sep.pars-methods function

Separate Item Parameters

This function splits the item parameters in the specified object into discrimination/slope parameters, difficulty/step/threshold/category parameters, and lower asymptote/category probability parameters. methods

sep.pars(x, cat, poly.mod, dimensions = 1, location = FALSE, 
  loc.out = FALSE, ...)

## S4 method for signature 'numeric'
sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...)

## S4 method for signature 'matrix'
sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...)

## S4 method for signature 'data.frame'
sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...)

## S4 method for signature 'irt.pars'
sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...)

## S4 method for signature 'list'
sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...)

Arguments

• x: Object containing item parameters. For details on the formatting of parameters for specific item response models see the corresponding methods (i.e., drm, gpcm, grm, mcm, and nrm ). See the Methods section for as.irt.pars for details on how to format the item parameters when combining parameters from multiple models.
• 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
• dimensions: number of modeled dimensions
• 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

Returns an object of class sep.pars

Author(s)

Jonathan P. Weeks weeksjp@gmail.com

Examples

###### Unidimensional Examples ######
# 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 (the parameters are 
# formatted as a matrix)
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 (the parameters are 
# included in a list)
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, descrip=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, descrip=TRUE)

###### Multidimensional Examples ######
# Create object for three dichotomous (M1PL) items for two dimensions 
# with parameters related to item difficulties of -1, 0, 1
x <- sep.pars(c(-1,0,1), dimensions=2)

# Create object for three dichotomous (M3PL) items and two polytomous 
# (MGPCM) items without a location parameter for four dimensions 
# (the parameters are included in a list)
a <- matrix(c(0.5038, 2.1910, 1.1317, 0.2493,
  2.9831, 0.4811, 0.3566, 0.4306,
  0.2397, 0.2663, 1.5588, 0.5295,
  0.2020, 0.2410, 1.2061, 0.5552,
  0.2054, 0.6302, 0.3152, 0.2037),5,4,byrow=TRUE)
b <- matrix(c(0.5240, rep(NA,3),
  -1.8841, rep(NA,3),
  0.2570, rep(NA,3),
  -1.4207, 0.3041, -0.5450, NA,
  -2.1720, 0.0954, 0.6531, 0.9114),5,4,byrow=TRUE)
c <- c(0.1022, 0.3528, 0.2498, 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, dimensions=4)
summary(x, descrip=TRUE)