R 3모수 모형

1 개요[ | ]

R IRT 3모수 모형
R 문항반응이론 3PLM
R 문항반응이론 3모수 모형

2 ltm 패키지[ | ]

R

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df <- read.csv("https://raw.githubusercontent.com/jmnote/ds/main/github.com/cran/ltm/data/LSAT.csv")
library(ltm)
model <- tpm(df)

# 로그우도
model$log.Lik

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# 계수
coef(model)

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# 문항특성곡선
plot(model)

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# 문항정보곡선
plot(model, "IIC")

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3 irtplay 패키지[ | ]

R

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df <- read.csv("https://raw.githubusercontent.com/jmnote/ds/main/github.com/cran/ltm/data/LSAT.csv")
library(irtplay)
model <- est_irt(data=df, model="3PLM", verbose=FALSE)
coef(model)

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plotICC <- function(model) {
  p1 <- model$estimates$par.1
  p2 <- model$estimates$par.2
  p3 <- model$estimates$par.3
  p3[is.na(p3)] <- 0
  D <- model$scale.D
  z <- seq(-4, 4, length=100)
  len <- nrow(model$estimates)
  pos <- round(seq(10, 90, length=len))
  for( i in 1:len ) {
    f <- function(x) {p3[i]+(1-p3[i])*plogis(p1[i]*D*(x-p2[i]))}
    if( i == 1 ) plot(z, f(z), type='l', col=i, ylim=c(0,1),
                      main="Item Characteristic Curves",
                      xlab="Ability", ylab="Probability")
    else lines(z, f(z), type='l', col=i, ylim=c(0,1))
    text(z[pos[i]], f(z[pos[i]]), adj=c(0,2), labels=c(paste('Item',i)), col=i)
  }
}
plotICC(model)

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4 같이 보기[ | ]