4.3-2

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: TAM
 Type: Package
 Title: Test Analysis Modules
-Version: 4.2-11
-Date: 2023-08-28 17:23:17.934842
+Version: 4.3-2
+Date: 2024-02-20 00:16:18
 Author:
  Alexander Robitzsch [aut,cre] (<https://orcid.org/0000-0002-8226-3132>),
  Thomas Kiefer [aut], 
@@ -37,4 +37,4 @@ License: GPL (>= 2)
 URL: 
  http://www.edmeasurementsurveys.com/TAM/Tutorials/, 
  https://github.com/alexanderrobitzsch/TAM,
- https://sites.google.com/site/alexanderrobitzsch2/software
+ https://sites.google.com/view/alexander-robitzsch/software

NAMESPACE

@@ -295,6 +295,7 @@ S3method(plot, tam.pv.mcmc)
 S3method(predict, tam.mml)
 S3method(predict, tam.mml.3pl)
 S3method(predict, tamaan)
+S3method(print, designMatrices)
 S3method(print, IRT.threshold)
 S3method(print, tam)
 S3method(print, tam_linking_2studies)

R/IRT.itemfit.R

@@ -1,14 +1,14 @@
 ## File Name: IRT.itemfit.R
-## File Version: 9.08
+## File Version: 9.091
 
 ###########################################################################
-IRT.itemfit.rmsea.default <- function( object )
+IRT_itemfit_rmsea_default <- function(object)
 {
  mod1 <- object
- probs <- IRT.irfprob( mod1 )
- n.ik <- IRT.expectedCounts( mod1 )
- pi.k <- attr( probs, "prob.theta")
- if ( is.vector( pi.k) ){
+ probs <- IRT.irfprob(mod1)
+ n.ik <- IRT.expectedCounts(mod1)
+ pi.k <- attr(probs, "prob.theta")
+ if ( is.vector(pi.k) ){
  pi.k <- matrix( pi.k, ncol=1 )
  }
  n.ik <- aperm( n.ik, c(3,1,2,4))
@@ -17,7 +17,7 @@ IRT.itemfit.rmsea.default <- function( object )
  return(res)
 }
 ###########################################################################
-IRT.itemfit.tam.default <- function( object, method="RMSD", ... )
+IRT.itemfit.tam.default <- function(object, method="RMSD", ... )
 {
  res <- NULL
  if ( method %in% c("RMSD","rmsea") ){

R/IRT.modelfit.tam.R → R/IRT_modelfit_TAM.R

@@ -1,9 +1,9 @@
-## File Name: IRT.modelfit.tam.R
-## File Version: 9.06
+## File Name: IRT_modelfit_TAM.R
+## File Version: 9.122
 
 ###########################################################
 # general model fit function for TAM objects
-IRT.modelfit.TAM <- function( object, mod )
+IRT_modelfit_TAM <- function( object, mod )
 {
  res <- tam.modelfit( object )
  res$IRT.IC <- IRT.IC(object)
@@ -18,7 +18,7 @@ IRT.modelfit.TAM <- function( object, mod )
 IRT.modelfit.tam.mml <- function( object, ... )
 {
  cl <- paste(match.call())[2]
- res <- IRT.modelfit.TAM( object, mod=cl )
+ res <- IRT_modelfit_TAM( object, mod=cl )
  return(res)
 }
 IRT.modelfit.tam.mml.3pl <- IRT.modelfit.tam.mml
@@ -34,9 +34,11 @@ summary.IRT.modelfit.TAM.helper <- function( object, ... )
  print(obji)
  cat("\nFit Statistics\n")
  obji <- object$statlist
- for (vv in seq(1,ncol(obji))){ obji[,vv] <- round( obji[,vv], 3 ) }
+ for (vv in seq(1,ncol(obji))){
+ obji[,vv] <- round( obji[,vv], 3 )
+ }
  print(obji)
- }
+}
 #################################################################
 
 summary.IRT.modelfit.tam.mml <- summary.IRT.modelfit.TAM.helper

R/RcppExports.R

@@ -1,5 +1,5 @@
 ## File Name: RcppExports.R
-## File Version: 4.002011
+## File Version: 4.003002
 # Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 

R/designMatrices_aux.R

@@ -1,10 +1,8 @@
 ## File Name: designMatrices_aux.R
-## File Version: 9.104
+## File Version: 9.105
 
 #############################################################
-print.designMatrices <-
- function( X, ... ){
- x <- X
+print.designMatrices <- function( x, ... ){
  BB <- x$flatB
  colnames(BB) <- paste("B_", colnames(BB), sep="")
  out <- cbind( x$flatA, BB )

