plot.standardization: Plot DDF
In manuelreif/mcIRT: IRT models for multiple choice items (mcIRT)

Description Usage Arguments Details Author(s) References See Also Examples

This function visualizes DDF summary indices and empirical distractor functions.

1 2	## S3 method for class 'standardization' plot(x, type = "rmwsd", ...)

`x`	An object of class `standardization`.
`type`	Choose between different types of plots. Valid inputs are "rmwsd", "catwise" and "allin1". See details for more information.
`...`	Further arguments for the `plot()` function.

There are different types of plots.

rmwsd Is a radar plot, which shows the difference of percent correct in 2 groups on the x-axis, and the variance of the difference on the y-axis.

catwise Prompts a plot which shows the empirical category probability functions.

allin1 Items on the x-axis, standardized p-difference on the y-axis for each category (categories are colored). This plot is supposed to give a quick impression of the overall DDF status of this item set.

For more detailed information, e.g. how to interprete the plots see Dorans and Kullick (1986).

Manuel Reif

Dorans, N. J. and Kullick, E. (1986). Demonstrating the utility of the standardization approach to assessing unexpected differential item performance on the Scholastic Aptitude Test. Journal of Educational Measurement, 23, 355-368.

DDF nrm reshMG

##### CREATE DATA ###########

x1 <- rnorm(10*5,0,1.7)
zeta <- tapply(x1,rep(1:10,each=5), function(x) round(x - mean(x),3))

# lambda parameters
x2 <- rnorm(10*5,0,1.1)
lam  <- tapply(x2,rep(1:10,each=5), function(x)
  {
  sort(round(x - mean(x),3),decreasing=FALSE)
  })


# create a parlist as a first step
ParList <- mapply(function(one,two)
  {
  x1 <- c(one,two)
  names(x1) <- paste0(rep(c("zeta","lam"),each=length(one)),1:length(one))
  return(x1)
  },one=zeta,two=lam,SIMPLIFY=FALSE)


names(ParList) <- paste0("item",1:length(ParList))


erglist     <- vector(mode="list",length=100)
erglistTIME <- vector(mode="list",length=100)

perp1 <- rnorm(6000,0,1)
perp2 <- rnorm(6000,0.3,1)

simdat1 <- NRM.sim(ParList,perp1)
simdat2 <- NRM.sim(ParList,perp2)

simdatall <- rbind(simdat1,simdat2)

simdatallg <- data.frame(GROUP=factor(rep(c("A","B"),each=6000)),simdatall)


##### RESHAPE ###########

my_resh <- reshMG(simdatallg,items=2:11,groups=1,correct=rep(3,10),echo=FALSE)
  
##### DDF ###########

my_ddf <- DDF(my_resh)


##### PLOTS ###########

plot(my_ddf)
plot(my_ddf, type="allin1")
plot(my_ddf, type="catwise")