R 1모수 모형

1 개요

R IRT 1모수 모형
R 문항반응이론 1PLM
R 문항반응이론 1모수 모형

2 ltm 패키지

df <- read.csv("https://github.com/jmnote/zdata/raw/master/github.com/cran/ltm/data/LSAT.csv")
library(ltm)
model <- rasch(df)

# 로그우도
model$log.Lik

# 계수
coef(model)

# 문항특성곡선
plot(model)

# 문항정보곡선
plot(model, "IIC")

3 irtplay 패키지

df <- read.csv("https://github.com/jmnote/zdata/raw/master/github.com/cran/ltm/data/LSAT.csv")
library(irtplay)
model <- est_irt(data=df, model="1PLM", verbose=FALSE)
coef(model)

plotICC <- function(model) {
  D <- model$scale.D
  x <- seq(-4, 4, .05)
  len <- dim(model$estimates)[1]
  offset <- 1.5 - seq(0, 1.5, length=len)
  for( i in 1:len ) {
    p1 <- model$estimates$par.1[i]
    p2 <- model$estimates$par.2[i]
    p3 <- model$estimates$par.3[i]
    if(is.na(p1)) p1 <- 1
    if(is.na(p2)) p2 <- 0
    if(is.na(p3)) p3 <- 0
    f <- function(x) {p3+(1-p3)/(1+exp(-p1*D*(x-p2)))}
    if( i == 1 ) plot(x, f(x), type='l', col=i, ylim=c(0,1),
                      main="Item Characteristic Curves",
                      xlab="Ability",
                      ylab="Probability")
    else lines(x, f(x), type='l', col=i, ylim=c(0,1))
    t <- p2 + offset[i]
    text(t, f(t), adj=c(0,2), labels=c(paste('Item',i)), col=i)
  }
}
plotICC(model)

4 같이 보기