VGAMdata (version 1.1-12)

exam1: Examination data

Description

Exam results of 35 students on 18 questions.

Usage

data(exam1)

Format

A data frame with 35 observations on the following 18 variables.

% \item{name}{Name of the student }

q01, q02, q03, q04, q05, q06

binary response

q07, q08, q09, q10, q11, q12

binary response

q13, q14, q15, q16, q17, q18

binary response

Details

For each question, a 1 means correct, a 0 means incorrect. A simple Rasch model may be fitted to this dataframe using rcim and binomialff.

Examples

summary(exam1)  # The names of the students are the row names

# Fit a simple Rasch model.
# First, remove all questions and people who were totally correct or wrong
exam1.1 <- exam1  [, colMeans(exam1  ) > 0]
exam1.1 <- exam1.1[, colMeans(exam1.1) < 1]
exam1.1 <- exam1.1[rowMeans(exam1.1) > 0, ]
exam1.1 <- exam1.1[rowMeans(exam1.1) < 1, ]
Y.matrix <- rdata <- exam1.1

if (FALSE)  # The following needs: library(VGAM)
rfit <- rcim(Y.matrix, family = binomialff(multiple.responses = TRUE),
             trace = TRUE)

coef(rfit)  # Row and column effects
constraints(rfit, matrix = TRUE)  # Constraint matrices side-by-side
dim(model.matrix(rfit, type = "vlm"))  # 'Big' VLM matrix


if (FALSE)  # This plot shows the (main) row and column effects
par(mfrow = c(1, 2), las = 1, mar = c(4.5, 4.4, 2, 0.9) + 0.1)
saved <- plot(rfit, rcol = "blue", ccol = "orange",
              cylab = "Item effects", rylab = "Person effects",
              rxlab = "", cxlab = "")

names(saved@post)  # Some useful output put here
cbind(saved@post$row.effects)
cbind(saved@post$raw.row.effects)
round(cbind(-saved@post$col.effects), dig = 3)
round(cbind(-saved@post$raw.col.effects), dig = 3)
round(matrix(-saved@post$raw.col.effects, ncol = 1,  # Rename for humans
             dimnames = list(colnames(Y.matrix), NULL)), dig = 3)