lsirm: Fit a LSIRM ( Latent Space Item Response Model)

Description

lsirm is used to fit 1PL LSIRM, 2PL LSIRM, and ordinal GRM LSIRM using Bayesian methods.

Usage

lsirm(formula, ...)

Value

lsirm returns an object of class list.

See corresponding functions such as lsirm1pl for 1PL LSIRM and lsirm2pl for 2PL LSIRM.

Arguments

formula: The form of formula is lsirm(A ~ <term 1>(<term 2>, <term 3> ...)), where A is an item response matrix to be analyzed, <term1> is the model you want to fit and has one of the following values: "lsirm1pl", "lsirm2pl", "lsirmgrm", and "lsirmgrm2pl"., and <term 2>, <term 3>, etc. are each option for the model.
...: Additional arguments for the corresponding function.

Details

The descriptions of options for each model, such as <term 2> and <term 3>, are included in lsirm1pl for 1PL LSIRM, lsirm2pl for 2PL LSIRM, and lsirmgrm for ordinal GRM LSIRM.

Examples

Run this code

# \donttest{
# generate example item response matrix
data     <- matrix(rbinom(500, size = 1, prob = 0.5),ncol=10,nrow=50)

lsirm_result <- lsirm(data~lsirm1pl())
lsirm_result <- lsirm(data~lsirm2pl())

# Realistic example with BFPT data
data(BFPT)
dat <- BFPT
dat[(dat == 0) | (dat == 6)] <- NA
reverse <- c(2, 4, 6, 8, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 23, 25, 27, 32, 34, 36, 42, 44, 46)
dat[, reverse] <- 6 - dat[, reverse]
dat <- dat[complete.cases(dat), ]
# Fit model (subset for speed)
fit_bfpt <- lsirm(dat[1:50, 1:10] ~ lsirmgrm(niter = 1000, nburn = 500))
summary(fit_bfpt)

# Fit with adaptive MCMC
lsirm_result <- lsirm(data~lsirm1pl(adapt = list(use_adapt = TRUE)))

# }