fullfact (version 1.0)

observLmer3: Variance components for normal data 3

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, dam by sire, and any additional fixed and/or random effects.

Usage

observLmer3(observ, dam, sire, response, remain, ml = F, iter = 1000)

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.

remain

Remaining formula using lme4 package format.

ml

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

iter

Number of iterations for computing the parametric bootstrap significance value for any fixed effects.

Value

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

Details

Extracts the dam, sire, dam, dam by sire, and residual variance components. Extracts any additional fixed effect and random effect variance components. The fixed-effect variance component is as a single group using the method described by Nakagawa and Schielzeth (2013). 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). Significance values for any fixed effects are determined using likelihood ratio tests and a parametric bootstrap method (Bolker et al. 2009) from the mixed function of the afex package.

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.

Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x

See Also

observLmer, observLmer2

Examples

data(chinook_length) #Chinook salmon offspring length
#just a few iterations for the p-value of fixed effect
length_mod3<- observLmer3(observ=chinook_length,dam="dam",sire="sire",response="length",
remain="egg_size + (1|tray)",iter=5)
length_mod3