sep.pars-methods {plink} | R Documentation |
This function splits the item parameters in the specified object into discrimination parameters, difficulty/step/threshold/category parameters, and lower asymptote/category probability parameters.
sep.pars(x, cat, poly.mod, location = FALSE, loc.out = FALSE, ...) ## S4 method for signature 'numeric' sep.pars(x, cat, poly.mod, location, loc.out, ...) ## S4 method for signature 'matrix' sep.pars(x, cat, poly.mod, location, loc.out, ...) ## S4 method for signature 'data.frame' sep.pars(x, cat, poly.mod, location, loc.out, ...) ## S4 method for signature 'irt.pars' sep.pars(x, cat, poly.mod, location, loc.out, ...) ## S4 method for signature 'list' sep.pars(x, cat, poly.mod, location, loc.out, ...)
x |
Object containing item parameters. See below for more details. |
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 |
location |
if TRUE , the step parameters are deviations from a location
parameter |
loc.out |
if TRUE , the step/threshold parameters will be reformated to
be deviations from a location parameter |
... |
further arguments passed to or from other methods |
Returns an object of class sep.pars
x
is assumed to be a vector of item difficulties.
Discrimination parameters are set to one and the lower asymptote values are set to zero
for all items.x
can include item parameters from multiple models. The general
format for structuring x
is an additive column approach. That is, the left-most
columns are typically for discrimination parameters, the next column, if applicable, is
for location parameters, the next set of columns is for difficulty/threshold/step/category
parameters, and the final set of columns is for guessing parameters. When multiple models
are included, or models with differing numbers of response categories, not all cells in
x
will have data. In these instances, cells with no data should be NA
.
x
should contain at least two
columns, the first for item discriminations (identical for all items) and the second
for item difficulties. The lower asymptote defaults to zero for all items; however, a
third column of zeros can be included.x
should include at lease two columns, the first for item
discriminations and the second for item difficulties. The lower asymptote defaults to
zero for all items; however, a third column of zeros can be included.x
should include three columns, the first for item discriminations,
the second for item difficulties, and the third for lower asymptote values.NA
.NA
.x
is an object of class irt.pars
If the nominal response model or multiple-choice model are used in conjunction with
another model with only a single discrimination parameter, the first list element should
be a matrix with discrimination values for the single discrimination value models in the
first column and NA
s in the remaining columns. Similarly, if the multiple-choice
model is included, lower asymptote parameters for the 3PL model should be included in the
first column of the matrix of guessing probabilities in the third list element with NA
s
for all other columns. For all other models, all the columns should be NA
.
x
should contain
at least two list elements, the first for item discriminations (identical for all items)
and the second for item difficulties. The lower asymptote defaults to zero for all items;
however, a third element with a vector/matrix of zeros can be included.
If x
includes parameters for multiple models and the number of list elements is
two, the first element should include a vector of ones. If the number of list elements
is three, the third element should include a vector of zeros.
x
should include at lease two list elements, the first for item
discriminations and the second for item difficulties. The lower asymptote defaults to
zero for all items; however, a third element with a vector/matrix of zeros can be included.
If x
includes parameters for multiple models and the number of list elements
is three, the third element should include a vector of zeros.
x
should include three list elements, the first for item discriminations,
the second for item difficulties, and the third for lower asymptote values.The list element containing the step or step deviation parameters should be a matrix. If the parameters are deviations from a location parameter, the location parameter must be in the first column with the remaining columns for the step deviation values. If no location parameter is included, the step parameters should begin in column one. (See the method for x = 'matrix' above for more information on the formatting for this object).
If x
includes multiple models and the number of list elements is two, the first
list element should include a vector of ones or a vector of constant discrimination
values. If there are three columns, the third list element should conatain a vector
of NA
for all the PCM items.
If x
includes multiple models and there are three list elements, the third element
should conatain a vector of NA
for all the GPCM items.
If x
includes multiple models and there are three list elements, the third element
should conatain a vector of NA
for all the GRM items.
NA
for the NRM items.Jonathan P. Weeks weeksjp@gmail.com
# Create object for three dichotomous (1PL) items with difficulties -1, 0, 1 x <- sep.pars(c(-1,0,1)) # Create object for three dichotomous (3PL) items and two polytomous (gpcm) items # without a location parameter (use signature matrix, missing) 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 <- sep.pars(pars, cat, pm) summary(x) # Create object for three dichotomous (3PL) items and two polytomous (gpcm) items # without a location parameter a <- c(1.2, .8, .9, .64, .88) b <- matrix(c( 2.3, rep(NA,3), -1.1, rep(NA,3), -.2, rep(NA,3), -1.8, -.73, .45, NA, .06, 1.4, 1.9, 2.6),5,4,byrow=TRUE) c <- c(1.4, 1.9, 2.6, NA, NA) pars <- list(a,b,c) cat <- c(2,2,2,4,5) pm <- as.poly.mod(5, c("drm","gpcm"), list(1:3,4:5)) x <- sep.pars(pars, cat, pm) summary(x) # Create object for three dichotomous (3PL) items, four polytomous items, # two gpcm items and two nrm items. Include a location parameter for the # gpcm items. Maintain the location parameter in the output. 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 <- sep.pars(pars, cat, pm, location=TRUE, loc.out=TRUE) summary(x, TRUE) # Create irt.pars object with two groups then run sep.pars pm <- as.poly.mod(36) x <- as.irt.pars(KB04$pars, KB04$common, cat=list(rep(2,36),rep(2,36)), list(pm,pm), grp.names=c("form.x","form.y")) out <- sep.pars(x) summary(out, TRUE)