likelihoodCurve {irtProb} | R Documentation |
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.
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)
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. |
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. |
graphic |
graphic: all the likelihood functions curves are displayed. |
Gilles Raiche, Universite du Quebec a Montreal (UQAM),
Departement d'education et pedagogie
Raiche.Gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/
## 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)