rpt.mcmcLMM: LMM-based repeatability estimated using MCMC sampling In rptR: Repeatability for Gaussian and non-Gaussian data

Description

Calculates repeatability from a linear mixed-effects models fitted by MCMC

Usage

 `1` ``` rpt.mcmcLMM(y, groups, CI=0.95, prior=NULL, verbose=FALSE, ...) ```

Arguments

 `y` Vector of a response values `groups` Vector of group identities `CI` Width of the Bayesian credible interval (defaults to 0.95) `prior` List of prior values passed to the MCMCglmm function in MCMCglmm (see there for more details). Default priors will be used if prior is `NULL`. `verbose` Whether or not MCMCglmm should print MH diagnostics are printed to screen. Defaults to FALSE. `...` Additonal arguements that are passed on to MCMCglmm (e.g. length of chain, thinning interval).

Details

Models are fitted using the MCMCglmm function in MCMCglmm. Models are fitted with `prior=list(R=list(V=1,n=10e-2), G=list(G1=list(V=1,n=10e-2)))` unless other priors are specified in the call.

Value

Returns an object of class rpt that is a a list with the following elements:

 `datatype` Response distribution (here: "Gaussian"). `method` Method used to calculate repeatability (intra-class correlation, ICC) (here: "MCMC"). `CI` Width of the Bayesian credibility interval. `R` Point estimate for repeatability (intra-class correlation, ICC), i.e. the mode of the posterior distribution. `se` Standard error (se) for repeatability (ICC), i.e. the standard deviation of the posterior distribution. Note that the distribution might not be symmetrical, in which case the se is less informative. `CI.R` Bayesian credibility interval for the repeatability (ICC) based on the posterior distribution of R. `P` Significace test, returned as `NA`, since the Bayesian approach conflicts with the null hypothesis testing. `R.post` MCMC samples form the posterior distributions of R.

Author(s)

Holger Schielzeth ([email protected]) & Shinichi Nakagawa ([email protected])

References

Carrasco, J. L. and Jover, L. (2003). Estimating the generalized concordance correlation coefficient through variance components. Biometrics 59: 849-858.

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

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# repeatability estimation for tarsus length - a very high R data(BodySize) attach(BodySize) (rpt.BS <- rpt.mcmcLMM(Tarsus, BirdID)) detach(BodySize) # repeatability estimation for weight (body mass) - a lower R than the previous one data(BodySize) attach(BodySize) (rpt.Weight <- rpt.mcmcLMM(Weight, BirdID)) detach(BodySize) ```