fullfact (version 1.5.2)

observLmer: Variance components for normal data

Description

Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire.

Usage

observLmer(observ, dam, sire, response, ml = F)

Value

A list object containing the raw variance components, the variance components as a percentage of the total variance component. Also, contains the difference in AIC and BIC, and likelihood ratio test Chi-square and p-value for all random effects.

Arguments

observ

Data frame of observed data.

dam

Column name containing dam (female) parent identity information.

sire

Column name containing sire (male) parent identity information.

response

Column name containing the offspring (response) phenotype values.

ml

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

Details

Extracts the dam, sire, dam, dam by sire, and residual variance components. Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).

References

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

observLmer2, observLmer3

Examples

data(chinook_length) #Chinook salmon offspring length
length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length")
length_mod1