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rpf (version 0.40)

Response Probability Functions

Description

The purpose of this package is to factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions are made available with R_RegisterCCallable.

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Version

Install

install.packages('rpf')

Monthly Downloads

20,661

Version

0.40

License

GPL (>= 3)

Maintainer

Joshua Pritikin

Last Published

October 22nd, 2014

Functions in rpf (0.40)

Compute the S fit statistic for a set of items

omitMostMissing

Omit items with the most missing data

Create a multiple-choice response model

Create a nominal response model

compressDataFrame

Compress a data frame into unique rows and frequencies

Liking for Science dataset

observedSumScore

Compute the observed sum-score

Class rpf.mdim.graded

The base class for multi-dimensional graded response probability functions.

expandDataFrame

Expand summary table of patterns and frequencies

Item parameter derivatives

Omit the given items

sumScoreEAPTest

Conduct the sum-score EAP distribution test

The base class for 1 dimensional response probability functions.

Item derivatives with respect to the location in the latent space

Randomly sample response patterns given a list of items

itemOutcomeBySumScore

Produce an item outcome by observed sum-score table

Description of LSAT7 data

Retrieve a description of the given parameter

Length of the item parameter vector

Class rpf.1dim.drm

Unidimensional dichotomous item models (1PL, 2PL, and 3PL).

Create a graded response model

Rescale item parameters

Create a similar item specification with the given number of factors

ptw2011.gof.test

Compute the P value that the observed and expected tables come from the same distribution

The ogive constant

Strip data and scores from an IFA group

The base class for response probability functions.

Calculate item and person Rasch fit statistics

Class rpf.1dim.graded

The base class for 1 dimensional graded response probability functions.

Map an item model, item parameters, and person trait score into a information vector

Map an item model, item parameters, and person trait score into a probability vector

Write a flexMIRT PRM file

ChenThissen1997

Computes local dependence indices for all pairs of items

Description of LSAT6 data

Monte-Carlo test for cross-tabulation tables

Convert an OpenMx MxModel object into an IFA group

Class rpf.1dim.grm

The unidimensional graded response item model.

Identify the columns with most missing data

The base class for multi-dimensional response probability functions.

Find the point where an item provides mean maximum information

orderCompletely

Order a data.frame by missingness and all columns

Class rpf.mdim.grm

The multidimensional graded response item model.

Transform from [0,1] to the reals

Class rpf.mdim.drm

Multidimensional dichotomous item models (M1PL, M2PL, and M3PL).

rpf.1dim.residual

Calculate residuals

Find the point where an item provides mean maximum information

An introduction

rpf - Response Probability Functions

Knox Cube Test dataset

Multinomial fit test

Class rpf.mdim.nrm

The nominal response item model (both unidimensional and multidimensional models have the same parameterization).

Generates item parameters

Compute the ordinal gamma association statistic

Read a flexMIRT PRM file

Compute the S fit statistic for 1 item

Compute EAP scores

rpf.1dim.moment

Calculate cell central moments

Convert an rpf item model name to an ID

rpf.1dim.stdresidual

Calculate standardized residuals

Create a dichotomous response model

Length of the item model vector

Class rpf.mdim.mcm

The multiple-choice response item model (both unidimensional and multidimensional models have the same parameterization).

Tabulate data.frame rows

Map an item model, item parameters, and person trait score into a probability vector

Compute the sum-score EAP table