threshpar {psychotools} | R Documentation |
A class and generic function for representing and extracting the item threshold parameters of a given item response model.
threshpar(object, ...) ## S3 method for class 'raschmodel' threshpar(object, type = c("mode", "median", "mean"), ref = NULL, alias = TRUE, relative = FALSE, cumulative = FALSE, vcov = TRUE, ...) ## S3 method for class 'rsmodel' threshpar(object, type = c("mode", "median", "mean"), ref = NULL, alias = TRUE, relative = FALSE, cumulative = FALSE, vcov = TRUE, ...) ## S3 method for class 'pcmodel' threshpar(object, type = c("mode", "median", "mean"), ref = NULL, alias = TRUE, relative = FALSE, cumulative = FALSE, vcov = TRUE, ...)
object |
a fitted model object whose threshold parameters should be extracted. |
type |
character of length one which determines the type of threshold parameters to return (see details below). |
ref |
a vector of labels or position indices of (relative)
threshold parameters or a contrast matrix which should be used
as restriction/for normalization. For partial credit models,
argument |
alias |
logical. If |
relative |
logical. If set to |
cumulative |
logical. If set to |
vcov |
logical. If |
... |
further arguments which are currently not used. |
threshpar
is both, a class to represent threshold parameters
of item response models as well as a generic function. The generic
function can be used to extract the threshold parameters of a given
item response model.
For objects of class threshpar
, methods to standard generic
functions print
and coef
can be used to print and
extract the threshold parameters.
Depending on argument type
, different item threshold parameters
are returned. For type = "mode"
, the returned item threshold
parameters correspond to the location on the theta axis where the
probability of category k equals the probability of category
k-1. For Rasch and partial credit models, item threshold parameters
of this type correspond directly to the estimated absolute item
threshold parameters of these models. For type = "median"
, the
returned item threshold parameters correspond to the location on the
theta axis where the probability of choosing category k or higher,
i.e., P(X_{ij} >= k), equals 0.5. For type = "mean"
, the
returned absolute item threshold parameters correspond to the location
on the theta axis where the expected category response is in the
middle between two categories, i.e. 0.5, 1.5, .... An illustration
of these threshold parameters can be found on page 104 in Masters &
Wright (1995).
A named list with item threshold parameters of class threshpar
and
additional attributes model
(the model name), type
(the type of item threshold parameters returned, see details above),
ref
(the items or parameters used as restriction/for
normalization), relative
(whether relative or absolute
item threshold parameters are returned), cumulative
(whether
the cumulative item threshold parameters are returned),
alias
(either FALSE
or a named character vector or list
with the removed aliased parameters), and vcov
(the estimated
and adjusted variance-covariance matrix).
Masters GN, Wright BD (1997). The Partial Credit Model. In Van der Linden WJ, Hambleton RK (eds.). Handbook of Modern Item Response Theory.
itempar
, personpar
, and discrpar
o <- options(digits = 4) ## load verbal aggression data data("VerbalAggression", package = "psychotools") ## fit a rasch model to dichotomized verbal aggression data raschmod <- raschmodel(VerbalAggression$resp2) ## extract threshold parameters with sum zero restriction tr <- threshpar(raschmod) tr ## compare to item parameters (again with sum zero restriction) ip <- itempar(raschmod) ip all.equal(coef(tr), coef(ip)) ## rating scale model example rsmod <- rsmodel(VerbalAggression$resp) trmod <- threshpar(rsmod, type = "mode") trmed <- threshpar(rsmod, type = "median") trmn <- threshpar(rsmod, type = "mean") ## compare different types of threshold parameters cbind("Mode" = coef(trmod, type = "vector"), "Median" = coef(trmod, type = "vector"), "Mean" = coef(trmn, type = "vector")) options(digits = o$digits)