R/tam.mml.wle2.R

@@ -1,11 +1,13 @@
 ## File Name: tam.mml.wle2.R
-## File Version: 0.853
+## File Version: 0.869
 
 ################################################################
 tam.mml.wle2 <- function( tamobj, score.resp=NULL, WLE=TRUE, adj=.3, Msteps=20,
- convM=.0001, progress=TRUE, output.prob=FALSE, pid=NULL )
+ convM=.0001, progress=TRUE, output.prob=FALSE, pid=NULL,
+ theta_init=NULL)
 {
  CALL <- match.call()
+ iweights <- NULL
  #--- process input data
  res <- tam_mml_wle_proc_input_data( tamobj=tamobj, score.resp=score.resp )
  AXsi <- res$AXsi
@@ -37,23 +39,33 @@ tam.mml.wle2 <- function( tamobj, score.resp=NULL, WLE=TRUE, adj=.3, Msteps=20,
  col.index <- rep( 1:nitems, each=maxK )
  cResp <- resp[, col.index ]*resp.ind[, col.index ]
  cResp <- 1 * t( t(cResp)==rep(0:(maxK-1), nitems) )
- cB <- t( matrix( aperm( B, c(2,1,3) ), nrow=dim(B)[3], byrow=TRUE ) )
+ if (!is.null(iweights)){
+ B1 <- B*iweights
+ } else {
+ B1 <- B
+ iweights <- rep(1, nitems)
+ }
+
+ cB <- t( matrix( aperm( B1, c(2,1,3) ), nrow=dim(B)[3], byrow=TRUE ) )
  cB[is.na(cB)] <- 0
  #Compute person sufficient statistics (total score on each dimension)
  PersonScores <- cResp %*% cB
 
  #Compute possible maximum score for each item on each dimension
- maxBi <- apply(B, 3, tam_rowMaxs, na.rm=TRUE)
+ maxBi <- apply(B1, 3, tam_rowMaxs, na.rm=TRUE)
+
 
  #Compute possible maximum score for each person on each dimension
  PersonMax <- resp.ind %*% maxBi
  PersonMax[ PersonMax==0 ] <- 2 * adj
 
  #Adjust perfect scores for each person on each dimension
- PersonScores[PersonScores==PersonMax] <- PersonScores[PersonScores==PersonMax] - adj
+ ind_max <- which(PersonScores==PersonMax)
+ PersonScores[ind_max] <- PersonScores[ind_max] - adj
 
  #Adjust zero scores for each person on each dimension
- PersonScores[PersonScores==0] <- PersonScores[PersonScores==0] + adj
+ ind0 <- which(PersonScores==0)
+ PersonScores[ind0] <- PersonScores[ind0] + adj
 
  #Calculate Axsi. Only need to do this once.
  # for (i in 1:nitems) {
@@ -63,7 +75,12 @@ tam.mml.wle2 <- function( tamobj, score.resp=NULL, WLE=TRUE, adj=.3, Msteps=20,
  # }
 
  #Initialise theta (WLE) values for all students
- theta <- log((PersonScores+.5)/(PersonMax-PersonScores+1)) #log of odds ratio of raw score
+ if (is.null(theta_init)){
+ theta <- log((PersonScores+.5)/(PersonMax-PersonScores+1))
+ #log of odds ratio of raw score
+ } else {
+ theta <- as.matrix( theta_init )
+ }
 
  ######################################
  #Compute WLE
@@ -92,12 +109,13 @@ tam.mml.wle2 <- function( tamobj, score.resp=NULL, WLE=TRUE, adj=.3, Msteps=20,
  xsi=xsi, theta=theta, nnodes=nstud, maxK=maxK, recalc=FALSE,
  use_rcpp=TRUE, maxcat=max(maxK), avoid_outer=TRUE )
  rprobsWLE <- resWLE$rprobs
+
  rprobsWLEL <- matrix(rprobsWLE, nrow=nitems*maxK, ncol=nstud )
  rprobsWLEL[is.na(rprobsWLEL)] <- 0
 
