| rpf - Response Probability Functions | rpf-package An introduction rpf |
| Convert an OpenMx MxModel object into an IFA group | as.IFAgroup |
| Identify the columns with most missing data | bestToOmit |
| Computes local dependence indices for all pairs of items | chen.thissen.1997 ChenThissen1997 |
| The base class for 1 dimensional response probability functions. | Class rpf.1dim rpf.1dim-class |
| Unidimensional dichotomous item models (1PL, 2PL, and 3PL). | Class rpf.1dim.drm rpf.1dim.drm-class |
| Unidimensional generalized partial credit monotonic polynomial. | Class rpf.1dim.gpcmp rpf.1dim.gpcmp-class |
| The base class for 1 dimensional graded response probability functions. | Class rpf.1dim.graded rpf.1dim.graded-class |
| The unidimensional graded response item model. | Class rpf.1dim.grm rpf.1dim.grm-class |
| Unidimensional graded response monotonic polynomial. | Class rpf.1dim.grmp rpf.1dim.grmp-class |
| Unidimensional logistic function of a monotonic polynomial. | Class rpf.1dim.lmp rpf.1dim.lmp-class |
| The base class for response probability functions. | $,rpf.base-method $<-,rpf.base-method Class rpf.base rpf.base-class |
| The base class for multi-dimensional response probability functions. | Class rpf.mdim rpf.mdim-class |
| Multidimensional dichotomous item models (M1PL, M2PL, and M3PL). | Class rpf.mdim.drm rpf.mdim.drm-class |
| The base class for multi-dimensional graded response probability functions. | Class rpf.mdim.graded rpf.mdim.graded-class |
| The multidimensional graded response item model. | Class rpf.mdim.grm rpf.mdim.grm-class |
| The multiple-choice response item model (both unidimensional and multidimensional models have the same parameterization). | Class rpf.mdim.mcm rpf.mdim.mcm-class |
| The nominal response item model (both unidimensional and multidimensional models have the same parameterization). | Class rpf.mdim.nrm rpf.mdim.nrm-class |
| Collapse small sample size categorical frequency counts | collapseCategoricalCells |
| Compress a data frame into unique rows and frequencies | compressDataFrame |
| Monte-Carlo test for cross-tabulation tables | crosstabTest |
| Compute Expected A Posteriori (EAP) scores | EAPscores |
| Expand summary table of patterns and frequencies | expandDataFrame |
| Convert factor loadings to response function slopes | fromFactorLoading |
| Convert factor thresholds to response function intercepts | fromFactorThreshold |
| Produce an item outcome by observed sum-score table | itemOutcomeBySumScore |
| Knox Cube Test dataset | kct kct.items kct.people |
| Transform from [0,1] to the reals | logit |
| Description of LSAT6 data | LSAT6 |
| Description of LSAT7 data | LSAT7 |
| Multinomial fit test | multinomialFit |
| Compute the observed sum-score | observedSumScore |
| Omit the given items | omitItems |
| Omit items with the most missing data | omitMostMissing |
| Order a data.frame by missingness and all columns | orderCompletely |
| Compute the ordinal gamma association statistic | ordinal.gamma |
| Compute the P value that the observed and expected tables come from the same distribution | ptw2011.gof.test |
| Read a flexMIRT PRM file | read.flexmirt |
| Calculate item and person Rasch fit statistics | rpf.1dim.fit |
| Calculate cell central moments | rpf.1dim.moment |
| Calculate residuals | rpf.1dim.residual |
| Calculate standardized residuals | rpf.1dim.stdresidual |
| Item parameter derivatives | rpf.dLL rpf.dLL,rpf.base,numeric,NULL,numeric-method rpf.dLL,rpf.base,numeric,numeric,numeric-method _rpf_dLL |
| Create a dichotomous response model | rpf.drm |
