sep.pars-methods: Separate Item Parameters

In plink: IRT Separate Calibration Linking Methods

Description

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.

Usage

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

Value

Returns an object of class sep.pars

Author(s)

Jonathan P. Weeks [e-mail]

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)

plink documentation built on May 1, 2019, 8:07 p.m.