Confidence Intervals for glmMixture Objects

Computes Wald confidence intervals for one or more parameters in a glmMixture object.

Usage

# S3 method for class 'glmMixture'
confint(object, parm, level = 0.95, ...)

Arguments

object

An object of class glmMixture.

parm

A specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.

level

The confidence level required.

...

Additional arguments (currently ignored).

Value

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter.

Details

The intervals are calculated based on the sandwich variance estimator: Estimate +/- z_crit * SE. For Gaussian and Gamma families, a t-distribution is used with residual degrees of freedom. For Binomial and Poisson families, a standard normal distribution is used.

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
)

confint(fit)
#>                                          2.5 %     97.5 %
#> coef (Intercept)                     56.271559  59.233949
#> coef poly(unit_yob, 3, raw = TRUE)1 -60.626160 -26.894341
#> coef poly(unit_yob, 3, raw = TRUE)2  69.869166 159.938791
#> coef poly(unit_yob, 3, raw = TRUE)3 -88.631137 -25.652062
#> dispersion                          351.611444 394.611974
#> m.coef (Intercept)                    1.385269  13.737983
#> m.coef commf                        -12.298308  -1.164229
#> m.coef comml                        -16.116897  -1.831290