likelihoodCurve: Functions to Graph m4pl Likelihood Curves

In irtProb: Utilities and Probability Distributions Related to Multidimensional Person Item Response Models

Description

likelihoodCurve and groupLikelihoodCurve are used to graph the likelihood function curves according to the only theta, theta anc pseudo-guessing, theta and fluctuation, like theta and inattention m4pl models: only two simultaneous person parameters are taken in account.

Usage

 likelihoodCurve(x, s, b, c, d,
                 limitT = c(min = -4, max = 4), limitS = c(min = 0, max = 4),
                 limitC = c(min = 0, max = 1), limitD = c(min = 0, max = 1),
                 grain         = 150, annotate = TRUE,
                 logLikelihood = FALSE, color = TRUE,
                 main          = "Likelihood Curve",
                 xlab          = expression(theta), ylab = NULL, zlab = "P(X)",
                 type          = "levelplot", m = 0)

 groupLikelihoodCurves(plotT, plotS, plotC, plotD, main=NULL, cex=0.7)
 

Arguments

x	numeric: binary (0,1) response pattern.
s	numeric: vector of inverse a discrimination item parameters.
b	numeric: vector of b difficulty item parameters.
c	numeric: vector of c pseudo-guessing item parameters.
d	numeric: vector of d inattention item parameters.
limitT	numeric: minimum and maximum of the proficiency person parameter used for the x axis.
limitS	numeric: minimum and maximum of the fluctuation person parameter used for the y axis.
limitC	numeric: minimum and maximum of the pseudo-guessing person parameter used for the y axis.
limitD	numeric: minimum and maximum of the inattention person parameter used for the y axis.
grain	numeric: number of theta values used to compute pattern distribution probability.
annotate	logical: does annotation is applied to the graphs?
logLikelihood	numeric: data.frame of the log likelihood of the studied models.
color	logical: does color is applied to contourplot or wireframe.
main	character: main title.
xlab	character: x axis label.
ylab	character: y axis label.
zlab	character: z axis label.
type	character: type of 3D plot ("levelplot", "contourplot" or "wireframe").
m	numeric: mean of the a priori probability distribution.
plotT	trellis: 2D theta likelihood curve.
plotS	trellis: 3D theta * S likelihood curve.
plotC	trellis: 3D theta * C likelihood curve.
plotD	trellis: 3D theta * D likelihood curve.
cex	numeric: zaxis label size.

Value

likelihoodCurve

plotT	trellis: theta likelihood functions curves.
plotS	trellis: theta * S likelihood functions curves.
plotC	trellis: theta * C likelihood functions curves.
plotD	trellis: theta * D likelihood functions curves.
parameters	numeric: list of data.frame of person parameters for each model studied. Each element of the list shows estimation with different a priori probability distributions (uniform, normal and none).
logLikelihood	numeric: data.frame of the log likelihood for each model studied.

groupLikelihoodCurves

graphic

graphic: all the likelihood functions curves are displayed.

Author(s)

Gilles Raiche, Universite du Quebec a Montreal (UQAM),

Departement d'education et pedagogie

[e-mail], http://www.er.uqam.ca/nobel/r17165/

Examples

## Not run: 
 ## SIMULATION OF A RESPONSE PATTERN WITH 60 ITEMS
 nItems <- 60
 a      <- rep(1.702,nItems); b <- seq(-4,4,length=nItems)
 c      <- rep(0,nItems);     d <- rep(1,nItems)
 nSubjects <-  1
 theta     <-  -1
 S         <-  0.0
 C         <-  0.5
 D         <-  0.0
 
 set.seed(seed = 100)
 x         <- ggrm4pl(n=nItems, rep=1,
                      theta=theta, S=S, C=C, D=D,
                      s=1/a, b=b,c=c,d=d)

 ## Likelihood curves, person parameters estimates
  # and log likelihood of models graphed
 test <- likelihoodCurve(x=x, s=1/a, b=b, c=c, d=d, color=TRUE,
                         main="Likelihood Curve",
                         xlab=expression(theta), ylab=NULL, zlab="P(X)",
                         type="wireframe" , grain=50, limitD=c(0,1),
                         logLikelihood=FALSE, annotate=TRUE )

 # Contentd of the object test
 test$plotT
 test$plotC
 test$plotS
 test$plotD
 test$par
 round(test$logLikelihood,2)

 ## Graph of all the likelihood function curves
 groupLikelihoodCurves(test$plotT, test$plotS, test$plotC, test$plotD,
                       main=NULL, cex=0.7)
 
## End(Not run)
 

irtProb documentation built on May 2, 2019, 1:30 p.m.