exametrika (version 1.1.0)

IRT: Estimating Item parameters using EM algorithm

Description

A function for estimating item parameters using the EM algorithm.

Usage

IRT(U, model = 2, na = NULL, Z = NULL, w = NULL, verbose = TRUE)

Value

model

number of item parameters you set.

testlength

Length of the test. The number of items included in the test.

nobs

Sample size. The number of rows in the dataset.

params

Matrix containing the estimated item parameters

Q3mat

Q3-matrix developed by Yen(1984)

itemPSD

Posterior standard deviation of the item parameters

ability

Estimated parameters of students ability

ItemFitIndices

Fit index for each item.See also ItemFit

TestFitIndices

Overall fit index for the test.See also TestFit

Arguments

U

U is either a data class of exametrika, or raw data. When raw data is given, it is converted to the exametrika class with the dataFormat function.

model

This argument takes the number of item parameters to be estimated in the logistic model. It is limited to values 2, 3, or 4.

na

na argument specifies the numbers or characters to be treated as missing values.

Z

Z is a missing indicator matrix of the type matrix or data.frame

w

w is item weight vector

verbose

logical; if TRUE, shows progress of iterations (default: TRUE)

Details

Apply the 2, 3, and 4 parameter logistic models to estimate the item and subject populations. The 4PL model can be described as follows. $$P(\theta,a_j,b_j,c_j,d_j)= c_j + \frac{d_j -c_j}{1+exp\{-a_j(\theta - b_j)\}}$$ \(a_j, b_j, c_j\), and \(d_j\) are parameters related to item j, and are parameters that adjust the logistic curve. \(a_j\) is called the slope parameter, \(b_j\) is the location, \(c_j\) is the lower asymptote, and \(d_j\) is the upper asymptote parameter. The model includes lower models, and among the 4PL models, the case where \(d=1\) is the 3PL model, and among the 3PL models, the case where \(c=0\) is the 2PL model.

References

Yen, W. M. (1984) Applied Psychological Measurement, 8, 125-145.

Examples

# \donttest{
# Fit a 3-parameter IRT model to the sample dataset
result.IRT <- IRT(J15S500, model = 3)

# Display the first few rows of estimated student abilities
head(result.IRT$ability)

# Plot Item Characteristic Curves (ICC) for items 1-6 in a 2x3 grid
plot(result.IRT, type = "ICC", items = 1:6, nc = 2, nr = 3)

# Plot Item Information Curves (IIC) for items 1-6 in a 2x3 grid
plot(result.IRT, type = "IIC", items = 1:6, nc = 2, nr = 3)

# Plot the Test Information Curve (TIC) for all items
plot(result.IRT, type = "TIC")
# }