rptR: rptR: Repeatability Estimation for Gaussian and Non-Gaussian...

In 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 rpt is a wrapper function that calls more specialised functions as required. Specialised functions can also be called directly (see rpt for details). All functions return lists of values in the form of an S3 object rpt. The function summary.rpt produces summaries in a detailed format and plot.rpt plots bootstraps or permutation results.

Note

Currently there four different functions depending on the distribution and type of response: (1) rptGaussian for a Gaussian response distributions, (2) rptPoisson for Poisson-distributed data, (3) rptBinary for binary response following binomial distributions and (4) 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 lme4, and the non-Gaussian functions use an observational level random effect to account for overdispersion.

All functions use the argument formula, which is the same formula interface as in the lme4 package (indeed models are fitted by lmer or 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 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 data.frame given in the data argument. The link argument specifies the link function for a given non-Gaussian distribtion.

The argument ratio allows switching to raw variances rather than ratios of variances to be estimated and The argument adjusted allows switching to an estimation where the variance explained by fixed effects is included in the denominator of the repeatability calculation. The reserved 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 parallel argument, which enhances computation speed for larger computations.

When using rptR please cite:

Stoffel, M., Nakagawa, S. & Schielzeth, H. (2017) 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(s)

Martin Stoffel ([e-mail]), Shinichi Nakagawa ([e-mail]) & Holger Schielzeth ([e-mail])

References

Nakagawa, S. & Schielzeth, H. (2010) Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85: 935-956

rptR documentation built on May 2, 2019, 10:36 a.m.