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library(ltm)
df <- read.csv("https://github.com/jmnote/zdata/raw/master/github.com/cran/ltm/data/LSAT.csv")
model <- ltm(df ~ z1, IRT.param = TRUE)
coef(model)
Loading required package: MASS Loading required package: msm Loading required package: polycor Dffclt Dscrmn Item.1 -3.3597341 0.8253715 Item.2 -1.3696497 0.7229499 Item.3 -0.2798983 0.8904748 Item.4 -1.8659189 0.6885502 Item.5 -3.1235725 0.6574516
library(ltm)
df <- read.csv("https://github.com/jmnote/zdata/raw/master/github.com/cran/ltm/data/WIRS.csv")
model <- ltm(df ~ z1, constr = rbind(c(1, 1, 1), c(6, 2, -0.5))
coef(model)
## Two-factor model with an interaction term
library(ltm)
df <- read.csv("https://github.com/jmnote/zdata/raw/master/github.com/cran/ltm/data/WIRS.csv")
model <- ltm(df ~ z1 * z2)
coef(model)
## One-factor and a quadratic term
library(ltm)
df <- read.csv("https://github.com/jmnote/zdata/raw/master/github.com/cran/ltm/data/Mobility.csv")
model <- ltm(df ~ z1 + I(z1^2))
coef(model)
## The two-parameter logistic model with 20 quadrature points and 20 EM iterations
library(ltm)
df <- read.csv("https://github.com/jmnote/zdata/raw/master/github.com/cran/ltm/data/Abortion.csv")
model <- ltm(df ~ z1, control = list(GHk = 20, iter.em = 20))
coef(model)