# Response Probability Functions

## Documentation for package ‘rpf’ version 1.0.11

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A B C E F I K L M O P R S T W misc

## -- A --

An introduction | rpf - Response Probability Functions
as.IFAgroup | Convert an OpenMx MxModel object into an IFA group

## -- B --

bestToOmit | Identify the columns with most missing data

## -- C --

chen.thissen.1997 | Computes local dependence indices for all pairs of items
ChenThissen1997 | Computes local dependence indices for all pairs of items
Class rpf.1dim | The base class for 1 dimensional response probability functions.
Class rpf.1dim.drm | Unidimensional dichotomous item models (1PL, 2PL, and 3PL).
Class rpf.1dim.gpcmp | Unidimensional generalized partial credit monotonic polynomial.
Class rpf.1dim.graded | The base class for 1 dimensional graded response probability functions.
Class rpf.1dim.grm | The unidimensional graded response item model.
Class rpf.1dim.grmp | Unidimensional graded response monotonic polynomial.
Class rpf.1dim.lmp | Unidimensional logistic function of a monotonic polynomial.
Class rpf.base | The base class for response probability functions.
Class rpf.mdim | The base class for multi-dimensional response probability functions.
Class rpf.mdim.drm | Multidimensional dichotomous item models (M1PL, M2PL, and M3PL).
Class rpf.mdim.graded | The base class for multi-dimensional graded response probability functions.
Class rpf.mdim.grm | The multidimensional graded response item model.
Class rpf.mdim.mcm | The multiple-choice response item model (both unidimensional and multidimensional models have the same parameterization).
Class rpf.mdim.nrm | The nominal response item model (both unidimensional and multidimensional models have the same parameterization).
collapseCategoricalCells | Collapse small sample size categorical frequency counts
compressDataFrame | Compress a data frame into unique rows and frequencies
crosstabTest | Monte-Carlo test for cross-tabulation tables

## -- E --

EAPscores | Compute Expected A Posteriori (EAP) scores
expandDataFrame | Expand summary table of patterns and frequencies

## -- F --

fromFactorLoading | Convert factor loadings to response function slopes
fromFactorThreshold | Convert factor thresholds to response function intercepts

## -- I --

itemOutcomeBySumScore | Produce an item outcome by observed sum-score table

## -- K --

kct | Knox Cube Test dataset
kct.items | Knox Cube Test dataset
kct.people | Knox Cube Test dataset

## -- L --

logit | Transform from [0,1] to the reals
LSAT6 | Description of LSAT6 data
LSAT7 | Description of LSAT7 data

## -- M --

multinomialFit | Multinomial fit test

## -- O --

observedSumScore | Compute the observed sum-score
omitItems | Omit the given items
omitMostMissing | Omit items with the most missing data
orderCompletely | Order a data.frame by missingness and all columns
ordinal.gamma | Compute the ordinal gamma association statistic

## -- P --

ptw2011.gof.test | Compute the P value that the observed and expected tables come from the same distribution

