mcm-methods {plink} | R Documentation |
This function computes the probability of responding in a specific category for one or more items for a given set of theta values using the multiple-choice model.
mcm(x, cat, theta = seq(-4,4,0.05), ...) ## S4 method for signature 'matrix', 'numeric' mcm(x, cat, theta, ...) ## S4 method for signature 'data.frame', 'numeric' mcm(x, cat, theta, ...) ## S4 method for signature 'list', 'numeric' mcm(x, cat, theta, ...) ## S4 method for signature 'irt.pars', 'ANY' mcm(x, cat, theta, ...) ## S4 method for signature 'sep.pars', 'ANY' mcm(x, cat, theta, ...)
x |
an R object containing item parameters |
cat |
vector identifying the number of response categories plus one for each item (the additional category is for 'do not know') |
theta |
vector of theta values for which probabilities will be computed.
If theta is not specified, an equal interval range of values from -4 to 4 is used
with an increment of 0.05 |
... |
further arguments passed to or from other methods |
Returns an object of class irt.prob
NA
.NA
s
in the remaining cells of the (k+1)/3 subset of columns.NA
s.
For example, for one four category and one five category item, the first column includes
the discrimination parameters for the 'do not know' categories. Columns 2-5 contain the
discrimination parameters for the other categories. The fifth column for the four
response item should be NA
. The sixth column includes the category difficulties
for the 'do not know' category. Columns 7-10 contain the difficulty parameters for the
other categories. The tenth column for the four response item should be NA
. The
remaining four columns include the guessing probabilities, although the last column
for the four response item should be NA
.
NA
.NA
s in the remaining
cells.NA
s.
For example, for one four category and one five category item, the first four columns for
the four response item in the first list element would include the discrimination parameters.
The fifth column for this item would be NA
. The first four columns for the four
response item in the second list element would include the category difficulty parameters.
The fifth column for this item would be NA
. The first three columns for the four
response item in the third list element would include the guessing probabilities. The fourth
column for this item would be NA
.
"irt.pars"
. If x
contains
dichotomous items or items associated with another polytomous model, a warning will be
displayed stating that probabilities will be computed for the mcm items only. If x
contains parameters for multiple groups, a list of "irt.prob"
objects will be returned.sep.pars
. If x
contains
dichotomous items or items associated with another polytomous model, a warning will be
displayed stating that probabilities will be computed for the mcm items only.Jonathan P. Weeks weeksjp@gmail.com
Thissen, D., & Steinberg, L. (1984). A response model for multiple choice items. Psychometrika, 49(4), 501-519.
Thissen, D., & Steinberg, L. (1996) A response model for multiple choice items. In W.J. van der Linden & Hambleton, R. K. (Eds.) Handbook of Modern Item Response Theory. New York: Springer-Verlag
mixed:
compute probabilities for mixed-format items
plot:
plot item characteristic/category curves
irt.prob
, irt.pars
, sep.pars:
classes
## Item parameters from Thissen & Steinberg (1984, p. 510) ## Items R,S,T,U for the whole test a <- matrix(c(-1.7, -1, 1.1, .3, 1.9, -2.1, -.6, 1.2, 2.3, -.8, -1.3, -.9, -.2, 1.9, .5, -1.9, -.5, 0, -.6, 1.9),4,5,byrow=TRUE) c <- matrix(c(.3, -2.3, 2.4, -2.5, 2.1, 2.1, .05, -3, -.6, 1, -.9, -2.5, -.1, 1.8, 1.6, -.1, -2, .5, .8, .8),4,5,byrow=TRUE) d <- matrix(c(.25, .25, .25, .25, .2, .2, .4, .2, .2, .2, .4, .2, .25, .25, .25, .25), 4,4,byrow=TRUE) pars <- cbind(a,c,d) x <- mcm(pars, rep(5,4)) plot(x,item.names=paste("Item",c("R","S","T","U")), auto.key=list(space="right")) ## Item parameters from Thissen & Steinberg (1984, p. 511) ## Items W,X,Y,Z for the pars <- vector("list",3) pars[[1]] <- matrix(c(-2.3, -.2, 2, .9, -.3, -.8, .6, -.5, 1.1, -.4, -.5, -.2, 2, -1.2, 0, -1.5, -.7, -.2, .1, 2.3),4,5,byrow=TRUE) pars[[2]] <- matrix(c(.5, .7, -.5, -1.9, 1.1, 1.6, -2.8, 1.5, 0, -.3, -.3, .7, -1, .7, 0, .4, .4, -.5, .5, -.8),4,5,byrow=TRUE) pars[[3]] <- matrix(c(.2, .4, .2, .2, .2, .2, .4, .2, .2, .4, .2, .2, .2, .2, .2, .4), 4,4,byrow=TRUE) x <- mcm(pars, rep(5,4)) plot(x,item.names=paste("Item",c("W","X","Y","Z")), auto.key=list(space="right"))