discrpar | R Documentation |
A class and generic function for representing and extracting the discrimination parameters of a given item response model.
discrpar(object, ...)
## S3 method for class 'raschmodel'
discrpar(object, ref = NULL, alias = TRUE, vcov = TRUE, ...)
## S3 method for class 'rsmodel'
discrpar(object, ref = NULL, alias = TRUE, vcov = TRUE, ...)
## S3 method for class 'pcmodel'
discrpar(object, ref = NULL, alias = TRUE, vcov = TRUE, ...)
## S3 method for class 'nplmodel'
discrpar(object, ref = NULL, alias = TRUE, vcov = TRUE, ...)
## S3 method for class 'gpcmodel'
discrpar(object, ref = NULL, alias = TRUE, vcov = TRUE, ...)
object |
a fitted model object whose discrimination parameters should be extracted. |
ref |
a restriction to be used. Not used for models estimated via CML as
the discrimination parameters are fixed to 1 in |
alias |
logical. If |
vcov |
logical. If |
... |
further arguments which are currently not used. |
discrpar
is both, a class to represent discrimination parameters of
item response models as well as a generic function. The generic function can
be used to extract the discrimination parameters of a given item response
model.
For objects of class discrpar
, several methods to standard generic
functions exist: print
, coef
, vcov
. coef
and
vcov
can be used to extract the discrimination parameters and their
variance-covariance matrix without additional attributes.
A named vector with discrimination parameters of class discrpar
and
additional attributes model
(the model name), ref
(the items or
parameters used as restriction/for normalization), alias
(either
TRUE
or a named numeric vector with the aliased parameters not included
in the return value), and vcov
(the estimated and adjusted
variance-covariance matrix).
personpar
, itempar
,
threshpar
, guesspar
, upperpar
o <- options(digits = 4)
## load verbal aggression data
data("VerbalAggression", package = "psychotools")
## fit Rasch model to verbal aggression data
rmod <- raschmodel(VerbalAggression$resp2)
## extract the discrimination parameters
dp1 <- discrpar(rmod)
## extract the standard errors
sqrt(diag(vcov(dp1)))
if(requireNamespace("mirt")) {
## fit 2PL to verbal aggression data
twoplmod <- nplmodel(VerbalAggression$resp2)
## extract the discrimination parameters
dp2 <- discrpar(twoplmod)
## this time with the first discrimination parameter being the reference
discrpar(twoplmod, ref = 1)
## extract the standard errors
sqrt(diag(vcov(dp2)))
}
options(digits = o$digits)
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