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Analysis of Contingency Tables with Linkage Error Adjustment

Source: R/02_plctable.R
plctable.Rd

plctable constructs a contingency table and adjusts the fitted model for mismatch errors.

Usage

plctable(
  formula,
  adjustment,
  subset,
  na.action,
  exclude = c(NA, NaN),
  control = list(),
  ...
)

Arguments

formula

a formula object with the left and right hand sides specifying the column and row variable of the flat table, respectively.

adjustment

An object inheriting from class "adjustment", or a list containing the necessary parameter specifications.

subset

An optional vector specifying a subset of observations to be used.

na.action

A function which indicates what should happen when the data contain NAs.

exclude

Vector of values to be excluded when forming the table (passed to xtabs).

control

A list of parameters for controlling the linkage error adjustment process.

...

Additional arguments passed to the internal function.

Value

An object representing the fitted model. The specific class and structure of the returned object depend directly on the adjustment method provided:

  • If adjustment is of class adjMixture, returns an object of class ctableMixture.

See also

adjMixture, ctableMixture

Examples

set.seed(102)
n <- 400

# Simulate true categorical data
exposure <- sample(c("low", "high"), n, replace = TRUE)
# True relationship: high exposure -> higher chance of disease
prob_disease <- ifelse(exposure == "high", 0.7, 0.3)
true_outcome <- ifelse(runif(n) < prob_disease, "disease", "healthy")

# Induce linkage (mismatch) errors at a fixed overall rate
true_mismatch_rate <- 0.20
is_mismatch <- rbinom(n, 1, true_mismatch_rate)

obs_outcome <- true_outcome
mismatch_idx <- which(is_mismatch == 1)
# Shuffle outcomes for the mismatched records
obs_outcome[mismatch_idx] <- sample(obs_outcome[mismatch_idx])

linked_df <- data.frame(exposure, outcome = obs_outcome)

# Specify the Adjustment Method
adj <- adjMixture(
  linked.data = linked_df,
  m.rate = true_mismatch_rate
)

# Fit the adjusted contingency table model
fit <- plctable(
  ~ exposure + outcome,
  adjustment = adj
)