ -RN_to_matrix(RNdata = NULL, weight = NULL) -} -\arguments{ -\item{RNdata}{A data frame generated with \code{\link[=comm_subset]{comm_subset()}} or a data.frame -containing columns with all the information needed for the basic analysis of -recruitment networks: canopy species (canopy), recruit species (recruit), -cover of the canopy (cj) and recruit (ci), -and columns with possible weighting variables.} - -\item{weight}{The name of the column of the data frame to be used as weight -variable for the links.} -} -\value{ -A matrix object with the species names as row (recruit species) and -column names (canopy species), and cells indicating the chosen weight for -each interaction. For example, the output of function local_RN provides the -number of recruits of species i interacting with canopy species j (fij), -the number of plots (i.e. incidence) where the interaction has been found (Tij), -and the binary presence/absence of the interaction in the whole study site (Pij). -Can be used as bipartite input. -} -\description{ -For some analyses, the data must be stored as a matrix rather than data frame. -The next function takes a RN stored as data frame and transforms it into a matrix. -The output matrix can be used input for bipartite package. -} -\examples{ -data(RecruitNet) -data(CanopyCover) -Ventisquero_RNc <- comm_to_RN(RecruitNet, CanopyCover, site = "Ventisquero") -Ventisquero_matrix <- RN_to_matrix(Ventisquero_RNc, weight = "fij") -Ventisquero_matrix <- RN_to_matrix(Ventisquero_RNc, weight = "Tij") -} diff --git a/man/RecruitNet.Rd b/man/RecruitNet.Rd deleted file mode 100644 index 7d6c63a..0000000 --- a/man/RecruitNet.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/datasets.R -\docType{data} -\name{RecruitNet} -\alias{RecruitNet} -\title{RecruitNet} -\format{ -An object of class \code{data.frame} with 135210 rows and 26 columns. -} -\source{ -https://doi.org/10.1002/ecy.3923 -} -\usage{ -RecruitNet -} -\description{ -Plant-plant interactions global database -} -\keyword{datasets} diff --git a/man/RecruitNetP-package.Rd b/man/RecruitNetP-package.Rd deleted file mode 100644 index 12dd607..0000000 --- a/man/RecruitNetP-package.Rd +++ /dev/null @@ -1,31 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RecruitnetP-package.R -\docType{package} -\name{RecruitNetP-package} -\alias{RecruitNetP} -\alias{RecruitNetP-package} -\title{RecruitNetP: Analysis of Recruitment Networks} -\description{ -Analyze recruitment networks and patterns of plant-plant interactions. -} -\seealso{ -Useful links: -\itemize{ - \item \url{https://github.com/EcologyR/RecruitNetP} - \item Report bugs at \url{https://github.com/EcologyR/RecruitNetP/issues} -} - -} -\author{ -\strong{Maintainer}: Francisco Rodriguez-Sanchez \email{f.rodriguez.sanc@gmail.com} (\href{https://orcid.org/0000-0002-7981-1599}{ORCID}) [copyright holder] - -Authors: -\itemize{ - \item Alicia Montesinos-Navarro \email{alimontesinos@gmail.com} (\href{https://orcid.org/0000}{ORCID}) [copyright holder] - \item José Luis Garrido \email{jlgarridosan@gmail.com} (\href{https://orcid.org/0000}{ORCID}) [copyright holder] - \item Miguel Verdú \email{Miguel.Verdu@ext.uv.es} (\href{https://orcid.org/0000}{ORCID}) [copyright holder] - \item Julio M. Alcántara \email{jmalcan@ujaen.es} (\href{https://orcid.org/0000}{ORCID}) [copyright holder] -} - -} -\keyword{internal} diff --git a/man/associndex.Rd b/man/associndex.Rd deleted file mode 100644 index 501772f..0000000 --- a/man/associndex.Rd +++ /dev/null @@ -1,76 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/associndex.R -\name{associndex} -\alias{associndex} -\title{Calculate interaction strength indices} -\usage{ -associndex( - int_data = NULL, - cover_data = NULL, - area_sampled = NULL, - thr_dens = 100 -) -} -\arguments{ -\item{int_data}{data frame with at least four columns: -\itemize{ -\item Study_site (unique name of the study