varcomp_vcov.Rd
Estimate 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