Nothing
R/jSDM-package.R
In jSDM: Joint Species Distribution Models
#' @docType package
#' @name jSDM-package
#' @aliases jSDM-package
#' @title joint species distribution models
#' @description \code{jSDM} is an R package for fitting joint species distribution models (JSDM) in a hierarchical Bayesian framework.
#'
#' The Gibbs sampler is written in 'C++'. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation efficiency.
#'\tabular{ll}{
#' Package: \tab jSDM\cr
#' Type: \tab Package\cr
#' Version: \tab 0.2.1\cr
#' Date: \tab 2019-01-11\cr
#' License: \tab GPL-3 \cr
#' LazyLoad: \tab yes\cr }
#'
#' @details The package includes the following functions to fit various species distribution models :
#'
#' \tabular{ll}{
#' function \tab data-type \cr
#' \code{\link{jSDM_binomial_logit}} \tab presence-absence \cr
#' \code{\link{jSDM_binomial_probit}} \tab presence-absence \cr
#' \code{\link{jSDM_binomial_probit_sp_constrained}} \tab presence-absence \cr
#' \code{\link{jSDM_binomial_probit_long_format}} \tab presence-absence \cr
#' \code{\link{jSDM_poisson_log}} \tab abundance \cr }
#'
#' \itemize{
#'
#' \item{ \code{\link{jSDM_binomial_probit}} : \cr
#' \cr
#' \bold{Ecological process:}
#' \deqn{y_{ij} \sim \mathcal{B}ernoulli(\theta_{ij})}{y_ij ~ Bernoulli(\theta_ij),}
#' where \tabular{ll}{
#' if \code{n_latent=0} and \code{site_effect="none"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j}{(\theta_ij) = X_i \beta_j} \cr
#' if \code{n_latent>0} and \code{site_effect="none"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j}{(\theta_ij) = X_i \beta_j + W_i \lambda_j} \cr
#' if \code{n_latent=0} and \code{site_effect="fixed"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' if \code{n_latent>0} and \code{site_effect="fixed"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} \cr
#' if \code{n_latent=0} and \code{site_effect="random"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} \cr
#' if \code{n_latent>0} and \code{site_effect="random"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' }}
#'
#' \item{ \code{\link{jSDM_binomial_probit_sp_constrained}} : \cr
#' \cr
#' This function allows to fit the same models than the function \code{\link{jSDM_binomial_probit}} except for models not including latent variables, indeed \code{n_latent} must be greater than zero in this function.
#' At first, the function fit a JSDM with the constrained species arbitrarily chosen as the first ones in the presence-absence data-set.
#' Then, the function evaluates the convergence of MCMC \eqn{\lambda} chains using the Gelman-Rubin convergence diagnostic (\eqn{\hat{R}}).
#' It identifies the species (\eqn{\hat{j}_l}) having the higher \eqn{\hat{R}} for \eqn{\lambda_{\hat{j}_l}}.
#' These species drive the structure of the latent axis \eqn{l}.
#' The \eqn{\lambda} corresponding to this species are constrained to be positive and placed on the diagonal of the \eqn{\Lambda} matrix for fitting a second model.
#' This usually improves the convergence of the latent variables and factor loadings. The function returns the parameter posterior distributions for this second model.
#'}
#'
#'\item{ \code{\link{jSDM_binomial_logit}} : \cr
#' \cr
#' \bold{Ecological process : }
#' \deqn{y_{ij} \sim \mathcal{B}inomial(\theta_{ij},t_i)}{y_ij ~ Binomial(\theta_ij,t_i),}
#' where \tabular{ll}{
#' if \code{n_latent=0} and \code{site_effect="none"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j}{(\theta_ij) = X_i \beta_j} \cr
#' if \code{n_latent>0} and \code{site_effect="none"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j}{(\theta_ij) = X_i \beta_j + W_i \lambda_j} \cr
#' if \code{n_latent=0} and \code{site_effect="fixed"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} \cr
#' if \code{n_latent>0} and \code{site_effect="fixed"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} \cr
#' if \code{n_latent=0} and \code{site_effect="random"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' if \code{n_latent>0} and \code{site_effect="random"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' }}
#'
#' \item{ \code{\link{jSDM_poisson_log}} : \cr
#' \cr
#' \bold{Ecological process : }
#' \deqn{y_{ij} \sim \mathcal{P}oisson(\theta_{ij})}{y_ij ~ Poisson(\theta_ij),}
#' where \tabular{ll}{
#' if \code{n_latent=0} and \code{site_effect="none"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j}{(\theta_ij) = X_i \beta_j} \cr
#' if \code{n_latent>0} and \code{site_effect="none"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j}{(\theta_ij) = X_i \beta_j + W_i \lambda_j} \cr
#' if \code{n_latent=0} and \code{site_effect="fixed"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} \cr
#' if \code{n_latent>0} and \code{site_effect="fixed"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} \cr
#' if \code{n_latent=0} and \code{site_effect="random"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' if \code{n_latent>0} and \code{site_effect="random"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' }}
#'
#' \item{ \code{\link{jSDM_binomial_probit_long_format}} : \cr
#' \cr
#' \bold{Ecological process:}
#' \deqn{y_n \sim \mathcal{B}ernoulli(\theta_n)}{y_n ~ Bernoulli(\theta_n)} such as \eqn{species_n=j} and \eqn{site_n=i},
#' where \tabular{ll}{
#' if \code{n_latent=0} and \code{site_effect="none"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j} \cr
#' if \code{n_latent>0} and \code{site_effect="none"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + W_i \lambda_j} \cr
#' if \code{n_latent=0} and \code{site_effect="fixed"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' if \code{n_latent>0} and \code{site_effect="fixed"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + W_i \lambda_j + \alpha_i} \cr
#' if \code{n_latent=0} and \code{site_effect="random"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + \alpha_i} \cr
#' if \code{n_latent>0} and \code{site_effect="random"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + W_i \lambda_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' }}
#'
#' }
#' @author
#' Ghislain Vieilledent <ghislain.vieilledent@cirad.fr>
#'
#' Jeanne Clément <jeanne.clement16@laposte.net>
#'
#' Frédéric Gosselin <frederic.gosselin@inrae.fr>
#'
#'@references
#' Chib, S. and Greenberg, E. (1998) Analysis of multivariate probit models. \emph{Biometrika}, 85, 347-361.
