R/rptR.R

#' rptR: Repeatability Estimation for Gaussian and Non-Gaussian data
#' 
#' 
#' @description A collection of functions for calculating point estimates, interval estimates and 
#'   significance tests of the repeatability (intra-class correlation coefficient) as well as 
#'   variance components in mixed effects models. The function \link{rpt} is a wrapper function 
#'   that calls more specialised functions as required. Specialised functions can also be called 
#'   directly (see \link{rpt} for details). All
#'   functions return lists of values in the form of an S3 object \code{rpt}. The function \link{summary.rpt} produces summaries in a
#'   detailed format and \link{plot.rpt} plots bootstraps or permutation results.
#'   
#' @note
#' 
#' Currently there four different functions depending on the distribution and type of response: (1)
#' \link{rptGaussian} for a Gaussian response distributions, (2) \link{rptPoisson} for Poisson-distributed data, 
#' (3) \link{rptBinary} for binary response following binomial distributions and (4) \link{rptProportion} for
#' response matrices with a column for successes and a column for failures that are analysed as proportions
#' following binomial distributions. All function use a mixed model framework in \code{lme4}, 
#' and the non-Gaussian functions use an observational level random effect to account
#' for overdispersion.
#' 
#' All functions use the argument \code{formula}, which is the same formula interface as in the
#' \code{lme4} package (indeed models are fitted by \code{lmer} or \code{glmer}). Repeatabilites are 
#' calculated for the response variable, while one or
#' more grouping factors of interest can be assigned as random effects in the form (1|group) and
#' have to be specified with the \code{grname} argument. This allows to estimate adjusted
#' repeatabilities (controlling for fixed effects) and the estimation of multiple variance 
#' components simultaneously (multiple random effects). All variables have to be columns 
#' in a \code{data.frame} given in the \code{data} argument. The \code{link} argument specifies
#' the link function for a given non-Gaussian distribtion. 
#' 
#' The argument \code{ratio} allows switching to raw variances rather than ratios of variances 
#' to be estimated and The argument \code{adjusted} allows switching to an estimation where the 
#' variance explained by fixed effects is included in the denominator of the repeatability 
#' calculation. The reserved \code{grname} terms "Residual", "Overdispersion" and "Fixed" allow 
#' the estimation of oversipersion variance, residual variance and variance explained by 
#' fixed effects, respectively. All computation can be parallelized with the \code{parallel} 
#' argument, which enhances computation speed for larger computations.
#' 
#' When using \code{rptR} please cite:   
#' 
#' Stoffel, M., Nakagawa, S. & Schielzeth, H. (2017) \emph{rptR: Repeatability estimation and variance decomposition by generalized linear
#' mixed-effects models.}. Methods Ecol Evol. Accepted Author Manuscript. doi:10.1111/2041-210X.12797
#' 
#' @author Martin Stoffel (martin.adam.stoffel@@gmail.com), Shinichi Nakagawa
#'   (s.nakagawa@unsw.edu.au) & Holger Schielzeth (holger.schielzeth@@uni-jena.de)
#' 
#' @references Nakagawa, S. & Schielzeth, H. (2010) \emph{Repeatability for Gaussian and
#'   non-Gaussian data: a practical guide for biologists}. Biological Reviews 85: 935-956
#'   
#' @docType package
#' @name rptR
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rptR documentation built on May 2, 2019, 10:36 a.m.