observLmer: Variance components for 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 linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, and dam by sire.

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

`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.

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).

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.

Maximum likelihood (ML) estimates the parameters that maximize the likelihood of the observed data and has the advantage of using all the data and accounting for non-independence (Lynch and Walsh 1998, p. 779; Bolker et al. 2009). On the other hand, ML has the disadvantage of assuming that all fixed effects are known without error, producing a downward bias in the estimation of the residual variance component. This bias can be large if there are lots of fixed effects, especially if sample sizes are small. Restricted maximum likelihood (REML) has the advantage of not assuming the fixed effects are known and averages over the uncertainty, so there can be less bias in the estimation of the residual variance component. However, REML only maximizes a portion of the likelihood to estimate the effect parameters, but is the preferred method for analyzing large data sets with complex structure.

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.

observLmer2, observLmer3

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2
3

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

[1] "2019-01-21 19:36:20 UTC"
singular fit
singular fit
Time difference of 1.363317 secs
$random
    effect     variance      percent     d.AIC      d.BIC       Chi.sq
1 dam:sire 1.719506e-01 1.730760e+01 111.90386 106.805481 1.139039e+02
2     sire 3.165478e-13 3.186195e-11  -2.00000  -7.098376 1.364242e-12
3      dam 1.900485e-01 1.912924e+01  41.61831  36.519932 4.361831e+01
       p.value
1 1.367824e-26
2 9.999991e-01
3 3.990881e-11

$other
  component  variance   percent
1  Residual 0.6314986  63.56316
2     Total 0.9934978 100.00000

$calculation
  component     variance      percent
1  additive 1.266191e-12 1.274478e-10
2    nonadd 6.878026e-01 6.923041e+01
3  maternal 1.900485e-01 1.912924e+01