#'
#' Warton, D. I.; Blanchet, F. G.; O'Hara, R. B.; O'Hara, R. B.; Ovaskainen, O.; Taskinen, S.; Walker, S. C. and Hui, F. K. C. (2015) So Many Variables: Joint Modeling in Community Ecology. \emph{Trends in Ecology & Evolution}, 30, 766-779.
#'
#' Ovaskainen, O., Tikhonov, G., Norberg, A., Blanchet, F. G., Duan, L., Dunson, D., Roslin, T. and Abrego, N. (2017) How to make more out of community data? A conceptual framework and its implementation as models and software. \emph{Ecology Letters}, 20, 561-576.
#'
#' @keywords MCMC Metropolis algorithm binomial biodiversity logistic model multivariate regression
#' @importFrom Rcpp evalCpp
#' @importFrom graphics par
#' @importFrom stats median model.frame model.matrix pnorm quantile nobs
#' @useDynLib jSDM , .registration=TRUE
NULL
Try the jSDM package in your browser
Any scripts or data that you put into this service are public.
jSDM documentation built on July 26, 2023, 5:21 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' @docType package
#' @name jSDM-package
#' @aliases jSDM-package
#' @title joint species distribution models
#' @description \code{jSDM} is an R package for fitting joint species distribution models (JSDM) in a hierarchical Bayesian framework.
#'
#' The Gibbs sampler is written in 'C++'. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation efficiency.
#'\tabular{ll}{
#' Package: \tab jSDM\cr
#' Type: \tab Package\cr
#' Version: \tab 0.2.1\cr
#' Date: \tab 2019-01-11\cr
#' License: \tab GPL-3 \cr
#' LazyLoad: \tab yes\cr }
#'
#' @details The package includes the following functions to fit various species distribution models :
#'
#' \tabular{ll}{
#' function \tab data-type \cr
#' \code{\link{jSDM_binomial_logit}} \tab presence-absence \cr
#' \code{\link{jSDM_binomial_probit}} \tab presence-absence \cr
#' \code{\link{jSDM_binomial_probit_sp_constrained}} \tab presence-absence \cr
#' \code{\link{jSDM_binomial_probit_long_format}} \tab presence-absence \cr
#' \code{\link{jSDM_poisson_log}} \tab abundance \cr }
#'
#' \itemize{
#'
#' \item{ \code{\link{jSDM_binomial_probit}} : \cr
#' \cr
#' \bold{Ecological process:}
#' \deqn{y_{ij} \sim \mathcal{B}ernoulli(\theta_{ij})}{y_ij ~ Bernoulli(\theta_ij),}
#' where \tabular{ll}{
#' if \code{n_latent=0} and \code{site_effect="none"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j}{(\theta_ij) = X_i \beta_j} \cr
#' if \code{n_latent>0} and \code{site_effect="none"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j}{(\theta_ij) = X_i \beta_j + W_i \lambda_j} \cr
#' if \code{n_latent=0} and \code{site_effect="fixed"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' if \code{n_latent>0} and \code{site_effect="fixed"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} \cr
#' if \code{n_latent=0} and \code{site_effect="random"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} \cr
#' if \code{n_latent>0} and \code{site_effect="random"} \tab probit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' }}
#'
#' \item{ \code{\link{jSDM_binomial_probit_sp_constrained}} : \cr
#' \cr
#' This function allows to fit the same models than the function \code{\link{jSDM_binomial_probit}} except for models not including latent variables, indeed \code{n_latent} must be greater than zero in this function.
#' At first, the function fit a JSDM with the constrained species arbitrarily chosen as the first ones in the presence-absence data-set.
#' Then, the function evaluates the convergence of MCMC \eqn{\lambda} chains using the Gelman-Rubin convergence diagnostic (\eqn{\hat{R}}).
