fullfact (version 1.0)

powerLmer: Power analysis for normal data

Description

Extracts the power values of dam, sire, and dam by sire variance components from a linear mixed-effect model using the lmer function of the lme4 package.

Usage

powerLmer(varcomp, nval, alpha = 0.05, nsim = 100, ml = F)

Arguments

varcomp

Vector of known dam, sire, dam by sire, and residual variance components, i.e. c(dam,sire,ds,res).

nval

Vector of known dam, sire, and offspring per family sample sizes, i.e. c(dam,sire, offspring).

alpha

Statistical significance value. Default is 0.05.

nsim

Number of simulations. Default is 100.

ml

Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood.

Value

A data frame with the sample sizes, variance component inputs, variance component outputs, and power values.

Details

Extracts the dam, sire, dam, and dam by sire power values. Power values are calculated by stochastically simulation data and then calculating the proportion of significance values less than alpha for each component (Bolker 2008). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).

References

Bolker BM. 2008. Ecological models and data in R. Princeton University Press, New Jersey.

Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3): 127-135. DOI: 10.1016/j.tree.2008.10.008

Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.

See Also

powerLmer2, powerLmer3

Examples

#100 simulations
#pwr_L1<- powerLmer(varcomp=c(0.19,0.03,0.02,0.76),nval=c(10,10,20))
#pwr_L1
#5simulations
pwr_L1<- powerLmer(varcomp=c(0.19,0.03,0.02,0.76),nval=c(10,10,20),nsim=5)
pwr_L1