site), -\item Recruit (species of the recruit), -\item Canopy (species of the canopy, or "Open"), and -\item Frequency (number of recruits of that species observed under that -canopy species in any plot, with "Open" representing recruits observed -without a canopy species). -If available, the dimensions of the study plots can be also included as -variables PlotdimX and PlotdimY. -}} - -\item{cover_data}{data frame with at least five columns: -\itemize{ -\item Study_site (unique name of the study site), -\item Plot (unique name of each plot within a study site), -\item Canopy (species of the canopy), -\item Cover (percentage of cover of that species in that plot), and -\item Sampled_distance_or_area (total area of the plot). -}} - -\item{area_sampled}{Total area surveyed in the study site (in m2).} - -\item{thr_dens}{Maximum density considered as reasonable. For example, if a canopy species is rare -(e.g. 0.01 m2) but there is one sapling growing beneath, we would estimate a density of 100 saplings/m2 -for this interaction, what is likely an overestimate because it seems reasonable that increasing the -effort under this canopy species we would obtain a much lower density estimate. The threshold is set -by default to 100 recruits/m2.} -} -\value{ -data frame with indices of pair-wise interaction strength and the data neeeded for their estimation. -Each row corresponds to each pair-wise interaction, and contains the following columns: -Recruit: recruit species. -Canopy: canopy species. -inter_ID: a unique identifier for each pair-wise interaction, combining Study_site, Recruit, and Canopy species. -Study_site: name of the study site. -Canopy_Freq: number of recruits found in the vicinity of the canopy species. -Open_Freq: number of recruits found in open spaces. -Canopy_cover: percentage of the total area sampled in the study site occupied by the canopy species. -Open_cover: percentage of the total area sampled in the study site occupied by Open spaces. -Freq_tot: sum of Canopy_Freq and Open_Freq (total number of recruits of the recruit species in the area sampled -in the study site. -RecrDens_canopy: density of recruits under the canopy species (recruits/m2). -RecrDens_open: density of recruits in open spaces (recruits/m2). -Max_Recr_Density: maximum recruitment density between under the canopy species vs in open spaces. -max_Recr: maximum recruitment density of the recruit species in the study site. -Ns: Normalized Neighbour Suitability index (Mingo, 2014). -NintC: commutative symmetry intensity index (Díaz-Sierra et al., 2017) -NintA:additive symmetry intensity index (Díaz-Sierra et al., 2017) -RII: Relative Interaction Index (Armas et al., 2004). -} -\description{ -Calculate interaction strength indices -} -\examples{ -data(RecruitNet) -data(CanopyCover) -int_data <- comm_subset(RecruitNet, site = "Ventisquero") -cover_data <- CanopyCover[CanopyCover$Study_site == "Ventisquero", ] -Ventisquero_int <- associndex(int_data, cover_data) - -} diff --git a/man/comm_subset.Rd b/man/comm_subset.Rd deleted file mode 100644 index 0961b05..0000000 --- a/man/comm_subset.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/comm_subset.R -\name{comm_subset} -\alias{comm_subset} -\title{Subset sites} -\usage{ -comm_subset(dataset = NULL, site = NULL) -} -\arguments{ -\item{dataset}{A data frame containing recruitment data.} - -\item{site}{character. Name of a study site within the dataset or -a character vector with the names of several study sites.} -} -\value{ -A data frame containing all the data from the desired community/ies -} -\description{ -This function extracts the information of a given local community and -stores it in a separate data frame. It can also extract more than one site. -} -\examples{ -data(RecruitNet) - -Ventisquero <- comm_subset(RecruitNet, site = "Ventisquero") - -TwoSites <- comm_subset(RecruitNet, c("Ventisquero","Agadir")) -} diff --git a/man/comm_summary.Rd b/man/comm_summary.Rd deleted file mode 100644 index dd4f4b8..0000000 --- a/man/comm_summary.