tam.jml.RdThis function estimate unidimensional item response models with joint maximum likelihood (JML, see e.g. Linacre, 1994).
tam.jml(resp, group=NULL, adj=.3, disattenuate=FALSE, bias=TRUE,
xsi.fixed=NULL, xsi.inits=NULL, theta.fixed=NULL, A=NULL, B=NULL, Q=NULL,
ndim=1, pweights=NULL, constraint="cases", verbose=TRUE, control=list(), version=3,
theta_proc=NULL)
# S3 method for tam.jml
summary(object, file=NULL, ...)
# S3 method for tam.jml
logLik(object, ...)A matrix of item responses. Missing responses must be declared
as NA.
An optional vector of group identifier
An optional logical indicating whether the person parameters should be disattenuated due to unreliability? The disattenuation is conducted by applying the Kelley formula.
Adjustment constant which is subtracted or added to extreme scores (score of zero or maximum score). The default is a value of 0.3
A logical which indicates if JML bias should be reduced by multiplying item parameters by the adjustment factor of \((I-1)/I\)
An optional matrix with two columns for fixing some of the basis parameters \(\xi\) of item intercepts. 1st column: Index of \(\xi\) parameter, 2nd column: Fixed value of \(\xi\) parameter
An optional vector of initial \(\xi\) parameters. Note that
all parameters must be specified and the vector is not of the
same format as xsi.fixed.
Matrix for fixed person parameters \(\theta\). The first
column includes the index whereas the second column includes
the fixed value. This argument can only be applied for version=1.
A design array \(A\) for item category intercepts. For item \(i\) and category \(k\), the threshold is specified as \( \sum _j a_{ikj} \xi_j\).
A design array for scoring item category responses. Entries in \(B\) represent item loadings on abilities \(\theta\).
A Q-matrix which defines loadings of items on dimensions.
Number of dimensions in the model. The default is 1.
An optional vector of person weights.
Type of constraint for means. Either "cases"
(set mean of person parameters to zero)
or "items" (set mean of item parameters to zero).
Logical indicating whether output should
be printed during iterations. This argument replaces control$progress.
A list of control arguments. See tam.mml
for more details.
Version function which should be used. version=2
is the former tam.jml2 function in TAM (<2.0).
The default version=3 allows efficient handling in case of missing
data.
Function for processing theta within iterations. Can only
be applied for version=1.
Object of class tam.jml (only for summary.tam
function)
A file name in which the summary output will be written
(only for summary.tam.jml function)
Further arguments to be passed
A list with following entries
Data frame with item parameters
Vector of item parameters \(\xi\)
Standard error of item parameters \(\xi\)
MLE in final step
Standard error of WLE
WLE in last iteration
WLE reliability
Scores for each person (sufficient statistic)
Sufficient statistic for each item parameter
Maximum person score
Maximum item score
Deviance
Deviance history in iterations
Original data frame
Response indicator matrix
Vector of group identifiers (if provided as an argument)
Vector of person weights
Design matrix \(A\) of item intercepts
Loading (or scoring) matrix \(B\)
Number of items
Maximum number of categories
Number of persons in resp
Like resp.ind, only in the format of a list
Fixed \(\xi\) item parameters
Control list
Extended data frame of item parameters
Summary of person parameters
Linacre, J. M. (1994). Many-Facet Rasch Measurement. Chicago: MESA Press.
This joint maximum likelihood estimation procedure should be compatible with Winsteps and Facets software, see also http://www.rasch.org/software.htm.
For estimating the same class of models with marginal
maximum likelihood estimation see tam.mml.