R/Logistik.R

In difR: Collection of Methods to Detect Dichotomous Differential Item Functioning (DIF)

Logistik<-function (data, member, member.type = "group", match = "score", 
    anchor = 1:ncol(data), type = "both", criterion = "LRT",all.cov = FALSE) 
{
    R2 <- function(m, n) 1 - (exp(-m$null.deviance/2 + m$deviance/2))^(2/n)
    R2max <- function(m, n) 1 - (exp(-m$null.deviance/2))^(2/n)
    R2DIF <- function(m, n) R2(m, n)/R2max(m, n)
    dev <- R2full <- R2simple <- deltaR <- NULL
    mFull <- mSimple <- seFull <- seSimple <- matrix(0, ncol(data), 
        4)
   if (all.cov) cov.matM0<-cov.matM1<-vector("list",ncol(data))
    else cov.matM0<-cov.matM1<-NULL
    if (member.type == "group") 
        GROUP <- as.factor(member)
    else GROUP <- member
    for (item in 1:ncol(data)) {
        if (match[1] == "score") {
            data2 <- rbind(data[, anchor])
            if (sum(anchor == item) == 0) 
                data2 <- cbind(data2, data[, item])
            SCORES <- rowSums(data2, na.rm = TRUE)
        }
        else SCORES <- match
        ITEM <- data[, item]
        m0 <- switch(type, both = glm(ITEM ~ SCORES * GROUP, 
            family = "binomial"), udif = glm(ITEM ~ SCORES + 
            GROUP, family = "binomial"), nudif = glm(ITEM ~ SCORES * 
            GROUP, family = "binomial"))
        m1 <- switch(type, both = glm(ITEM ~ SCORES, family = "binomial"), 
            udif = glm(ITEM ~ SCORES, family = "binomial"), nudif = glm(ITEM ~ 
                SCORES + GROUP, family = "binomial"))
        if (criterion == "LRT") 
            dev[item] <- deviance(m1) - deviance(m0)
        else {
            if (criterion != "Wald") 
                stop("'criterion' must be either 'LRT' or Wald'", 
                  call. = FALSE)
            else {
                coeff <- as.numeric(coefficients(m0))
                covMat <- summary(m0)$cov.scaled
                if (type == "udif") 
                  C <- rbind(c(0, 0, 1))
                else {
                  if (type == "nudif") 
                    C <- rbind(c(0, 0, 0, 1))
                  else C <- rbind(c(0, 0, 1, 0), c(0, 0, 0, 1))
                }
                dev[item] <- t(C %*% coeff) %*% solve(C %*% covMat %*% 
                  t(C)) %*% C %*% coeff
            }
        }
        R2full[item] <- R2DIF(m0, nrow(data))
        R2simple[item] <- R2DIF(m1, nrow(data))
        deltaR[item] <- R2DIF(m0, nrow(data)) - R2DIF(m1, nrow(data))
        mFull[item, 1:length(m0$coefficients)] <- m0$coefficients
        mSimple[item, 1:length(m1$coefficients)] <- m1$coefficients
        seFull[item, 1:length(m0$coefficients)] <- sqrt(diag(vcov(m0)))
        seSimple[item, 1:length(m1$coefficients)] <- sqrt(diag(vcov(m1)))
        if (all.cov) cov.matM0[[item]]<-vcov(m0)
        if (all.cov) cov.matM1[[item]]<-vcov(m1)
    }
    colnames(mFull) <- colnames(mSimple) <- colnames(seFull) <- colnames(seSimple) <- c("(Intercept)", 
        "SCORE", "GROUP", "SCORE:GROUP")
    res <- list(stat = dev, R2M0 = R2full, R2M1 = R2simple, deltaR2 = deltaR, 
        parM0 = mFull, parM1 = mSimple, seM0 = seFull, seM1 = seSimple, 
        cov.M0=cov.matM0, cov.M1=cov.matM1,
        criterion = criterion, member.type = member.type, match = ifelse(match[1] == 
            "score", "score", "matching variable"))
    return(res)
}