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/comm_summary.R -\name{comm_summary} -\alias{comm_summary} -\title{Summarise metadata of a community} -\usage{ -comm_summary(dataset = NULL) -} -\arguments{ -\item{dataset}{A data frame for a single site (or a group of sites). -For example, a data frame created with \code{\link[=comm_subset]{comm_subset()}}. This function relies on -the variable names used in the original RecruitNet database, so it cannot be used -with other datasets unless they follow exactly the ordering and the names used in RecruitNet.} -} -\value{ -A data frame with summary information of the local community/ies. -} -\description{ -This function provides a summary of the characteristics (i.e. metadata) of -a local community. -} -\examples{ -All_sites_details <- comm_summary(RecruitNet) -Ventisquero <- comm_subset(RecruitNet, site = "Ventisquero") -Ventisquero_details <- comm_summary(Ventisquero) -} diff --git a/man/comm_to_RN.Rd b/man/comm_to_RN.Rd deleted file mode 100644 index cf5e16b..0000000 --- a/man/comm_to_RN.Rd +++ /dev/null @@ -1,37 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/comm_to_RN.R -\name{comm_to_RN} -\alias{comm_to_RN} -\title{Merge cover data with interaction data for a given site} -\usage{ -comm_to_RN(RN_data = NULL, cover_data = NULL, site = NULL) -} -\arguments{ -\item{RN_data}{data set where recruitment networks are stored. -Usually, it will be "RecruitNet" (or the name assigned to the dataset when it was imported).} - -\item{cover_data}{data set where cover data is stored. -Usually, it will be "CanopyCover" (or the name assigned to the dataset -"CanopyCover.csv" when it was imported).} - -\item{site}{name of the study site.} -} -\value{ -A data.frame containing 7 columns with all the information needed -for the basic analysis of recruitment networks and canopy-recruit interactions: -canopy species (Canopy), recruit species (Recruit), recruitment frequency (fij), -Tij = TODO, -Pij = TODO, -cover of the canopy (cj) and cover of the recruit (ci). -} -\description{ -This function returns a data frame that merges the information from the -recruitment network and species cover for a local community. -It is a shortcut to directly obtain the data for a local community -from the RecruitNet dataset. -} -\examples{ -data(RecruitNet) -data(CanopyCover) -Ventisquero_RNc <- comm_to_RN(RecruitNet, CanopyCover, site = "Ventisquero") -} diff --git a/man/download_RN.Rd b/man/download_RN.Rd deleted file mode 100644 index 5b70cb9..0000000 --- a/man/download_RN.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/download_RN.R -\name{download_RN} -\alias{download_RN} -\title{Download RecruitNet database} -\usage{ -download_RN(path = getwd(), destfile = "RN.zip", unzip = TRUE) -} -\arguments{ -\item{path}{character. Path to folder where the RecruitNet database should be saved.} - -\item{destfile}{character. Name of the zip file to be saved.} - -\item{unzip}{Logical. Uncompress the zip file? Default is TRUE.} -} -\value{ -A zip file or two CSV files, depending if unzip is TRUE. -} -\description{ -Download latest version of the RecruitNet database (Verdú et al. 2023, -\doi{doi:10.1002/ecy.3923}). -} -\examples{ -\dontshow{if (interactive()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} -download_RN() -\dontshow{\}) # examplesIf} -} diff --git a/man/funtopol.Rd b/man/funtopol.Rd deleted file mode 100644 index d9bd577..0000000 --- a/man/funtopol.Rd +++ /dev/null @@ -1,56 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/funtopol.R -\name{funtopol} -\alias{funtopol} -\title{Functional topology of a recruitment network.} -\usage{ -funtopol(int_data) -} -\arguments{ -\item{int_data}{Data frame with columns named "Canopy" and "Recruit" identifying -observed interactions (i.e. interactions with frequency > 0).