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rpf (version 0.48)

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

0.48

License

GPL (>= 3)

Maintainer

Joshua Pritikin

Last Published

August 11th, 2015

Functions in rpf (0.48)

expandDataFrame

Expand summary table of patterns and frequencies

Compute EAP scores

fromFactorLoading

Convert factor loadings to response function slopes

The base class for multi-dimensional response probability functions.

omitMostMissing

Omit items with the most missing data

Length of the item parameter vector

Monte-Carlo test for cross-tabulation tables

Rescale item parameters

Compute the S fit statistic for 1 item

Class rpf.1dim.grm

The unidimensional graded response item model.

rpf.1dim.residual

Calculate residuals

rpf.1dim.stdresidual

Calculate standardized residuals

compressDataFrame

Compress a data frame into unique rows and frequencies

ptw2011.gof.test

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

Class rpf.mdim.mcm

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

The base class for response probability functions.

The base class for 1 dimensional response probability functions.

rpf.1dim.moment

Calculate cell central moments

sumScoreEAPTest

Conduct the sum-score EAP distribution test

Item parameter derivatives

Strip data and scores from an IFA group

observedSumScore

Compute the observed sum-score

Class rpf.1dim.drm

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

Item derivatives with respect to the location in the latent space

Multinomial fit test

fromFactorThreshold

Convert factor thresholds to response function intercepts

Length of the item model vector

Class rpf.mdim.drm

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

Tabulate data.frame rows

Retrieve a description of the given parameter

itemOutcomeBySumScore

Produce an item outcome by observed sum-score table

Class rpf.1dim.graded

The base class for 1 dimensional graded response probability functions.

Description of LSAT7 data

Create a multiple-choice response model

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

The ogive constant

Read a flexMIRT PRM file

Randomly sample response patterns given a list of items

ChenThissen1997

Computes local dependence indices for all pairs of items

Compute the ordinal gamma association statistic

Transform from [0,1] to the reals

Compute the sum-score EAP table

orderCompletely

Order a data.frame by missingness and all columns

Class rpf.mdim.nrm

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

Class rpf.mdim.graded

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

toFactorThreshold

Convert response function intercepts to factor thresholds

Convert an OpenMx MxModel object into an IFA group

Convert an rpf item model name to an ID

Compute the S fit statistic for a set of items

An introduction

rpf - Response Probability Functions

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

Omit the given items

Find the point where an item provides mean maximum information

Liking for Science dataset

Find the point where an item provides mean maximum information

Create a similar item specification with the given number of factors

Create a graded response model

Generates item parameters

Identify the columns with most missing data

toFactorLoading

Convert response function slopes to factor loadings

Calculate item and person Rasch fit statistics

Class rpf.mdim.grm

The multidimensional graded response item model.

Knox Cube Test dataset

Create a nominal response model

Description of LSAT6 data

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

Write a flexMIRT PRM file

Create a dichotomous response model