exam1: Examination data

exam1R Documentation

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.

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.

Source

Taken from William Revelle's Short Guide to R, http://www.unt.edu/rss/rasch_models.htm, http://www.personality-project.org/r/. Downloaded in October 2013.

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

## Not run:  # 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

## End(Not run)

## Not run:  # 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)

## End(Not run)

VGAMdata documentation built on March 18, 2022, 8:03 p.m.