guesspar | R Documentation |
A class and generic function for representing and extracting the so-called guessing parameters of a given item response model.
guesspar(object, ...)
## S3 method for class 'raschmodel'
guesspar(object, alias = TRUE, vcov = TRUE, ...)
## S3 method for class 'rsmodel'
guesspar(object, alias = TRUE, vcov = TRUE, ...)
## S3 method for class 'pcmodel'
guesspar(object, alias = TRUE, vcov = TRUE, ...)
## S3 method for class 'nplmodel'
guesspar(object, alias = TRUE, logit = FALSE, vcov = TRUE, ...)
## S3 method for class 'gpcmodel'
guesspar(object, alias = TRUE, vcov = TRUE, ...)
object |
a fitted model object whose guessing parameters should be extracted. |
alias |
logical. If |
logit |
logical. If a |
vcov |
logical. If |
... |
further arguments which are currently not used. |
guesspar
is both, a class to represent guessing parameters of item
response models as well as a generic function. The generic function can be
used to extract the guessing parameters of a given item response model.
For objects of class guesspar
, several methods to standard generic
functions exist: print
, coef
, vcov
. coef
and
vcov
can be used to extract the guessing parameters and their
variance-covariance matrix without additional attributes.
A named vector with guessing parameters of class guesspar
and
additional attributes model
(the model name), alias
(either
TRUE
or a named numeric vector with the aliased parameters not included
in the return value), logit
(indicating whether the estimates are on the
logit scale or not), and vcov
(the estimated and adjusted
variance-covariance matrix).
personpar
, itempar
,
threshpar
, discrpar
, upperpar
if(requireNamespace("mirt")) {
o <- options(digits = 3)
## load simulated data
data("Sim3PL", package = "psychotools")
## fit 2PL to data simulated under the 3PL
twoplmod <- nplmodel(Sim3PL$resp)
## extract the guessing parameters (all fixed at 0)
gp1 <- guesspar(twoplmod)
## fit 3PL to data simulated under the 3PL
threeplmod <- nplmodel(Sim3PL$resp, type = "3PL")
## extract the guessing parameters
gp2 <- guesspar(threeplmod)
## extract the standard errors
sqrt(diag(vcov(gp2)))
## extract the guessing parameters on the logit scale
gp2_logit <- guesspar(threeplmod, logit = TRUE)
## along with the delta transformed standard errors
sqrt(diag(vcov(gp2_logit)))
options(digits = o$digits)
}
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