Summary Method for Adjusted Contingency Tables
Source:R/mixture_ctable_methods.R
summary.ctablemixture.RdProvides a detailed summary of the ctableMixture model fit, including
the estimated cell probabilities with standard errors, convergence status,
and a Chi-squared test of independence performed on the adjusted counts.
Usage
# S3 method for class 'ctableMixture'
summary(object, ...)Value
An object of class summary.ctableMixture containing:
- call
The function call.
- m.rate
The assumed mismatch rate.
- ftable
The estimated contingency table of correctly matched counts.
- coefficients
A matrix containing estimates, standard errors, z-values, and p-values for cell probabilities.
- chisq
The result of a Pearson's Chi-squared test on the adjusted table.
- converged
Logical indicating if the EM algorithm converged.
- iterations
Number of iterations performed.
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. Generate the detailed summary object
sum_fit <- summary(fit)
# 5. Access specific components of the summary
print(sum_fit$coefficients)
#> Estimate Std. Error z value Pr(>|z|)
#> (high, no) 0.1506535 0.02531371 5.95146 2.657602e-09
#> (high, yes) 0.3194929 0.03122428 10.23219 0.000000e+00
#> (low, no) 0.3956622 0.03296915 12.00098 0.000000e+00
#> (low, yes) 0.1341914 0.02425366 5.53283 3.151040e-08
print(sum_fit$chisq)
#>
#> Pearson's Chi-squared test with Yates' continuity correction
#>
#> data: object$ftable
#> X-squared = 53.089, df = 1, p-value = 3.188e-13
#>