mixed-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 when the items are from a mixed-format test.
mixed(x, cat, poly.mod, theta, dimensions = 1, ...) ## S4 method for signature 'numeric', 'numeric' mixed(x, cat, poly.mod, theta, dimensions, ...) ## S4 method for signature 'matrix', 'numeric' mixed(x, cat, poly.mod, theta, dimensions, ...) ## S4 method for signature 'data.frame', 'numeric' mixed(x, cat, poly.mod, theta, dimensions, ...) ## S4 method for signature 'list', 'numeric' mixed(x, cat, poly.mod, theta, dimensions, ...) ## S4 method for signature 'irt.pars', 'ANY' mixed(x, cat, poly.mod, theta, dimensions, ...) ## S4 method for signature 'sep.pars', 'ANY' mixed(x, cat, poly.mod, theta, dimensions, ...)
x |
an R object containing item parameters |
cat |
vector identifying the number of response categories for each item. If
multiple-choice model items are included, cat for these items should equal
the number of response categories plus one (the additional category is for
'do not know') |
poly.mod |
object of class poly.mod identifying
the items associated with each IRT model |
theta |
vector, matrix, or list 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 0.5. See details below for more information. |
dimensions |
number of modeled dimensions |
... |
further arguments passed to or from other methods |
The item parameters supplied to this method can be associated with a single IRT model or
multiple models. When the parameters are tied to only one model, the format of x
(for either unidimensional or multidimensional models) should follow the conventions in
drm
for dichotomous response models (i.e. 1PL, 2PL, 3PL), gpcm
for the partial credit model and generalized partial credit model, grm
for
the graded response model, mcm
for the multiple-choice model, and
nrm
for the nominal response model. When the parameters are associated with
two or more models, the parameters should be combined. See as.irt.pars
or
for more details on how the parameters from different models can be combined. Additional
arguments for the above models can be passed to this method as well.
theta
can be specified as a vector, matrix, or list. For the unidimensional case, theta
should be a vector. If a matrix or list of values is supplied, they will be converted to a single vector
of theta values. For the multidimensional case, if a vector of values is supplied it will be assumed
that this same set of values should be used for each dimension. Probabilities will be computed for each
combination of theta values. Similarly, if a list is supplied, probabilites will be computed for each
combination of theta values. In instances where probabilities are desired for specific combinations of
theta values, a j x m matrix should be specified for j ability points and m dimensions where the columns
are ordered from dimension 1 to m.
Returns an object of class irt.prob
Jonathan P. Weeks weeksjp@gmail.com
plot:
plot item characteristic/category curves
irt.prob
, irt.pars
, sep.pars:
classes
###### Unidimensional Examples ###### # Compute probabilites for three dichotomous (3PL) items and two polytomous (gpcm) items # without a location parameter dichot <- matrix(c(1.2, .8, .9, 2.3, -1.1, -.2, .24, .19, .13),3,3) poly <- matrix(c(.64, -1.8, -.73, .45, NA, .88, .06, 1.4, 1.9, 2.6),2,5,byrow=TRUE) pars <- rbind(cbind(dichot,matrix(NA,3,2)),poly) cat <- c(2,2,2,4,5) pm <- as.poly.mod(5, c("drm","gpcm"), list(1:3,4:5)) x <- mixed(pars, cat, pm) plot(x) # Compute probabilities for three dichotomous (3PL) items, four polytomous items, # two gpcm items and two nrm items. Include a location parameter for the # gpcm items a <- matrix(c( 1.2, rep(NA,4), .8, rep(NA,4), .9, rep(NA,4), .64, rep(NA,4), .88, rep(NA,4), .905, .522, -.469, -.959, NA, .828, .375, -.357, -.079, -.817),7,5,byrow=TRUE) b <- matrix(c( 2.3, rep(NA,4), -1.1, rep(NA,4), -.2, rep(NA,4), -.69, -1.11, -.04, 1.14, NA, 1.49, -1.43, -.09, .41, 1.11, .126, -.206, -.257, .336, NA, .565, .865, -1.186, -1.199, .993),7,5,byrow=TRUE) c <- c(.14, .19, .26, rep(NA,4)) pars <- list(a,b,c) cat <- c(2,2,2,4,5,4,5) pm <- as.poly.mod(7, c("drm","gpcm","nrm"), list(1:3,4:5,6:7)) x <- mixed(pars, cat, pm, location=TRUE) plot(x) ###### Multidimensional Example ###### # Compute response probabilities for four dichotomous items # modeled using the M2PL and three polytomous items modeled # using the multidimensional graded response model. For the # later items, cumulative probabilities are computed. a <- matrix(c(1.66,1.72,.69,.19,.88,1.12,.68,1.21, .873, .226, .516, .380, .613, .286 ),7,2,byrow=TRUE) d <- matrix(c(-.38,NA,NA,NA,NA, -.68,NA,NA,NA,NA, -.91,NA,NA,NA,NA, -1.08,NA,NA,NA,NA, 2.255, 1.334, -.503, -2.051, -3.082, 1.917, 1.074, -.497, -1.521, -2.589, 1.624, .994, -.656, -1.978, NA),7,5,byrow=TRUE) cat <- c(2,2,2,2,6,6,5) pars <- cbind(a,d) pm <- as.poly.mod(7,c("drm","grm"),list(1:4,5:7)) x <- mixed(pars, cat, pm, dimensions=2, catprob=TRUE) plot(x)