Confidence Intervals for Adjusted Cell Probabilities

Computes Wald-type confidence intervals for the estimated cell probabilities of the correctly matched population.

Usage

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

Arguments

object

An object of class ctableMixture.

parm

A specification of which parameters are to be given confidence intervals. If missing, all parameters (cells) are considered.

level

The confidence level required. Defaults to 0.95.

...

Additional arguments (currently ignored).

Value

A matrix with columns giving lower and upper confidence limits for each parameter.

Details

The intervals are calculated using the standard error estimates derived from vcov.ctableMixture. The lower and upper bounds are truncated at 0 and 1, respectively, to ensure valid probability estimates.

Examples

set.seed(125)
n <- 300

# 1. Simulate true categorical data with dependency
exposure <- sample(c("low", "high"), n, replace = TRUE)

# Induce dependency - High exposure -> higher disease probability
prob_disease <- ifelse(exposure == "high", 0.7, 0.3)
true_disease <- ifelse(runif(n) < prob_disease, "yes", "no")

# 2. Induce 15% linkage error
mis_idx <- sample(1:n, size = floor(0.15 * n))
obs_disease <- true_disease
obs_disease[mis_idx] <- sample(obs_disease[mis_idx])

linked_df <- data.frame(exposure = exposure, disease = obs_disease)

# 3. Fit the adjusted contingency table model
adj <- adjMixture(linked.data = linked_df, m.rate = 0.15)
fit <- plctable(~ exposure + disease, adjustment = adj)

# 4. Compute confidence intervals
# 95% CI for all cell probabilities
confint(fit)
#>                  2.5 %    97.5 %
#> (high, no)  0.10103959 0.2002675
#> (high, yes) 0.25829442 0.3806913
#> (low, no)   0.33104381 0.4602805
#> (low, yes)  0.08665511 0.1817277

# 90% CI for specific cells by name
confint(fit, parm = c("(low, yes)", "(high, no)"), level = 0.90)
#>                   5 %      95 %
#> (low, yes) 0.09429769 0.1740851
#> (high, no) 0.10901620 0.1922909