# loading data
data(Anxiety, package = "ShinyItemAnalysis")
Data <- Anxiety[, paste0("R", 1:29)] # items
group <- Anxiety[, "gender"] # group membership variable
# testing both DIF effects with adjacent category logit model
(x <- difORD(Data, group, focal.name = 1, model = "adjacent"))
if (FALSE) {
# graphical devices
plot(x, item = 6)
plot(x, item = "R6")
plot(x, item = "R6", group.names = c("Males", "Females"))
# estimated parameters
coef(x)
coef(x, SE = TRUE) # with SE
coef(x, SE = TRUE, simplify = TRUE) # with SE, simplified
# AIC, BIC, log-likelihood
AIC(x)
BIC(x)
logLik(x)
# AIC, BIC, log-likelihood for the first item
AIC(x, item = 1)
BIC(x, item = 1)
logLik(x, item = 1)
# testing both DIF effects with Benjamini-Hochberg adjustment method
difORD(Data, group, focal.name = 1, model = "adjacent", p.adjust.method = "BH")
# testing both DIF effects with item purification
difORD(Data, group, focal.name = 1, model = "adjacent", purify = TRUE)
# testing uniform DIF effects
difORD(Data, group, focal.name = 1, model = "adjacent", type = "udif")
# testing non-uniform DIF effects
difORD(Data, group, focal.name = 1, model = "adjacent", type = "nudif")
# testing both DIF effects with total score as matching criterion
difORD(Data, group, focal.name = 1, model = "adjacent", match = "score")
# testing both DIF effects with cumulative logit model
(x <- difORD(Data, group, focal.name = 1, model = "cumulative"))
# graphical devices
plot(x, item = 7, plot.type = "cumulative")
plot(x, item = 7, plot.type = "category")
# estimated parameters
coef(x, simplify = TRUE)
}
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