#' It identifies the species (\eqn{\hat{j}_l}) having the higher \eqn{\hat{R}} for \eqn{\lambda_{\hat{j}_l}}.
#' These species drive the structure of the latent axis \eqn{l}.
#' The \eqn{\lambda} corresponding to this species are constrained to be positive and placed on the diagonal of the \eqn{\Lambda} matrix for fitting a second model.
#' This usually improves the convergence of the latent variables and factor loadings. The function returns the parameter posterior distributions for this second model.
#'}
#'
#'\item{ \code{\link{jSDM_binomial_logit}} : \cr
#' \cr
#' \bold{Ecological process : }
#' \deqn{y_{ij} \sim \mathcal{B}inomial(\theta_{ij},t_i)}{y_ij ~ Binomial(\theta_ij,t_i),}
#' where \tabular{ll}{
#' if \code{n_latent=0} and \code{site_effect="none"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j}{(\theta_ij) = X_i \beta_j} \cr
#' if \code{n_latent>0} and \code{site_effect="none"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j}{(\theta_ij) = X_i \beta_j + W_i \lambda_j} \cr
#' if \code{n_latent=0} and \code{site_effect="fixed"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} \cr
#' if \code{n_latent>0} and \code{site_effect="fixed"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} \cr
#' if \code{n_latent=0} and \code{site_effect="random"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' if \code{n_latent>0} and \code{site_effect="random"} \tab logit\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' }}
#'
#' \item{ \code{\link{jSDM_poisson_log}} : \cr
#' \cr
#' \bold{Ecological process : }
#' \deqn{y_{ij} \sim \mathcal{P}oisson(\theta_{ij})}{y_ij ~ Poisson(\theta_ij),}
#' where \tabular{ll}{
#' if \code{n_latent=0} and \code{site_effect="none"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j}{(\theta_ij) = X_i \beta_j} \cr
#' if \code{n_latent>0} and \code{site_effect="none"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j}{(\theta_ij) = X_i \beta_j + W_i \lambda_j} \cr
#' if \code{n_latent=0} and \code{site_effect="fixed"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} \cr
#' if \code{n_latent>0} and \code{site_effect="fixed"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} \cr
#' if \code{n_latent=0} and \code{site_effect="random"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + \alpha_i}{(\theta_ij) = X_i \beta_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' if \code{n_latent>0} and \code{site_effect="random"} \tab log\eqn{(\theta_{ij}) = X_i \beta_j + W_i \lambda_j + \alpha_i}{(\theta_ij) = X_i \beta_j + W_i \lambda_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' }}
#'
#' \item{ \code{\link{jSDM_binomial_probit_long_format}} : \cr
#' \cr
#' \bold{Ecological process:}
#' \deqn{y_n \sim \mathcal{B}ernoulli(\theta_n)}{y_n ~ Bernoulli(\theta_n)} such as \eqn{species_n=j} and \eqn{site_n=i},
#' where \tabular{ll}{
#' if \code{n_latent=0} and \code{site_effect="none"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j} \cr
#' if \code{n_latent>0} and \code{site_effect="none"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + W_i \lambda_j} \cr
#' if \code{n_latent=0} and \code{site_effect="fixed"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' if \code{n_latent>0} and \code{site_effect="fixed"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + W_i \lambda_j + \alpha_i} \cr
#' if \code{n_latent=0} and \code{site_effect="random"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + \alpha_i} \cr
#' if \code{n_latent>0} and \code{site_effect="random"} \tab probit\eqn{(\theta_n) = D_n \gamma + X_n \beta_j + W_i \lambda_j + \alpha_i} and \eqn{\alpha_i \sim \mathcal{N}(0,V_\alpha)}{\alpha_i ~ N(0,V_\alpha)} \cr
#' }}
#'
#' }
#' @author
#' Ghislain Vieilledent <ghislain.vieilledent@cirad.fr>
#'
#' Jeanne Clément <jeanne.clement16@laposte.net>
#'
#' Frédéric Gosselin <frederic.gosselin@inrae.fr>
#'
#'@references
#' Chib, S. and Greenberg, E. (1998) Analysis of multivariate probit models. \emph{Biometrika}, 85, 347-361.
#'
#' Warton, D. I.; Blanchet, F. G.; O'Hara, R. B.; O'Hara, R. B.; Ovaskainen, O.; Taskinen, S.; Walker, S. C. and Hui, F. K. C. (2015) So Many Variables: Joint Modeling in Community Ecology. \emph{Trends in Ecology & Evolution}, 30, 766-779.
#'
#' Ovaskainen, O., Tikhonov, G., Norberg, A., Blanchet, F. G., Duan, L., Dunson, D., Roslin, T. and Abrego, N. (2017) How to make more out of community data? A conceptual framework and its implementation as models and software. \emph{Ecology Letters}, 20, 561-576.
#'
#' @keywords MCMC Metropolis algorithm binomial biodiversity logistic model multivariate regression
#' @importFrom Rcpp evalCpp
#' @importFrom graphics par
#' @importFrom stats median model.frame model.matrix pnorm quantile nobs
#' @useDynLib jSDM , .registration=TRUE
NULL
Try the jSDM package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.