Extract estimated variance components — extract

Extracts the estimated variance components from a fitted linear mixed effects model (lmeStruct object) or generalized least squares model (glsStruct object).

extract_varcomp(mod, separate_variances, vector)

Arguments

mod: Fitted model of class lmeStruct or glsStruct.
separate_variances: Logical indicating whether to return the separate level-1 variance components for each stratum if using varIdent function to allow for different variances per stratum. Default is FALSE.
vector: Logical indicating whether to return the variance components as a numeric vector. Default is FALSE.

Value

If vector = FALSE, an object of class varcomp consisting of a list of estimated variance components. Models that do not include correlation structure parameters or variance structure parameters will have empty lists for those components. If vector = TRUE, a numeric vector of estimated variance components.

If separate_variances = TRUE and if weights = varIdent(form = ~ 1 | Stratum) is specified in the model fitting, separate level-1 variance estimates will be returned for each stratum. If

separate_variances = TRUE but if the weighting structure is not specified with varIdent, or if separate_variances = FALSE, then no separate level-1 variance estimates will be returned.

Examples


library(nlme)
data(Bryant2016)
Bryant2016_RML <- lme(fixed = outcome ~ treatment,
                      random = ~ 1 | school/case,
                      correlation = corAR1(0, ~ session | school/case),
                      weights = varIdent(form = ~ 1 | treatment),
                      data = Bryant2016)
extract_varcomp(Bryant2016_RML, separate_variances = FALSE)
#> $Tau
#> $Tau$school
#> school.var((Intercept)) 
#>                165.9984 
#> 
#> $Tau$case
#> case.var((Intercept)) 
#>          2.171361e-05 
#> 
#> 
#> $cor_params
#> [1] 0.9745914
#> 
#> $var_params
#> [1] 1.250291
#> 
#> $sigma_sq
#> [1] 663.8412
#> 
#> attr(,"class")
#> [1] "varcomp"
extract_varcomp(Bryant2016_RML, separate_variances = TRUE)
#> $Tau
#> $Tau$school
#> school.var((Intercept)) 
#>                165.9984 
#> 
#> $Tau$case
#> case.var((Intercept)) 
#>          2.171361e-05 
#> 
#> 
#> $cor_params
#> [1] 0.9745914
#> 
#> $sigma_sq
#>  baseline treatment 
#>  663.8412 1037.7353 
#> 
#> attr(,"class")
#> [1] "varcomp"
extract_varcomp(Bryant2016_RML, vector = TRUE)
#> Tau.school.school.var((Intercept))     Tau.case.case.var((Intercept)) 
#>                       1.659984e+02                       2.171361e-05 
#>                         cor_params                         var_params 
#>                       9.745914e-01                       1.250291e+00 
#>                           sigma_sq 
#>                       6.638412e+02