} -} -\value{ -The function returns two outputs: a data frame with the numeric summary of the -functional structure and a list containing the lists of species of each functional -type (core, satellite, strict transients and disturbance-dependent transients). -} -\description{ -Alcantara & Rey (2012) derived a qualitative way to infer potential species -persistence from their position in the RNs based on a combination of non-negative -matrix theory and graph theory. Basically, directed unipartite graphs can be -unambiguously dissected into 'SCCs'. SCCs are the largest possible subgroups of -nodes connected so that all the nodes in a subgroup can be reached from all others -following the directions of the links. In the case of RNs, we can define five -types of SCCs which play different functional roles: -\itemize{ -\item The "core" of the network is the SCC formed by the largest number of species. -All species in the core must recruit, at least, in the vicinity of another core -species, and allow the recruitment of at least another core species. -\item “Satellites” are non-core species that can be reached from some core species, -following the direction of the arrows. For example, a satellite species is one -that recruits in the vicinity of some core species but that does not show -recruitment of any species in its vicinity. -\item "Disturbance-dependent transients" are species that can be reached from the open -node but not from core or satellite species (i.e. for example, species that only -recruit away from established plants). -\item "Strict transients" are species that cannot be reached from any other node (i.e. -those that do not recruit in the studied local assemblage). -} - -Assuming that the dynamics of the system is linear and time-invariant (LTI dynamics), -like in Markov models (Horn, 1975, Siles et al., 2008), only core and satellite -species will persist in equilibrium in the absence of disturbance. When the dynamics -are non-linear, this result cannot be guaranteed to hold, but simulations have shown -that the probability of persistence is higher and the time to extinction is longer -for core and satellite than for transient species (Alcantara et al., 2017). The sum -of core and satellite species is a qualitative approximation to the number of species -that can potentially persist. -} -\examples{ -data(RecruitNet) -int_data <- comm_subset(RecruitNet, site = "Ventisquero") -Ventisquero_funtopol <- funtopol(int_data) -Ventisquero_funtopol$Descriptors -Ventisquero_funtopol$Functional_classification -} diff --git a/man/link_completeness.Rd b/man/link_completeness.Rd deleted file mode 100644 index 37bed4c..0000000 --- a/man/link_completeness.Rd +++ /dev/null @@ -1,44 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/link_completeness.R -\name{link_completeness} -\alias{link_completeness} -\title{Link_completeness} -\usage{ -link_completeness( - dataset = NULL, - site = NULL, - type = c("incidence", "abundance") -) -} -\arguments{ -\item{dataset}{The name of the data set where recruitment networks are stored. -Usually, it will be "RecruitNet" (or the name assigned to the data set when -it was imported). At least, the data set must contain columns named -"Study_site", "Plot", "Canopy", "Recruit" and "Frequency". -In data sets containing a single site or a single plot, -the corresponding columns must be included anyway.} - -\item{site}{The name of a study site.} - -\item{type}{Can take two values: "incidence" or "abundance". -Estimates based on incidence data are recommended, but can only be obtained -if your data set is structured in multiple plots. Alternatively, for data -collected in a single plot, estimates can be based only on abundance data.