observGlmer2: Variance components for non-normal data 2

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. Options to include one random position and/or one random block effect(s).


observGlmer2(observ, dam, sire, response, fam_link, position = NULL, block = NULL,
quasi = F)



Data frame of observed data.


Column name containing dam(female) parent identity information.


Column name containing sire(male) parent identity information.


Column name containing the offspring (response) phenotype values.


The family and link in family(link) format. Supported options are binomial(logit), binomial(probit), poisson(log), and poisson (sqrt).


Optional column name containing position factor information.


Optional column name containing block factor information.


Incorporate overdispersion or quasi-error structure.


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. Extracts optional position and block 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). Significance values for the random effects are determined using likelihood ratio tests (Bolker et al. 2009).


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.


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

See Also

observGlmer, observGlmer3


data(chinook_survival) #Chinook salmon offspring survival
## Convert replicate-level recorded data to individual-level (binary) data
chinook_survival2<- buildBinary(dat=chinook_survival,copy=c(2:6,9),one="alive",zero="dead")
## Not run: survival_mod2<- observGlmer2(observ=chinook_survival2,dam="dam",sire="sire",
response="status",fam_link=binomial(logit),position="tray")  #a few minutes
## End(Not run)

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