The goal of rptR is to provide point estimates,
confidence intervals and significance tests for the
repeatability (intra-class correlation coefficient) of
measurements based on generalised linear mixed models (GLMMs). The
function ?summary.rpt produces summaries in a detailed
format, whereby ?plot.rpt plots the distributions of
bootstrap or permutation test estimates.
When using rptR, please cite our paper:
Stoffel, M. A., Nakagawa, S., & Schielzeth, H. (2017). rptR:
Repeatability estimation and variance decomposition by generalized
linear mixed-effects models. Methods in Ecology and Evolution,
8(11), 1639-1644. 
You can install the stable version of rptR from CRAN
with:
install.packages("rptR")Or the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("mastoffel/rptR", build_vignettes = TRUE, dependencies = TRUE)
# manual
browseVignettes("rptR")If you find a bug, please report a minimal reproducible example in the issues.
Repeatability of beetle body length (BodyL) for both
Container and Population while adjusting for
Treatment and Sex:
library(rptR)
data(BeetlesBody)
rpts <- rpt(BodyL ~ Treatment + Sex + (1 | Container) + (1 | Population),
grname = c("Container", "Population"), data = BeetlesBody,
datatype = "Gaussian", nboot = 100, npermut = 100)summary(rpts)
#>
#> Repeatability estimation using the lmm method
#>
#> Call = rpt(formula = BodyL ~ Treatment + Sex + (1 | Container) + (1 | Population), grname = c("Container", "Population"), data = BeetlesBody, datatype = "Gaussian", nboot = 100, npermut = 100)
#>
#> Data: 960 observations
#> ----------------------------------------
#>
#> Container (120 groups)
#>
#> Repeatability estimation overview:
#> R SE 2.5% 97.5% P_permut LRT_P
#> 0.0834 0.0247 0.0449 0.135 0.01 0
#>
#> Bootstrapping and Permutation test:
#> N Mean Median 2.5% 97.5%
#> boot 100 0.08428 0.077960 0.0449 0.1352
#> permut 100 0.00428 0.000315 0.0000 0.0232
#>
#> Likelihood ratio test:
#> logLik full model = -1528.553
#> logLik red. model = -1555.264
#> D = 53.4, df = 1, P = 1.34e-13
#>
#> ----------------------------------------
#>
#>
#> Population (12 groups)
#>
#> Repeatability estimation overview:
#> R SE 2.5% 97.5% P_permut LRT_P
#> 0.491 0.107 0.233 0.644 0.02 0
#>
#> Bootstrapping and Permutation test:
#> N Mean Median 2.5% 97.5%
#> boot 100 0.477 0.491 0.233 0.644
#> permut 100 0.454 0.453 0.422 0.483
#>
#> Likelihood ratio test:
#> logLik full model = -1528.553
#> logLik red. model = -1595.399
#> D = 134, df = 1, P = 3.19e-31
#>
#> ----------------------------------------rptR estimates uncertainties around repeatability
estimates with parametric bootstrapping. The distribution of bootstrap
estimates can easily be plotted.
plot(rpts, grname="Container", type="boot", cex.main=0.8, col = "#ECEFF4")
plot(rpts, grname="Population", type="boot", cex.main=0.8, col = "#ECEFF4")