} -} -\value{ -A data frame TODO -} -\description{ -Observed and estimated number of links, and link completeness. -Methods based in Chao et al. (2014) as implemented in R package iNEXT. -} -\examples{ -\dontshow{if (interactive()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} - -link_completeness(RecruitNet, "Ventisquero", "incidence") -link_completeness(RecruitNet, "Laxe", "abundance") -link_completeness(RecruitNet, "Laxe", "incidence") # Issues a warning -link_completeness(RecruitNet, "LosReyes", "abundance") -link_completeness(RecruitNet, "LosReyes", "incidence") #Issues an error -\dontshow{\}) # examplesIf} -} diff --git a/man/sign_net.Rd b/man/sign_net.Rd deleted file mode 100644 index 253a685..0000000 --- a/man/sign_net.Rd +++ /dev/null @@ -1,60 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sign_net.R -\name{sign_net} -\alias{sign_net} -\title{Association significance testing} -\usage{ -sign_net(inter_data, cover_data, site, type) -} -\arguments{ -\item{inter_data}{data frame with at least four columns: -Study_site (unique name of the study site), -Recruit (species of the recruit), -Canopy (species of the canopy, or "Open"), and -Frequency (number of recruits of that species observed under that canopy species -in any plot, with "Open" representing recruits observed without a canopy species).} - -\item{cover_data}{data frame with at least five columns: -Study_site (unique name of the study site), -Plot (unique name of each plot within a study site), -Canopy (species of the canopy), -Cover (percentage of cover of that species in that plot), and -Sampled_distance_or_area (total area of that plot or length in the case of transects).} - -\item{site}{is the name of the Study_site} - -\item{type}{is a character that identifies which hypothesis is being tested with three options: -\enumerate{ -\item "by_pairwise_interaction": whether the number of recuits of each recruit species under each canopy species is significantly different from -the number observed in the open, considering the percentage of cover of that canopy species and open area (i.e.bare ground), respectively. -\item "by_recruit_sp": whether the number of recuits of each recruit species under any canopy species (all together) is significantly different from -the number observed in the open, considering the percentage of cover of all canopy species together and open area (i.e.bare ground), respectively. -\item "by_canopy_sp": whether the number of recuits of any recruit species (all together) under a given canopy species is significantly different from -the number observed in the open, considering the percentage of cover of that canopy species and open area (i.e.bare ground), respectively -}} -} -\value{ -a list with four elements. The first element data frame with the -same structure as the input with three additional columns: -int_p (p-value of the binomial test of association), -int_sign (the sign of the association, being Posive (or Negative) if the -association is stronger (or weaker) than expected by the percentage cover -of Canopy and Open, and Neutral if there is not enought power to conduct the test, -stdres (standarized resdual quantifying the difference between the observed -and expected values), and -testability (indicating whether the sample size allow to conduct or not the test -being Non-testable those rows in which int_sign = Neutral). -The second, third and fourth elements of teh list are the matrices of only positive, -negative and neutral ( testable) associations based on the database generated in the first element of the list. -} -\description{ -Generate a list with four elements, a data base with the significance tests -of association of each interaction (in rows) and three matrices of interaction -of positive, negative and neutral interactions, from a study site (for a single sites) -} -\examples{ -\dontshow{if (interactive()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} - -sign_vest <- sign_net(RecruitNet, CanopyCover, "Ventisquero", "by_pairwise_interaction" ) -\dontshow{\}) # examplesIf} -} diff --git a/tests/testthat/test-RN_to_matrix.R b/tests/testthat/test-RN_to_matrix.R deleted file mode 100644 index bebafcd..0000000 --- a/tests/testthat/test-RN_to_matrix.R +++ /dev/null @@ -1,161 +0,0 @@ -test_that("RN_to_matrix works", { - data(RecruitNet) - data(CanopyCover) - Ventisquero_RNc <- comm_to_RN(RecruitNet, CanopyCover, site = "Ventisquero") - out <- RN_to_matrix(Ventisquero_RNc, weight = "fij") - expect_equal(out, - structure(c(2L, 0L, 0L, 20L, 1L, 1L, 2L, 0L, 19L, 2L, 5L, 13L, - 13L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 2L, 1L, 0L, 0L, 3L, 2L, 0L, 11L, 0L, 1L, 2L, - 0L, 5L, 1L, 0L, 3L, 8L, 2L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 4L, - 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 19L, - 3L, 0L, 6L, 0L, 12L, 7L, 2L, 40L, 16L, 2L, 59L, 41L, 16L, 1L, - 2L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, - 0L, 0L, 0L, 0L, 2L, 0L, 0L, 12L, 0L, 1L, 2L, 0L, 6L, 1L, 0L, - 8L, 5L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 1L, 0L, 2L, 1L, 0L, 0L, 0L, - 2L, 1L, 0L, 3L, 4L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5L, 6L, 0L, 13L, - 1L, 5L, 2L, 2L, 27L, 10L, 3L, 31L, 82L, 3L, 4L, 0L, 15L, 0L, - 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 3L, 3L, 0L, - 0L, 3L, 3L, 0L, 6L, 0L, 0L, 1L, 0L, 0L, 3L, 0L, 13L, 23L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 193L, 26L, 5L, 422L, 5L, 141L, 163L, 45L, - 306L, 77L, 26L, 435L, 457L, 132L, 1L, 5L, 87L, 13L, 7L, 21L, - 1L, 2L, 4L, 6L, 3L, 3L, 3L, 1L, 4L, 13L, 9L, 11L, 15L, 4L, 9L, - 9L, 0L, 0L, 9L, 5L, 4L, 4L, 0L, 14L, 4L, 15L, 20L, 33L, 11L, - 2L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 2L, 4L, 0L, 1L, 27L, 1L, 0L, 51L, 12L, 14L, 14L, 0L, 82L, - 2L, 7L, 91L, 58L, 13L, 4L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 3L, - 0L, 0L, 0L, 0L, 0L, 1L, 17L, 3L, 2L, 0L, 0L, 3L, 0L, 0L, 2L, - 0L, 0L, 1L, 0L, 17L, 1L, 0L, 7L, 10L, 1L, 1L, 0L, 0L, 0L, 0L, - 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, - 8L, 2L, 1L, 27L, 0L, 12L, 4L, 0L, 57L, 12L, 1L, 22L, 20L, 9L, - 2L, 1L, 2L, 0L, 0L, 2L, 0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, - 1L, 1L, 0L, 0L, 1L, 108L, 27L, 6L, 201L, 9L, 128L, 79L, 29L, - 444L, 56L, 11L, 232L, 40L, 64L, 14L, 2L, 7L, 1L, 0L, 13L, 0L, - 0L, 1L, 8L, 0L, 0L, 0L, 0L, 0L, 7L, 5L, 8L, 19L, 0L, 0L, 24L, - 3L, 0L, 16L, 4L, 22L, 2L, 0L, 16L, 8L, 0L, 31L, 12L, 2L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, - 1L, 0L, 0L, 0L, 0L, 2L, 0L, 5L, 1L, 1L, 0L, 0L, 2L, 2L, 0L, 1L, - 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 2L, - 0L, 0L, 1L, 1L, 0L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, - 0L, 1L, 1L, 2L, 0L, 4L, 19L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 3L, 1L, 0L, 0L, 1L, 0L, 6L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 2L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 7L, 0L, 0L, 1L, 1L, 0L, 1L, - 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, - 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, - 0L, 0L, 0L, 0L), dim = 35:36, - dimnames = list(c("Acer_monspessulanum", - "Amelanchier_ovalis", - "Berberis_hispanica", - "Bupleurum_gibraltaricum", - "Cistus_albidus", - "Crataegus_laciniata", - "Crataegus_monogyna", - "Cytisus_scoparius", - "Daphne_gnidium", - "Digitalis_obscura", - "Dorycnium_pentaphyllum", - "Fraxinus_angustifolia", - "Genista_cinerea", - "Helleborus_foetidus", - "Juniperus_oxycedrus", - "Juniperus_phoenicea", - "Lavandula_latifolia", - "Linum_sp", - "Phillyrea_latifolia", - "Pinus_halepensis", - "Pistacia_terebinthus", - "Prunus_mahaleb", - "Prunus_spinosa", - "Quercus_coccifera", - "Quercus_faginea", - "Quercus_ilex", - "Rhamnus_alaternus", - "Rhamnus_lycioides", - "Rhamnus_myrtifolius", - "Rosa_sp1", - "Ruscus_aculeatus", - "Staehelina_dubia", - "Thymus_mastichina", - "Thymus_orospedanus", - "Ulex_parviflorus"), - c("Acer_monspessulanum", - "Amelanchier