predict.pcmodel {psychotools} | R Documentation |
Prediction of (cumulated) response probabilities and responses based on fitted item response models.
## S3 method for class 'pcmodel' predict(object, newdata = NULL, type = c("probability", "cumprobability", "mode", "median", "mean", "category-information", "item-information", "test-information"), ref = NULL, ...)
object |
a fitted model object whose item parameters should be used for prediction. |
newdata |
an optional (possibly named) vector of person parameters
used for prediction. If |
type |
character of length one which determines the type of prediction (see details below). |
ref |
arguments passed over to internal calls of |
... |
further arguments which are currently not used. |
Depending on the value of type
either probabilities, responses
or some form of information under the model specified in object
are returned:
If type
is "probability"
, the category response
probabilities are returned.
If type
is "cumprobability"
, the cumulated category
response probabilities are returned, i.e., P(X_{ij} ≥q k) with
k corresponding to the categories of item j.
If type
is "mode"
, the most probable category response
for a given subject and item is returned.
If type
is "median"
, the first category k where
P(X_{ij} = k) ≥q 0.5 is returned.
If type
is "mean"
, the rounded expected category
response, i.e., E(X_{ij}|θ_{i}), is returned.
If type
is "category-information"
, the item-category
information as suggested by Bock (1972) is returned.
If type
is "item-information"
, the item information
as suggested by Samejima (1974) is returned.
If type
is "test-information"
, the sum over the
individual item informations is returned.
A (possibly named) numeric matrix with rows corresponding to
subjects and columns corresponding to the whole test, the single
items or categories. The exact content depends on the value of
type
(see details above).
Bock RD (1972). Estimating Item Parameters and Latent Ability When Responses Are Scored in Two or More Nominal Categories. Psychometrika, 37, 29–51.
Samejima F (1974). Normal Ogive Model on the Continuous Response Level in the Multidimensional Latent Space. Psychometrika, 39, 111–121.
The help page of the generic function predict
and
other predict methods (e.g., predict.lm
,
predict.glm
, ...)
o <- options(digits = 4) ## load verbal aggression data data("VerbalAggression", package = "psychotools") ## fit a partial credit model to first ten items pcmod <- pcmodel(VerbalAggression$resp[, 1:10]) ## predicted response probabilities for each ## subject and category (the default) head(predict(pcmod)) ## predicted mode (most probable category) ## for certain subjects whose person parameters ## are given via argument 'newdata' predict(pcmod, type = "mode", newdata = c("Sarah" = 1.2, "Michael" = 0.1, "Arnd" = -0.8)) ## rounded expected category value for the same subjects predict(pcmod, type = "mean", newdata = c("Sarah" = 1.2, "Michael" = 0.1, "Arnd" = -0.8)) ## in the rasch model mode, mean and median are the same raschmod <- raschmodel(VerbalAggression$resp2[, 1:10]) med <- predict(raschmod, type = "median") mn <- predict(raschmod, type = "mean") mod <- predict(raschmod, type = "mode") head(med) head(mn) head(mod) all.equal(med, mn) all.equal(mod, mn) options(digits = o$digits)