traceline {irtQ}

Compute Item/Test Characteristic Functions

Description

This function computes the item category probabilities, item characteristic function, and test characteristic function given a set of theta values. The returned object of this function can be used to draw the item or test characteristic curve using the function plot.traceline.

Usage

traceline(x, ...)

## Default S3 method:
traceline(x, theta, D = 1, ...)

## S3 method for class 'est_item'
traceline(x, theta, ...)

## S3 method for class 'est_irt'
traceline(x, theta, ...)

Arguments

x

A data frame containing the item metadata (e.g., item parameters, number of categories, models ...), an object of class est_item obtained from the function est_item, or an object of class est_irt obtained from the function est_irt. See irtfit, info, or simdat for more details about the item metadata. The data frame of item metadata can be easily obtained using the function shape_df.

...

Further arguments passed to or from other methods.

theta

A vector of theta values.

D

A scaling factor in IRT models to make the logistic function as close as possible to the normal ogive function (if set to 1.7). Default is 1.

Value

This function returns an object of class traceline. This object contains a list containing the item category probabilities, item characteristic function, and test characteristic function.

Methods (by class)

• default: Default method to compute the item category probabilities, item characteristic function, and test characteristic function for a data frame x containing the item metadata.

• est_item: An object created by the function est_item.

• est_irt: An object created by the function est_irt.

Author(s)

Hwanggyu Lim [e-mail]

See Also

plot.traceline, est_item

Examples

## example
## using a "-prm.txt" file obtained from a flexMIRT
# import the "-prm.txt" output file from flexMIRT
flex_prm <- system.file("extdata", "flexmirt_sample-prm.txt", package = "irtQ")

# read item parameters and transform them to item metadata
test_flex <- bring.flexmirt(file=flex_prm, "par")$Group1$full_df

# set theta values
theta <- seq(-3, 3, 0.5)

# compute the item category probabilities and item/test
# characteristic functions given the theta values
traceline(x=test_flex, theta, D=1)