responses4pl {irtProb} | R Documentation |
Simulation of response patterns and computation of the probability of the patterns according to the one, two, three and four parameters logistic item response models.~
gr4pl(N = 10, theta = 0, a = 1, b = 0, c = 0, d = 1) ggr4pl(n = 5, rep = 1, theta = 0, a = rep(1, n), b = rep(0, n), c = rep(0, n), d = rep(1, n)) pggr4pl(x = ggr4pl(rep = 1), rep = 1, n = dim(x)[2], N = dim(x)[1], theta = rep(0, N), a = rep(1, n), b = rep(0, n), c = rep(0, n), d = rep(1, n), log.p=FALSE, TCC = FALSE)
theta |
numeric; vector of proficiency levels (z sscores). |
x |
numeric matrix; response patterns (0 or 1). |
rep |
numeric; number of replications of the simulation of the response patterns. |
n |
numeric; number of items. |
N |
numeric; number of response patterns |
a |
numeric; item discrimination parameters. |
b |
numeric; item difficulty parameters. |
c |
numeric; item pseudo-guessing parameters. |
d |
numeric; item inattention parameters. |
log.p |
logical; if TRUE, probabilities p are given as log(p). |
TCC |
logical; if TRUE generate the TCC figures for each response patterns. Default FALSE. |
The function gr4pl
generates N
responses to an item according to the theta parameter and the items parameters.
The funcfion ggr4pl
will be used to generate rep
respose patterns at n
items. To compute
the probability of the response patterns, according to known person and item parameters, the function pggr4pl
will be applied.
gr4pl |
numeric; vector of item responses (0 or 1). |
ggr4pl |
numeric; data.frame of responses at n items. |
pggr4pl |
logical; if (TCC ==TRUE) return(list(prob=prob, tcc=tcc)); if (TCC==FALSE) return(prob) |
Gilles Raiche, Universite du Quebec a Montreal (UQAM),
Departement d'education et pedagogie
Raiche.Gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/
Hambleton, R. K. and Swaminathan, H. (1985). Item response theory - Principles and applications. Boston, Massachuset: Kluwer.
grm4pl
, ggrm4pl
, pggrm4pl
,
ctt2irt
, irt2ctt
## .................................................................... # Generation of reponses (0,1) from r4pl() for N subjects (default value of N= 10) gr4pl(c = 1) gr4pl(N = 5, theta = c(-4, 4), c = 0) # Generation of a 7 responses pattern (0,1) for [rep * length(theta)] subjects # The subjects number is equal to [rep * length(theta)]] # a,b,c et d are item parameters vectors nitems <- 7 N <- 10 a <- rep(1, nitems) b <- rnorm(nitems) c <- rep(0, nitems) d <- rep(1, nitems) theta <- seq(-4,4,length=5) x <- ggr4pl(n = nitems, rep = N, theta = theta, a = a, b = b, c = c, d = d) x ## Probability of a 10 responses pattern and test caracteristic curve (TCC) nitems <- 10 a <- rep(1,nitems) b <- seq(-4,4,length=nitems) c <- rep(0,nitems) d <- rep(1,nitems) N <- 3 theta <- seq(-1,1,length=12) # Generation of the response patterns x <- ggr4pl(n = nitems, rep = N, theta = theta, a = a, b = b, c = c, d = d) x # Without TCC res <- pggr4pl(x=x, rep=N, theta=theta,a=a,c=c,d=d,TCC=FALSE); res # With TCC for each response pattern res <- pggr4pl(x=x, rep=N, theta=theta,a=a,c=c,d=d,TCC=TRUE); res ## ....................................................................