IRT.WrightMap: Wright Map for Item Response Models by Using the 'WrightMap'...

In TAM: Test Analysis Modules

Wright Map for Item Response Models by Using the WrightMap Package

Description

This function creates a Wright map and works as a wrapper to the wrightMap function in the WrightMap package. The arguments thetas and thresholds are automatically generated from fitted objects in TAM.

Usage

## S3 method for class 'tam.mml'
IRT.WrightMap(object, prob.lvl=.5, type="PV", ...)

## S3 method for class 'tamaan'
IRT.WrightMap(object, prob.lvl=.5, type="PV", ...)

Arguments

object	Object of class tam.mml or class tamaan
prob.lvl	Requested probability level of thresholds.
type	Type of person parameter estimate. "PV" (plausible values), "WLE" (weighted likelihood estimates) and "Pop" (population trait distribution) can be specified.
...	Further arguments to be passed in the wrightMap (WrightMap) function. See Examples.

Details

A Wright map is only created for models with an assumed normal distribution. Hence, not for all models of the tamaan functions Wright maps are created.

Value

A Wright map generated by the WrightMap package.

Author(s)

The IRT.WrightMap function is based on the WrightMap::wrightMap function in the WrightMap package.

See Also

See the WrightMap::wrightMap function in the WrightMap package.

Examples

## Not run: 
library(WrightMap)

#############################################################################
# EXAMPLE 1: Unidimensional models dichotomous data
#############################################################################

data(data.sim.rasch)
str(data.sim.rasch)
dat <- data.sim.rasch

# fit Rasch model
mod1 <- TAM::tam.mml(resp=dat)
# Wright map
IRT.WrightMap( mod1 )
# some customized plots
IRT.WrightMap( mod1, show.thr.lab=FALSE, label.items=c(1:40), label.items.rows=3)

IRT.WrightMap( mod1,  show.thr.sym=FALSE, thr.lab.text=paste0("I",1:ncol(dat)),
     label.items="", label.items.ticks=FALSE)

#--- direct specification with wrightMap function
theta <- TAM::tam.wle(mod1)$theta
thr <- TAM::tam.threshold(mod1)

# default wrightMap plots
WrightMap::wrightMap( theta, thr, label.items.srt=90)
WrightMap::wrightMap( theta, t(thr), label.items=c("items") )

# stack all items below each other
thr.lab.text <- matrix( "", 1, ncol(dat) )
thr.lab.text[1,] <- colnames(dat)
WrightMap::wrightMap( theta, t(thr), label.items=c("items"),
       thr.lab.text=thr.lab.text, show.thr.sym=FALSE )

#############################################################################
# EXAMPLE 2: Unidimensional model polytomous data
#############################################################################

data( data.Students, package="CDM")
dat <- data.Students

# fit generalized partial credit model using the tamaan function
tammodel <- "
LAVAAN MODEL:
  SC=~ sc1__sc4
  SC ~~ 1*SC
    "
mod1 <- TAM::tamaan( tammodel, dat )
# create item level colors
library(RColorBrewer)
ncat <- 3               # number of category parameters
I <- ncol(mod1$resp)    # number of items
itemlevelcolors <- matrix(rep( RColorBrewer::brewer.pal(ncat, "Set1"), I),
        byrow=TRUE, ncol=ncat)
# Wright map
IRT.WrightMap(mod1, prob.lvl=.625, thr.sym.col.fg=itemlevelcolors,
     thr.sym.col.bg=itemlevelcolors, label.items=colnames( mod1$resp) )

#############################################################################
# EXAMPLE 3: Multidimensional item response model
#############################################################################

data( data.read, package="sirt")
dat <- data.read

# fit three-dimensional Rasch model
Q <- matrix( 0, nrow=12, ncol=3 )
Q[1:4,1] <- Q[5:8,2] <- Q[9:12,3] <- 1
mod1 <- TAM::tam.mml( dat, Q=Q, control=list(maxiter=20, snodes=1000)  )
summary(mod1)
# define matrix with colors for thresholds
c1 <- matrix( c( rep(1,4), rep(2,4), rep(4,4)), ncol=1 )
# create Wright map using WLE
IRT.WrightMap( mod1, prob.lvl=.65, type="WLE", thr.lab.col=c1, thr.sym.col.fg=c1,
        thr.sym.col.bg=c1, label.items=colnames(dat) )
# Wright map using PV (the default)
IRT.WrightMap( mod1, prob.lvl=.65, type="PV" )
# Wright map using population distribution
IRT.WrightMap( mod1, prob.lvl=.65, type="Pop" )