  resB <- tam_rcpp_wle_suffstat( RPROBS=rprobsWLEL, CBL=BL, CBB=BBL,
- CBBB=BBBL, cndim=ndim, cnitems=nitems, cmaxK=maxK, cnstud=nstud,
- resp_ind=resp.ind )
+ CBBB=BBBL, cndim=ndim, cnitems=nitems, cmaxK=maxK,
+ cnstud=nstud, resp_ind=resp.ind )
  B_bari <- array(resB$B_bari, dim=c(nstud, nitems,ndim))
  BB_bari <- array(resB$BB_bari, dim=c(nstud, nitems, ndim, ndim))
  BBB_bari <- array(resB$BBB_bari, dim=c(nstud, nitems, ndim))
@@ -145,7 +163,7 @@ tam.mml.wle2 <- function( tamobj, score.resp=NULL, WLE=TRUE, adj=.3, Msteps=20,
  # dampening the increment
  for ( d1 in 1:ndim){
  # increment[,d1] <- ifelse( abs(increment[,d1]) > 3, sign( increment[,d1] )*3, increment[,d1] )
- ci <- ceiling( abs(increment[,d1]) / ( abs( old_increment[,d1]) + 10^(-10) ) )
+ ci <- ceiling( abs(increment[,d1]) / ( abs( old_increment[,d1]) + 1e-10 ) )
  increment[,d1] <- ifelse( abs( increment[,d1]) > abs(old_increment[,d1]),
  increment[,d1]/(2*ci),
  increment[,d1] )
@@ -165,15 +183,16 @@ tam.mml.wle2 <- function( tamobj, score.resp=NULL, WLE=TRUE, adj=.3, Msteps=20,
  Miter <- Miter + 1
 
  if (progress){
-  cat( paste( "Iteration in WLE/MLE estimation ", Miter,
- " | Maximal change ", round( max(abs(increment)), 4), "\n" ) )
-  utils::flush.console()
-  }
+ cat( paste( "Iteration in WLE/MLE estimation ", Miter,
+ " | Maximal change ", round( max(abs(increment)), 4), "\n" ))
+ utils::flush.console()
+ }
  } # end of Newton-Raphson
 
  res <- tam_mml_wle_postproc( ndim=ndim, err_inv=err_inv, theta=theta, pid=pid,
  resp.ind=resp.ind, PersonScores=PersonScores, PersonMax=PersonMax,
- adj=adj, WLE=WLE, rprobsWLE=rprobsWLE, output.prob=output.prob, progress=progress,
- pweights=pweights, CALL=CALL, B=B, score.resp=score.resp )
+ adj=adj, WLE=WLE, rprobsWLE=rprobsWLE, output.prob=output.prob,
+ progress=progress, pweights=pweights, CALL=CALL, B=B,
+ score.resp=score.resp )
  return(res)
 }

R/tam_rbind_twomatrices.R

@@ -1,5 +1,5 @@
 ## File Name: tam_rbind_twomatrices.R
-## File Version: 9.06
+## File Version: 9.071
 
 #########################################
 # bind two matrices
@@ -13,4 +13,5 @@ tam_rbind_twomatrices <- function(X1, X2){
 }
 #########################################
 
-rbind.twomatrices <- tam_rbind_twomatrices
+# rbind.twomatrices <- tam_rbind_twomatrices
+rbind_twomatrices <- tam_rbind_twomatrices

R/tampv2datalist.R

@@ -1,20 +1,22 @@
 ## File Name: tampv2datalist.R
-## File Version: 9.13
+## File Version: 9.146
 
 
-##################################################################
+##*** converts a pv object into a list of datasets
 tampv2datalist <- function( tam.pv.object, pvnames=NULL, Y=NULL,
  Y.pid="pid", as_mids=FALSE, stringsAsFactors=FALSE )
 {
  pv <- tam.pv.object$pv
  ndim <- tam.pv.object$ndim
  nplausible <- tam.pv.object$nplausible
- Y00 <- data.frame( "pid"=tam.pv.object$pid, "pweights"=tam.pv.object$pweights,
+ Y00 <- data.frame( pid=tam.pv.object$pid, pweights=tam.pv.object$pweights,
  stringsAsFactors=stringsAsFactors )
+
  if ( ! is.null(Y) ){
  Y <- as.data.frame(Y)
  if( sum( colnames(Y) %in% Y.pid )==0 ){
- Y[, Y.pid] <- seq( 1, nrow(Y) )
+ Y[, Y.pid] <- pv$pid
+
  }
  }
  if ( is.null(pvnames) ){
@@ -39,4 +41,4 @@ tampv2datalist <- function( tam.pv.object, pvnames=NULL, Y=NULL,
 
  return(datalist)
 }
-##################################################################
+

README.md

@@ -7,7 +7,7 @@ a minimal runnable code necessary to reproduce the issue, which can be run on th
 all necessary information on the used librarys, the R version, and the OS it is run on, perhaps a ``sessionInfo()``.
 
