varcomp_vcov.RdEstimate the sampling variance-covariance of variance component parameters from a fitted linear mixed effects model (lmeStruct object) or generalized least squares model (glsStruct object) using the inverse Fisher information.
varcomp_vcov(mod, type = "expected", separate_variances = FALSE)Fitted model of class lmeStruct or glsStruct.
Type of information matrix. One of "expected" (the
default), "observed", or "average".
Logical indicating whether to return the Fisher
information matrix for separate level-1 variance components if using
varIdent function to allow for different variances per stratum.
Default is FALSE.
Sampling variance-covariance matrix corresponding to variance
component parameters of mod.
library(nlme)
data(Bryant2016)
Bryant2016_RML <- lme(fixed = outcome ~ treatment,
random = ~ 1 | school/case,
correlation = corAR1(0, ~ session | school/case),
data = Bryant2016)
varcomp_vcov(Bryant2016_RML, type = "expected")
#> Tau.school.var((Intercept))
#> Tau.school.var((Intercept)) 8.133900e+04
#> Tau.case.var((Intercept)) -2.140289e+04
#> cor_params -1.004342e-02
#> sigma_sq -4.701744e+02
#> Tau.case.var((Intercept)) cor_params
#> Tau.school.var((Intercept)) -2.140289e+04 -0.0100434219
#> Tau.case.var((Intercept)) 3.996251e+05 -8.2280433012
#> cor_params -8.228043e+00 0.0002154736
#> sigma_sq -3.815479e+05 9.8729751627
#> sigma_sq
#> Tau.school.var((Intercept)) -4.701744e+02
#> Tau.case.var((Intercept)) -3.815479e+05
#> cor_params 9.872975e+00
#> sigma_sq 4.602107e+05
Bryant2016_RML2 <- lme(fixed = outcome ~ treatment,
random = ~ 1 | school/case,
correlation = corAR1(0, ~ session | school/case),
weights = varIdent(form = ~ 1 | treatment),
data = Bryant2016)
varcomp_vcov(Bryant2016_RML2, separate_variances = TRUE)
#> Tau.school.var((Intercept))
#> Tau.school.var((Intercept)) 7.650376e+04
#> Tau.case.var((Intercept)) -1.002118e+04
#> cor_params 4.502481e-02
#> sigma_sq.baseline 1.053265e+03
#> sigma_sq.treatment 1.828453e+03
#> Tau.case.var((Intercept)) cor_params
#> Tau.school.var((Intercept)) -10021.178653 0.0450248054
#> Tau.case.var((Intercept)) 75198.102744 -1.9990651534
#> cor_params -1.999065 0.0001064976
#> sigma_sq.baseline -61234.417984 2.9076380204
#> sigma_sq.treatment -76051.418675 4.0240875929
#> sigma_sq.baseline sigma_sq.treatment
#> Tau.school.var((Intercept)) 1053.264890 1828.453115
#> Tau.case.var((Intercept)) -61234.417984 -76051.418675
#> cor_params 2.907638 4.024088
#> sigma_sq.baseline 88587.446312 111841.918017
#> sigma_sq.treatment 111841.918017 160901.251254