| Item derivatives with respect to the location in the latent space | rpf.dTheta rpf.dTheta,rpf.base,numeric,matrix,numeric-method rpf.dTheta,rpf.base,numeric,numeric,numeric-method _rpf_dTheta |
| Create monotonic polynomial generalized partial credit (GPC-MP) model | rpf.gpcmp |
| Create a graded response model | rpf.grm |
| Create monotonic polynomial graded response (GR-MP) model | rpf.grmp |
| Convert an rpf item model name to an ID | rpf.id_of |
| Map an item model, item parameters, and person trait score into a information vector | rpf.info |
| Create logistic function of a monotonic polynomial (LMP) model | rpf.lmp |
| Map an item model, item parameters, and person trait score into a probability vector | rpf.logprob rpf.logprob,rpf.1dim,numeric,matrix-method rpf.logprob,rpf.1dim,numeric,numeric-method rpf.logprob,rpf.mdim,numeric,matrix-method rpf.logprob,rpf.mdim,numeric,NULL-method rpf.logprob,rpf.mdim,numeric,numeric-method _rpf_logprob |
| Create a multiple-choice response model | rpf.mcm |
| Find the point where an item provides mean maximum information | rpf.mean.info |
| Find the point where an item provides mean maximum information | rpf.mean.info1 |
| Create a similar item specification with the given number of factors | rpf.modify rpf.modify,rpf.mdim.drm,numeric-method rpf.modify,rpf.mdim.graded,numeric-method rpf.modify,rpf.mdim.nrm,numeric-method |
| Create a nominal response model | rpf.nrm |
| Length of the item parameter vector | rpf.numParam rpf.numParam,rpf.base-method rpf_numParam_wrapper |
| Length of the item model vector | rpf.numSpec rpf.numSpec,rpf.base-method rpf_numSpec_wrapper |
| The ogive constant | rpf.ogive |
| Retrieve a description of the given parameter | rpf.paramInfo rpf.paramInfo,rpf.base-method rpf_paramInfo_wrapper |
| Map an item model, item parameters, and person trait score into a probability vector | rpf.prob rpf.prob,rpf.1dim,numeric,matrix-method rpf.prob,rpf.1dim,numeric,numeric-method rpf.prob,rpf.1dim.grm,numeric,numeric-method rpf.prob,rpf.base,data.frame,numeric-method rpf.prob,rpf.base,matrix,matrix-method rpf.prob,rpf.base,matrix,numeric-method rpf.prob,rpf.mdim,numeric,matrix-method rpf.prob,rpf.mdim,numeric,NULL-method rpf.prob,rpf.mdim,numeric,numeric-method rpf.prob,rpf.mdim.grm,numeric,matrix-method rpf.prob,rpf.mdim.grm,numeric,numeric-method rpf.prob,rpf.mdim.mcm,numeric,matrix-method rpf.prob,rpf.mdim.nrm,numeric,matrix-method _rpf_prob |
| Rescale item parameters | rpf.rescale rpf.rescale,rpf.base,numeric,numeric,matrix-method _rpf_rescale |
| Generates item parameters | rpf.rparam rpf.rparam,rpf.1dim.drm-method rpf.rparam,rpf.1dim.gpcmp-method rpf.rparam,rpf.1dim.graded-method rpf.rparam,rpf.1dim.grmp-method rpf.rparam,rpf.1dim.lmp-method rpf.rparam,rpf.mdim.drm-method rpf.rparam,rpf.mdim.graded-method rpf.rparam,rpf.mdim.mcm-method rpf.rparam,rpf.mdim.nrm-method |
| Randomly sample response patterns given a list of items | rpf.sample |
| Liking for Science dataset | science sfif sfpf sfsf sfxf |
| Compute the S fit statistic for a set of items | SitemFit |
| Compute the S fit statistic for 1 item | SitemFit1 |
| Strip data and scores from an IFA group | stripData |
| Compute the sum-score EAP table | sumScoreEAP |
| Conduct the sum-score EAP distribution test | sumScoreEAPTest |
| Tabulate data.frame rows | tabulateRows |
| Convert response function slopes to factor loadings | toFactorLoading |
| Convert response function intercepts to factor thresholds | toFactorThreshold |
| Write a flexMIRT PRM file | write.flexmirt |
