rpt.mcmcLMM: LMM-based repeatability estimated using MCMC sampling

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

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

Usage

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	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

See Also

rpt.remlLMM, rpt.aov, rpt.corr, rpt, print.rpt

Examples

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# 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)

rptR documentation built on May 2, 2019, 5:01 p.m.