A function that returns a named vector of evaluated criteria for each respective item in the test bank. The names are associated with the item number in the bank. Note that criteria values are returned such that the maximum value always represents the most optimal item (e.g., maximum information). In cases where the minimum value is typically selected (e.g., minimum variance) all values are multiplied by -1 to turn it into a maximization problem.
computeCriteria(
x,
criteria,
person = NULL,
test = NULL,
design = NULL,
subset = NULL,
info_mats = FALSE
)
a vector of criteria values for each respective item
an object of class 'mirtCAT_design' returned from the mirtCAT
function
when passing design_elements = TRUE
item selection criteria (see mirtCAT
's criteria
input)
(required when x
is missing) internal person object. To be
used when customNextItem
function has been defined
(required when x
is missing) internal test object. To be
used when customNextItem
function has been defined
(required when x
is missing) internal design object. To be
used when customNextItem
function has been defined
an integer vector indicating which items should be included in the optimal search;
the default NULL
includes all possible items. To allow only the first 10 items to be
selected from this can be modified to subset = 1:10
. This is useful when administering
a multi-unidimensional CAT session where unidimensional blocks should be clustered together
for smoother presentation. Useful when using the customNextItem
function in
mirtCAT
logical; if more than one trait is present in the test, should the respective information matricies be returned instead of the scalar summary statistics (e.g., D-rule). When TRUE will return a list of matricies associated with each respective item
Phil Chalmers rphilip.chalmers@gmail.com
Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. tools:::Rd_expr_doi("10.18637/jss.v048.i06")
Chalmers, R. P. (2016). Generating Adaptive and Non-Adaptive Test Interfaces for Multidimensional Item Response Theory Applications. Journal of Statistical Software, 71(5), 1-39. tools:::Rd_expr_doi("10.18637/jss.v071.i05")
mirtCAT
, updateDesign
, extract.mirtCAT
,
findNextItem
if (FALSE) {
# test defined in mirtCAT help file, first example
CATdesign <- mirtCAT(df, mod, design_elements = TRUE)
computeCriteria(CATdesign, criteria = 'MI')
computeCriteria(CATdesign, criteria = 'MEI')
}
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