resampGlmer: Bootstrap components for non-normal data
In fullfact: Full Factorial Breeding Analysis

Description Usage Arguments Details Value Note References See Also Examples

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

1	resampGlmer(resamp, dam, sire, response, fam_link, start, end, quasi = F)

`resamp`	Data frame of bootstrap resampled 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.
`fam_link`	The family and link in family(link) format. Supported options are binomial(link="logit"), binomial(link="probit"), poisson(link="log"), and poisson(link="sqrt").
`start`	Starting model number.
`end`	Ending model number.
`quasi`	Incorporate overdispersion or quasi-error structure.

Used for bootstrap resampled data set produced using resampRepli or resampFamily. Laplace approximation parameter estimation is used, which is a true likelihood method (Bolker et al. 2009). For the overdispersion option, an observation-level random effect is added to the model (Atkins et al. 2013). Extracts the dam, sire, dam, and dam by sire variance components. The residual variance component for the fam_links are described by Nakagawa and Schielzeth (2010, 2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).

A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. The number of rows in the data frame matches the number of iterations in the resampled data set and each row represents a model number.

The Laplace approximation is used because there were fewer disadvantages relative to penalized quasi-likelihood and Gauss-Hermite quadrature parameter estimation (Bolker et al. 2009). That is, penalized quasi-likelihood is not recommended for count responses with means less than 5 and binary responses with less than 5 successes per group. Gauss-Hermite quadrature is not recommended for more than two or three random effects because of the rapidly declining analytical speed with the increasing number of random effects.

Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. 2013. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors 27(1): 166-177. DOI: 10.1037/a0029508

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. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x

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

resampGlmer2, resampGlmer3

data(chinook_resampS) #5 iterations

#survival_rcomp<- resampGlmer(resamp=survival_datR,dam="dam",sire="sire",
#response="status",fam_link=binomial(link="logit"),start=1,end=1000)
## Not run: survival_rcomp<- resampGlmer(resamp=chinook_resampS,dam="dam",sire="sire",
response="status",fam_link=binomial(link="logit"),start=1,end=5) 
## End(Not run)