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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")
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.