compute_Modified_probabilities: Compute Modified Item Response Probabilities

In GLMBasedRaschEstimation: GLM-Based Estimation for Rasch Model Parameters

Compute Modified Item Response Probabilities

Description

Compute Modified Item Response Probabilities

Usage

compute_Modified_probabilities(results, theta_all)

Arguments

results	The data frame returned by fit_binary_irt.
theta_all	A numeric vector representing ability levels (total scores).

Value

A numeric matrix of predicted probabilities with dimensions length(theta_all) rows by nrow(results) columns. Each cell contains the probability of a correct response for a given ability level and item, calculated using the logistic function: exp(\theta - \beta) / (1 + exp(\theta - \beta)), where \beta is the item threshold. Columns are named after the items.

Examples

set.seed(123)
sample_data <- matrix(sample(c(0, 1), 100, replace = TRUE), ncol = 10)
prepared <- prepare_data(sample_data)
irt_results <- fit_binary_irt(prepared$matrix, prepared$total_score)

abilities <- seq(0, 10, length.out = 5)

probs <- compute_Modified_probabilities(irt_results, abilities)

print(head(probs))

GLMBasedRaschEstimation documentation built on April 22, 2026, 9:08 a.m.