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rpf (version 0.43)

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

0.43

License

GPL (>= 3)

Maintainer

Joshua Pritikin

Last Published

November 13th, 2014

Functions in rpf (0.43)

Write a flexMIRT PRM file

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

itemOutcomeBySumScore

Produce an item outcome by observed sum-score table

Retrieve a description of the given parameter

Tabulate data.frame rows

Length of the item model vector

Liking for Science dataset

Rescale item parameters

The ogive constant

Class rpf.1dim.graded

The base class for 1 dimensional graded response probability functions.

orderCompletely

Order a data.frame by missingness and all columns

Randomly sample response patterns given a list of items

compressDataFrame

Compress a data frame into unique rows and frequencies

Item parameter derivatives

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

expandDataFrame

Expand summary table of patterns and frequencies

Create a similar item specification with the given number of factors

Find the point where an item provides mean maximum information

Class rpf.mdim.mcm

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

Omit the given items

ChenThissen1997

Computes local dependence indices for all pairs of items

Class rpf.mdim.graded

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

Compute the sum-score EAP table

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

ptw2011.gof.test

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

Length of the item parameter vector

Create a graded response model

Compute the ordinal gamma association statistic

Convert an rpf item model name to an ID

Compute the S fit statistic for 1 item

Create a dichotomous response model

Identify the columns with most missing data

omitMostMissing

Omit items with the most missing data

The base class for 1 dimensional response probability functions.

rpf.1dim.moment

Calculate cell central moments

Compute the S fit statistic for a set of items

rpf.1dim.residual

Calculate residuals

Multinomial fit test

Class rpf.1dim.grm

The unidimensional graded response item model.

observedSumScore

Compute the observed sum-score

Generates item parameters

Transform from [0,1] to the reals

Description of LSAT6 data

Class rpf.1dim.drm

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

Calculate item and person Rasch fit statistics

sumScoreEAPTest

Conduct the sum-score EAP distribution test

Create a nominal response model

Knox Cube Test dataset

Convert an OpenMx MxModel object into an IFA group

rpf.1dim.stdresidual

Calculate standardized residuals

The base class for multi-dimensional response probability functions.

Class rpf.mdim.grm

The multidimensional graded response item model.

Description of LSAT7 data

Class rpf.mdim.nrm

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

Read a flexMIRT PRM file

Compute EAP scores

The base class for response probability functions.

Item derivatives with respect to the location in the latent space

Find the point where an item provides mean maximum information

An introduction

rpf - Response Probability Functions

Monte-Carlo test for cross-tabulation tables

Create a multiple-choice response model

Class rpf.mdim.drm

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

Strip data and scores from an IFA group