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#' Print a rpt object
#'
#' Displays the results a rpt object (i.e. the result of a rpt function call) in a nice form.
#'
#' @param x An rpt object returned from one of the rpt functions
#' @param \dots Additional arguments; none are used in this method.
#'
#' @return
#' Abbreviations in the print.rpt output:
#' \item{R}{Repeatability.}
#' \item{SE}{Standard error of R.}
#' \item{CI}{Confidence interval of R derived from parametric bootstrapping.}
#' \item{P}{P-value}
#' \item{LRT}{Likelihood-ratio test}
#' \item{Permutation}{Permutation of residuals}
#'
#' @references
#' Nakagawa, S. & Schielzeth, H. (2010) \emph{Repeatability for Gaussian and
#' non-Gaussian data: a practical guide for biologists}. Biological Reviews 85: 935-956
#'
#' @author Holger Schielzeth (holger.schielzeth@@uni-jena.de),
#' Shinichi Nakagawa (s.nakagawa@unsw.edu.au),
#' Martin Stoffel (martin.adam.stoffel@@gmail.com)
#'
#'
#' @keywords models
#'
#' @export
#'
#'
#'
print.rpt <- function(x, ...) {
if (x$ratio == TRUE) {
header_gaussian <- "Repeatability estimation using the lmm method"
header_nongaussian <- "Repeatability estimation using the glmm method and"
header2 <- "Repeatability for "
PE <- "R = "
subheader_link <- "Link-scale approximation:"
subheader_org <- "Original-scale approximation:"
} else if (x$ratio == FALSE) {
header_gaussian <- "Variance estimation using the lmm method"
header_nongaussian <- "Variance estimation using the glmm method and"
header2 <- "Variance of "
PE <- "Var = "
subheader_link <- "Link-scale approximation:"
subheader_org <- "Original-scale approximation:"
}
if (x$datatype == "Gaussian") {
cat("\n\n")
cat(header_gaussian, "\n\n")
for (i in 1:length(x$R)) {
cat(header2, names(x$R)[i], "\n",
PE, round(unlist(x$R[i]), 3), "\n", sep = "")
if (is.na(unlist(x$se)[1])){ # check if permutations have been done
cat("SE = ", x$se, "\n")
} else {
cat("SE = ", round(unlist(x$se[i, ]), 3), "\n", sep = "")
}
if (is.na(unlist(x$CI_emp)[1])){ # check if bootstraps have been done
cat("CI = [", x$CI_emp[1], ", ", x$CI_emp[2], "]", "\n", sep = "")
} else {
cat("CI = [", round(x$CI_emp[i, 1], 3), ", ", round(x$CI_emp[i, 2], 3), "]", "\n", sep = "")
}
cat("P = ", signif(x$P[i, 1], 3), " [", "LRT", "]", "\n",
" ", signif(x$P[i, 2], 3), " [", "Permutation", "]", "\n\n", sep = "")
}
}
if (x$datatype == "Poisson" | x$datatype == "Binary" | x$datatype == "Proportion") {
cat("\n\n")
cat(header_nongaussian, x$link,
"link", "\n\n")
# grnames <- names(x$R)
for (i in 1:ncol(x$R)) {
grname <- names(x$R)[i]
cat(header2, names(x$R)[i], "\n",
"--------------------------------", "\n",
subheader_link,
"\n", PE, round(x$R["R_link", grname], 3), "\n", "SE = ", round(x$se["se_link", grname], 3),
"\n", "CI = [", round(x$CI_emp$CI_link[grname, 1], 3), ", ", round(x$CI_emp$CI_link[grname, 2], 3), "]", "\n",
"P = ", signif(x$P[grname, "LRT_P"], 3), " [", "LRT", "]", "\n",
" ", signif(x$P[grname, "P_permut_link"], 3), " [", "Permutation", "]", "\n\n", sep = "")
if (x$ratio == TRUE) {
cat(subheader_org,
"\n", PE, round(x$R["R_org", grname], 3), "\n", "SE = ", round(x$se["se_org", grname], 3), "\n",
"CI = [", round(x$CI_emp$CI_org[grname, 1], 3), ", ", round(x$CI_emp$CI_org[grname, 2], 3), "]", "\n",
"P = ", signif(x$P[grname, "LRT_P"], 3), " [", "LRT", "]", "\n",
" ", signif(x$P[grname, "P_permut_org"], 3), " [", "Permutation", "]", "\n\n", sep = "")
}
}
}
}
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