 
-#### CRAN version `TAM` 4.1-4 (2022-08-28)
+#### CRAN version `TAM` 4.2-21 (2024-02-19)
 
 
 [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version-last-release/TAM)](https://cran.r-project.org/package=TAM)
@@ -21,9 +21,9 @@ The CRAN version can be installed from within R using:
 utils::install.packages("TAM")
 ```
 
-#### GitHub version `TAM` 4.2-11 (2023-08-28)
+#### GitHub version `TAM` 4.3-2 (2024-02-20)
 
-[![](https://img.shields.io/badge/github%20version-4.2--11-orange.svg)](https://github.com/alexanderrobitzsch/TAM)  
+[![](https://img.shields.io/badge/github%20version-4.3--2-orange.svg)](https://github.com/alexanderrobitzsch/TAM)  
 
 The version hosted [here](https://github.com/alexanderrobitzsch/TAM) is the development version of `TAM`. 
 The GitHub version can be installed using `devtools` as

inst/CITATION

@@ -26,7 +26,7 @@ citHeader( paste0( "To cite the '", pkg , "' package in publications use:") )
 bibentry(key = paste0(pkg, "_", meta$Version), 
  bibtype = "Manual",
  title = paste0( pkg , ": " , pkg_title ) ,
- author = personList( person_list1 ),
+ author = c( person_list1 ),
  year = year,
  note = vers,
  url = paste0( "https://CRAN.R-project.org/package=", pkg) ,

inst/NEWS

@@ -42,22 +42,37 @@ http://www.edmeasurementsurveys.com/TAM/Tutorials/
 
 
 -------------------------------------------------------------
-VERSIONS TAM 4.2 | 2023-08-28 | Last: TAM 4.2-11
+VERSIONS TAM 4.3 | 2024-02-20 | Last: TAM 4.3-2
+-------------------------------------------------------------
+
+xxx * ...
+
+
+DATA * included/modified datasets: ---
+EXAMP * included/modified examples: ---
+
+
+
+
+-------------------------------------------------------------
+VERSIONS TAM 4.2 | 2024-02-19 | Last: TAM 4.2-21
 -------------------------------------------------------------
 
 NOTE * included output values 'M_post' and 'SD_post' in 
  tam.latreg()
 FIXED * fixed a bug in tam.fa() due to changes in the GPArotation 
  package (thanks to @StegmannK, @thkiefer, @bernaard; 
- https://github.com/alexanderrobitzsch/TAM/issues/22)
-
-
+ https://github.com/alexanderrobitzsch/TAM/issues/22;
+ https://github.com/alexanderrobitzsch/TAM/issues/23)
+NOTE * included argument 'theta_init' in tam.mml.wle2() to
+ provide initial theta estimates (which is also available 
+ in tam.wle())
+NOTE * fixed a typo in ?tam.mml (thanks to Nan Wang)
+NOTE * minor changes in code due to CRAN requests 
 
 DATA * included/modified datasets: ---
 EXAMP * included/modified examples: ---
 
-
-
 -------------------------------------------------------------
 VERSIONS TAM 4.1 | 2022-08-28 | Last: TAM 4.1-4
 -------------------------------------------------------------

man/designMatrices.Rd

@@ -1,5 +1,5 @@
 %% File Name: designMatrices.Rd
-%% File Version: 2.222
+%% File Version: 2.228
 
 \name{designMatrices}
 
@@ -23,7 +23,7 @@ Generate design matrices, and display them at console.
 designMatrices(modeltype=c("PCM", "RSM"), maxKi=NULL, resp=resp,
  ndim=1, A=NULL, B=NULL, Q=NULL, R=NULL, constraint="cases",...)
 
-print.designMatrices(X, ...)
+\method{print}{designMatrices}(x, \dots)
 
 designMatrices.mfr(resp, formulaA=~ item + item:step, facets=NULL,
  constraint=c("cases", "items"), ndim=1, Q=NULL, A=NULL, B=NULL,
@@ -73,6 +73,10 @@ rownames.design(X)
 }
  \item{R}{
  This argument is not used
+}
+ \item{x}{
+ Object generated by \code{designMatrices}. This argument is used in
+ \code{print.designMatrices} and \code{rownames.design}.
 }
  \item{X}{
  Object generated by \code{designMatrices}. This argument is used in
@@ -135,8 +139,8 @@ items with differing number of response options.
 
 \seealso{
 See \code{\link{data.sim.mfr}} for some examples for creating design matrices.
-%% ~~objects to See Also as \code{\link{help}}, ~~~
 }
+%% ~~objects to See Also as \code{\link{help}}, ~~~
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%