library(ltm)
library(mirt)
library(ShinyItemAnalysis)
# loading data
data(GMAT, package = "difNLR")
# fitting 3PL model
fit <- mirt(GMAT[, 1:20], model = 1, itemtype = "3PL", SE = TRUE)
# item response curves for item 1
itemplot(fit, 1)
itemplot(fit, 1, CE = TRUE)
# item information curves
itemplot(fit, 1, type = "info")
itemplot(fit, 1, type = "infoSE")
itemplot(fit, 1, type = "info", CE = TRUE)
# estimated parameters
coef(fit, simplify = TRUE)$items[1, ] # classical intercept-slope parametrization
coef(fit, printSE = TRUE)$Item1 # classical intercept-slope parametrization with SE
coef(fit)$Item1 # classical intercept-slope parametrization with CI
coef(fit, IRTpars = TRUE, simplify = TRUE)$items[1, ] # IRT parametrization
coef(fit, IRTpars = TRUE, printSE = TRUE)$Item1 # IRT parametrization with SE
coef(fit, IRTpars = TRUE)$Item1 # IRT parametrization with CI
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