knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
Edlin Guerra-Castro, Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro
SSP is an R package design to estimate sampling effort in studies of ecological communities based on the definition of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon 2015), simulation of data and resampling (Guerra-Castro et al., 2020).
SSP includes seven functions: assempar
for extrapolation of assemblage parameters using pilot data; simdata
for simulation of several data sets based on extrapolated parameters; datquality
for evaluation of plausibility of simulated data; sampsd
for repeated estimations of MultSE for different sampling designs in simulated data sets; summary_sd
for summarizing the behavior of MultSE for each sampling design across all simulated data sets, ioptimum
for identification of the optimal sampling effort, and plot_ssp
to plot sampling effort vs MultSE.
The SSP package will be available on CRAN but can be downloaded from github using the following commands:
## Packages needed to build SSP and vignettes install.packages(pkgs = c('devtools', 'knitr', 'rmarkdown')) library(devtools) library(knitr) library(rmarkdown) ## install the latest version of SSP from github install_github('edlinguerra/SSP', build_vignettes = TRUE) library(SSP)
For examples about how to use SSP, see help('SSP')
after instalation.
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