## -- R --

read.flexmirt | Read a flexMIRT PRM file
rpf.1dim-class | The base class for 1 dimensional response probability functions.
rpf.1dim.drm-class | Unidimensional dichotomous item models (1PL, 2PL, and 3PL).
rpf.1dim.fit | Calculate item and person Rasch fit statistics
rpf.1dim.gpcmp-class | Unidimensional generalized partial credit monotonic polynomial.
rpf.1dim.graded-class | The base class for 1 dimensional graded response probability functions.
rpf.1dim.grm-class | The unidimensional graded response item model.
rpf.1dim.grmp-class | Unidimensional graded response monotonic polynomial.
rpf.1dim.lmp-class | Unidimensional logistic function of a monotonic polynomial.
rpf.1dim.moment | Calculate cell central moments
rpf.1dim.residual | Calculate residuals
rpf.1dim.stdresidual | Calculate standardized residuals
rpf.base-class | The base class for response probability functions.
rpf.dLL | Item parameter derivatives
rpf.dLL-method | Item parameter derivatives
rpf.drm | Create a dichotomous response model
rpf.dTheta | Item derivatives with respect to the location in the latent space
rpf.dTheta-method | Item derivatives with respect to the location in the latent space
rpf.gpcmp | Create monotonic polynomial generalized partial credit (GPC-MP) model
rpf.grm | Create a graded response model
rpf.grmp | Create monotonic polynomial graded response (GR-MP) model
rpf.id_of | Convert an rpf item model name to an ID
rpf.info | Map an item model, item parameters, and person trait score into a information vector
rpf.lmp | Create logistic function of a monotonic polynomial (LMP) model
rpf.logprob | Map an item model, item parameters, and person trait score into a probability vector
rpf.logprob-method | Map an item model, item parameters, and person trait score into a probability vector
rpf.mcm | Create a multiple-choice response model
rpf.mdim-class | The base class for multi-dimensional response probability functions.
rpf.mdim.drm-class | Multidimensional dichotomous item models (M1PL, M2PL, and M3PL).
rpf.mdim.graded-class | The base class for multi-dimensional graded response probability functions.
rpf.mdim.grm-class | The multidimensional graded response item model.
rpf.mdim.mcm-class | The multiple-choice response item model (both unidimensional and multidimensional models have the same parameterization).
rpf.mdim.nrm-class | The nominal response item model (both unidimensional and multidimensional models have the same parameterization).
rpf.mean.info | Find the point where an item provides mean maximum information
rpf.mean.info1 | Find the point where an item provides mean maximum information
rpf.modify | Create a similar item specification with the given number of factors
rpf.modify-method | Create a similar item specification with the given number of factors
rpf.nrm | Create a nominal response model
rpf.numParam | Length of the item parameter vector
rpf.numParam-method | Length of the item parameter vector
rpf.numSpec | Length of the item model vector
rpf.numSpec-method | Length of the item model vector
rpf.ogive | The ogive constant
rpf.paramInfo | Retrieve a description of the given parameter
rpf.paramInfo-method | Retrieve a description of the given parameter
rpf.prob | Map an item model, item parameters, and person trait score into a probability vector
rpf.prob-method | Map an item model, item parameters, and person trait score into a probability vector
rpf.rescale | Rescale item parameters
rpf.rescale-method | Rescale item parameters
rpf.rparam | Generates item parameters
rpf.rparam-method | Generates item parameters
rpf.sample | Randomly sample response patterns given a list of items
rpf_numParam_wrapper | Length of the item parameter vector
rpf_numSpec_wrapper | Length of the item model vector
rpf_paramInfo_wrapper | Retrieve a description of the given parameter

## -- S --

science | Liking for Science dataset
sfif | Liking for Science dataset
sfpf | Liking for Science dataset
sfsf | Liking for Science dataset
sfxf | Liking for Science dataset
SitemFit | Compute the S fit statistic for a set of items
SitemFit1 | Compute the S fit statistic for 1 item
stripData | Strip data and scores from an IFA group
sumScoreEAP | Compute the sum-score EAP table
sumScoreEAPTest | Conduct the sum-score EAP distribution test

## -- T --

tabulateRows | Tabulate data.frame rows
toFactorLoading | Convert response function slopes to factor loadings
toFactorThreshold | Convert response function intercepts to factor thresholds

## -- W --

write.flexmirt | Write a flexMIRT PRM file

## -- misc --

$-method | The base class for response probability functions.
$<--method | The base class for response probability functions.
_rpf_dLL | Item parameter derivatives
_rpf_dTheta | Item derivatives with respect to the location in the latent space
_rpf_logprob | Map an item model, item parameters, and person trait score into a probability vector
_rpf_prob | Map an item model, item parameters, and person trait score into a probability vector
_rpf_rescale | Rescale item parameters