#############################################################################
# EXAMPLE 4: Wright map for a multi-faceted Rasch model
#############################################################################

# This example is copied from
# http://wrightmap.org/post/107431190622/wrightmap-multifaceted-models

library(WrightMap)
data(data.ex10)
dat <- data.ex10

#--- fit multi-faceted Rasch model
facets <- dat[, "rater", drop=FALSE]  # define facet (rater)
pid <- dat$pid  # define person identifier (a person occurs multiple times)
resp <- dat[, -c(1:2)]  # item response data
formulaA <- ~item * rater  # formula
mod <- TAM::tam.mml.mfr(resp=resp, facets=facets, formulaA=formulaA, pid=dat$pid)

# person parameters
persons.mod <- TAM::tam.wle(mod)
theta <- persons.mod$theta
# thresholds
thr <- TAM::tam.threshold(mod)
item.labs <- c("I0001", "I0002", "I0003", "I0004", "I0005")
rater.labs <- c("rater1", "rater2", "rater3")

#--- Plot 1: Item specific
thr1 <- matrix(thr, nrow=5, byrow=TRUE)
WrightMap::wrightMap(theta, thr1, label.items=item.labs,
   thr.lab.text=rep(rater.labs, each=5))

#--- Plot 2: Rater specific
thr2 <- matrix(thr, nrow=3)
WrightMap::wrightMap(theta, thr2, label.items=rater.labs,
   thr.lab.text=rep(item.labs,  each=3), axis.items="Raters")

#--- Plot 3a: item, rater and item*rater parameters
pars <- mod$xsi.facets$xsi
facet <- mod$xsi.facets$facet

item.par <- pars[facet=="item"]
rater.par <- pars[facet=="rater"]
item_rat <- pars[facet=="item:rater"]

len <- length(item_rat)
item.long <- c(item.par, rep(NA, len - length(item.par)))
rater.long <- c(rater.par, rep(NA, len - length(rater.par)))
ir.labs <- mod$xsi.facets$parameter[facet=="item:rater"]

WrightMap::wrightMap(theta, rbind(item.long, rater.long, item_rat),
    label.items=c("Items",  "Raters", "Item*Raters"),
    thr.lab.text=rbind(item.labs, rater.labs, ir.labs), axis.items="")

#--- Plot 3b: item, rater and item*rater (separated by raters) parameters

# parameters item*rater
ir_rater <- matrix(item_rat, nrow=3, byrow=TRUE)
# define matrix of thresholds
thr <- rbind(item.par, c(rater.par, NA, NA), ir_rater)
# matrix with threshold labels
thr.lab.text <- rbind(item.labs, rater.labs,
           matrix(item.labs, nrow=3, ncol=5, byrow=TRUE))

WrightMap::wrightMap(theta, thresholds=thr,
      label.items=c("Items", "Raters", "Item*Raters (R1)",
                           "Item*Raters (R2)", "Item*Raters (R3)"),
      axis.items="", thr.lab.text=thr.lab.text )

#--- Plot 3c: item, rater and item*rater (separated by items) parameters

# thresholds
ir_item <- matrix(item_rat, nrow=5)
thr <- rbind(item.par, c(rater.par, NA, NA), cbind(ir_item, NA, NA))
# labels
label.items <- c("Items", "Raters", "Item*Raters\n (I1)", "Item*Raters \n(I2)",
     "Item*Raters \n(I3)", "Item*Raters \n (I4)", "Item*Raters \n(I5)")
thr.lab.text <- rbind(item.labs,
          matrix(c(rater.labs, NA, NA), nrow=6, ncol=5, byrow=TRUE))

WrightMap::wrightMap(theta, thr,  label.items=label.items,
      axis.items="", thr.lab.text=thr.lab.text  )

## End(Not run)

TAM documentation built on Aug. 29, 2022, 1:05 a.m.