| personfit {mirt} | R Documentation |
personfit calculates the Zh values from Drasgow, Levine and Williams (1985) for
unidimensional and multidimensional models. For Rasch models infit and outfit statistics are
also produced. The returned object is a data.frame
consisting either of the tabulated data or full data with the statistics appended to the
rightmost columns.
personfit(x, method = "EAP", Theta = NULL, stats.only = TRUE, ...)
x |
a computed model object of class |
method |
type of factor score estimation method. See |
Theta |
a matrix of factor scores used for statistics that require emperical estimates. If
supplied, arguments typically passed to |
stats.only |
logical; return only the person fit statistics without their associated response pattern? |
... |
additional arguments to be passed to |
Phil Chalmers rphilip.chalmers@gmail.com
Drasgow, F., Levine, M. V., & Williams, E. A. (1985). Appropriateness measurement with polychotomous item response models and standardized indices. British Journal of Mathematical and Statistical Psychology, 38, 67-86.
Reise, S. P. (1990). A comparison of item- and person-fit methods of assessing model-data fit in IRT. Applied Psychological Measurement, 14, 127-137.
Wright B. D. & Masters, G. N. (1982). Rating scale analysis. MESA Press.
## Not run:
#make some data
set.seed(1234)
a <- matrix(rlnorm(20),ncol=1)
d <- matrix(rnorm(20),ncol=1)
items <- rep('dich', 20)
data <- simdata(a,d, 2000, items)
x <- mirt(data, 1)
fit <- personfit(x)
head(fit)
#using precomputed Theta
Theta <- fscores(x, method = 'MAP', full.scores = TRUE)
head(personfit(x, Theta=Theta))
#muliple group Rasch model example
set.seed(12345)
a <- matrix(rep(1, 15), ncol=1)
d <- matrix(rnorm(15,0,.7),ncol=1)
itemtype <- rep('dich', nrow(a))
N <- 1000
dataset1 <- simdata(a, d, N, itemtype)
dataset2 <- simdata(a, d, N, itemtype, sigma = matrix(1.5))
dat <- rbind(dataset1, dataset2)
group <- c(rep('D1', N), rep('D2', N))
models <- 'F1 = 1-15'
mod_Rasch <- multipleGroup(dat, models, itemtype = 'Rasch', group = group)
coef(mod_Rasch, simplify=TRUE)
pf <- personfit(mod_Rasch, method='MAP')
head(pf)
## End(Not run)