_ovalis", - "Berberis_hispanica", - "Bupleurum_gibraltaricum", - "Cistus_albidus", - "Crataegus_laciniata", - "Crataegus_monogyna", - "Cytisus_scoparius", - "Daphne_gnidium", - "Digitalis_obscura", - "Dorycnium_pentaphyllum", - "Fraxinus_angustifolia", - "Genista_cinerea", - "Helleborus_foetidus", - "Juniperus_oxycedrus", - "Juniperus_phoenicea", - "Lavandula_latifolia", - "Linum_sp", "Open", - "Phillyrea_latifolia", - "Pinus_halepensis", - "Pistacia_terebinthus", - "Prunus_mahaleb", - "Prunus_spinosa", - "Quercus_coccifera", - "Quercus_faginea", - "Quercus_ilex", - "Rhamnus_alaternus", - "Rhamnus_lycioides", - "Rhamnus_myrtifolius", - "Rosa_sp1", - "Ruscus_aculeatus", - "Staehelina_dubia", - "Thymus_mastichina", - "Thymus_orospedanus", - "Ulex_parviflorus" - )))) - -}) diff --git a/tests/testthat/test-comm_summary.R b/tests/testthat/test-comm_summary.R deleted file mode 100644 index 48bda61..0000000 --- a/tests/testthat/test-comm_summary.R +++ /dev/null @@ -1,31 +0,0 @@ -test_that("comm_summary works", { - Ventisquero <- comm_subset(RecruitNet, site = "Ventisquero") - out <- comm_summary(Ventisquero) - expect_equal(out, - structure( - list( - `Local Community` = "Ventisquero", - Country = "Spain", - Latitude = 37.61555556, - Longitude = -3.734166667, - `Year of sampling` = 2020L, - `Site responsible` = "Julio M. Alcántara", - Biome = "Mediterranean Forests Woodlands and Scrub", - Vegetation = "Forest", - `Plant Community` = "Quercus ilex forest", - `Successional stage` = "Late", - Disturbance = NA, - `Sampling method` = "RN", - `Number of plots` = 20L, - `Plot area (m2)` = 500L, - `Area sampled (m2)` = 10000L, - `Number of plant species` = 43L, - `Contains Open node` = "Yes", - `Number of woody species` = 39L, - `Number of herb species` = 4L, - `Number of other types` = 0L - ), - row.names = "Value", - class = "data.frame" - )) -}) diff --git a/tests/testthat/test-comm_to_RN.R b/tests/testthat/test-comm_to_RN.R deleted file mode 100644 index 39f03ab..0000000 --- a/tests/testthat/test-comm_to_RN.R +++ /dev/null @@ -1,35 +0,0 @@ -test_that("comm_to_RN produces expected output", { - data(RecruitNet) - data(CanopyCover) - out <- comm_to_RN(RecruitNet, CanopyCover, site = "Ventisquero") - - expect_equal(head(out), - structure( - list( - Canopy = c( - "Acer_monspessulanum", - "Acer_monspessulanum", - "Acer_monspessulanum", - "Acer_monspessulanum", - "Acer_monspessulanum", - "Acer_monspessulanum" - ), - Recruit = c( - "Acer_monspessulanum", - "Berberis_hispanica", - "Cistus_albidus", - "Crataegus_monogyna", - "Daphne_gnidium", - "Helleborus_foetidus" - ), - fij = c(2L, 0L, 0L, 20L, 1L, 1L), - Tij = c(1L, 0L, 0L, 8L, 1L, 1L), - Pij = c(1L, 0L, 0L, 1L, 1L, 1L), - cj = c(96.39, 96.39, 96.39, 96.39, 96.39, 96.39), - ci = c(96.39, 31.39, 10.15, 214.34, 32.14, 12.76) - ), - row.names = c(NA, 6L), - class = "data.frame" - )) - -}) diff --git a/tests/testthat/test-link_completeness.R b/tests/testthat/test-link_completeness.R deleted file mode 100644 index 2a9c472..0000000 --- a/tests/testthat/test-link_completeness.R +++ /dev/null @@ -1,23 +0,0 @@ -test_that("link_completeness works", { - data(RecruitNet) - - out <- link_completeness(RecruitNet, "Ventisquero", "incidence") - expect_equal(out, structure( - list(`Incidence based estimate` = c(299, 488.535555555556, 0.612033242206868, 0.8813)), - class = "data.frame", - row.names = c("Lobs", "Lest", "Completeness Links (q=0)", "Coverage Links (q=1)") - )) - - - out <- link_completeness(RecruitNet, "Laxe", "abundance") - expect_equal(out, structure( - list(`Abundance based estimate` = c(99, 115.660283416316, 0.855955018229141, 0.9923)), - class = "data.frame", - row.names = c("Lobs", "Lest", "Completeness of links (q=0)", "Coverage of links (q=1)") - )) - - expect_warning(link_completeness(RecruitNet, "Laxe", "incidence")) - - expect_error(link_completeness(RecruitNet, "LosReyes", "incidence")) - -})