Extract Variance-Covariance Matrix from a glmMixture Object
Source:R/mixture_glm_methods.R
vcov.glmMixture.RdReturns the variance-covariance matrix of the main parameters of a fitted
glmMixture object. The matrix is estimated using a sandwich estimator
to account for the mixture structure.
Usage
# S3 method for class 'glmMixture'
vcov(object, ...)Value
A matrix of the estimated covariances between the parameter estimates. Row and column names correspond to the parameter names (coefficients, dispersion, etc.).
Examples
# Load the LIFE-M demo dataset
data(lifem)
# Phase 1: Adjustment Specification
# We model the correct match indicator via logistic regression using
# name commonness scores (commf, comml) and a 5% expected mismatch rate.
adj_object <- adjMixture(
linked.data = lifem,
m.formula = ~ commf + comml,
m.rate = 0.05,
safe.matches = hndlnk
)
# Phase 2: Estimation & Inference
# Fit a Gaussian regression model utilizing a cubic polynomial for year of birth.
fit <- plglm(
age_at_death ~ poly(unit_yob, 3, raw = TRUE),
family = "gaussian",
adjustment = adj_object
)
vcov(fit)
#> coef (Intercept)
#> coef (Intercept) 2.46975902
#> coef poly(unit_yob, 3, raw = TRUE)1 -23.52175723
#> coef poly(unit_yob, 3, raw = TRUE)2 50.32131256
#> coef poly(unit_yob, 3, raw = TRUE)3 -28.84771322
#> dispersion 13.50918917
#> m.coef (Intercept) 0.34449875
#> m.coef commf -0.09520238
#> m.coef comml -0.41118201
#> coef poly(unit_yob, 3, raw = TRUE)1
#> coef (Intercept) -23.5217572
#> coef poly(unit_yob, 3, raw = TRUE)1 330.8987262
#> coef poly(unit_yob, 3, raw = TRUE)2 -799.9390974
#> coef poly(unit_yob, 3, raw = TRUE)3 492.3118825
#> dispersion -176.5971120
#> m.coef (Intercept) -4.1178131
#> m.coef commf -0.4020898
#> m.coef comml 5.6864113
#> coef poly(unit_yob, 3, raw = TRUE)2
#> coef (Intercept) 50.321313
#> coef poly(unit_yob, 3, raw = TRUE)1 -799.939097
#> coef poly(unit_yob, 3, raw = TRUE)2 2084.357322
#> coef poly(unit_yob, 3, raw = TRUE)3 -1356.437057
#> dispersion 319.984050
#> m.coef (Intercept) 7.291627
#> m.coef commf 4.228244
#> m.coef comml -13.515778
#> coef poly(unit_yob, 3, raw = TRUE)3
#> coef (Intercept) -28.8477132
#> coef poly(unit_yob, 3, raw = TRUE)1 492.3118825
#> coef poly(unit_yob, 3, raw = TRUE)2 -1356.4370569
#> coef poly(unit_yob, 3, raw = TRUE)3 931.4044404
#> dispersion 0.1667223
#> m.coef (Intercept) -4.5079215
#> m.coef commf -4.3081749
#> m.coef comml 11.3205245
#> dispersion m.coef (Intercept)
#> coef (Intercept) 13.5091892 0.3444988
#> coef poly(unit_yob, 3, raw = TRUE)1 -176.5971120 -4.1178131
#> coef poly(unit_yob, 3, raw = TRUE)2 319.9840498 7.2916269
#> coef poly(unit_yob, 3, raw = TRUE)3 0.1667223 -4.5079215
#> dispersion 2018.5407733 -15.5338192
#> m.coef (Intercept) -15.5338192 6.1090919
#> m.coef commf 1.3092814 -4.1172117
#> m.coef comml 31.3783994 -5.2864219
#> m.coef commf m.coef comml
#> coef (Intercept) -0.09520238 -0.4111820
#> coef poly(unit_yob, 3, raw = TRUE)1 -0.40208978 5.6864113
#> coef poly(unit_yob, 3, raw = TRUE)2 4.22824403 -13.5157782
#> coef poly(unit_yob, 3, raw = TRUE)3 -4.30817490 11.3205245
#> dispersion 1.30928145 31.3783994
#> m.coef (Intercept) -4.11721170 -5.2864219
#> m.coef commf 5.02343393 0.2860827
#> m.coef comml 0.28608274 10.0709274