Extract variance and correlation components

# S3 method for class 'glmmTMB'
VarCorr(x, sigma = 1, ...)

Arguments

x: a fitted glmmTMB model
sigma: residual standard deviation (usually set automatically from internal information)
...: extra arguments (for consistency with generic method)

Details

For an unstructured variance-covariance matrix, the internal parameters are structured as follows: the first n parameters are the log-standard-deviations, while the remaining n(n-1)/2 parameters are the elements of the Cholesky factor of the correlation matrix, filled in column-wise order (see the TMB documentation for further details).

Examples

## Comparing variance-covariance matrix with manual computation
data("sleepstudy",package="lme4")
fm4 <- glmmTMB(Reaction ~ Days + (Days|Subject), sleepstudy)
VarCorr(fm4)[[c("cond","Subject")]]
#>             (Intercept)     Days
#> (Intercept)   565.49168 11.05472
#> Days           11.05472 32.68062
#> attr(,"stddev")
#> (Intercept)        Days 
#>   23.780069    5.716696 
#> attr(,"correlation")
#>             (Intercept)       Days
#> (Intercept)  1.00000000 0.08131852
#> Days         0.08131852 1.00000000
#> attr(,"blockCode")
#> us 
#>  1 
#> attr(,"class")
#> [1] "vcmat_us" "matrix"   "array"   
## hand calculation
pars <- getME(fm4,"theta")
## construct cholesky factor
L <- diag(2)
L[lower.tri(L)] <- pars[-(1:2)]
C <- crossprod(L)
diag(C) <- 1
sdvec <- exp(pars[1:2])
(V <- outer(sdvec,sdvec) * C)
#>           [,1]     [,2]
#> [1,] 565.49168 11.09145
#> [2,]  11.09145 32.68062