Function to generate empirical unidimensional item and test plots

Source: R/empirical_plot.R

Given a dataset containing item responses this function will construct empirical graphics using the observed responses to each item conditioned on the total score. When individual item plots are requested then the total score will be formed without the item of interest (i.e., the total score without that item).

empirical_plot(
  data,
  which.items = NULL,
  type = "prop",
  smooth = FALSE,
  formula = resp ~ s(TS, k = 5),
  main = NULL,
  par.strip.text = list(cex = 0.7),
  par.settings = list(strip.background = list(col = "#9ECAE1"), strip.border = list(col =
    "black")),
  auto.key = list(space = "right", points = FALSE, lines = TRUE),
  ...
)

Arguments

data

a data.frame or matrix of item responses (see mirt for typical input)

which.items

a numeric vector indicating which items to plot in a faceted image plot. If NULL then empirical test plots will be constructed instead

type

character vector specifying type of plot to draw. When which.item is NULL can be 'prop' (default) or 'hist', otherwise can be 'prop' (default) or 'boxplot'

smooth

logical; include a GAM smoother instead of the raw proportions? Default is FALSE

formula

formula used for the GAM smoother

main

the main title for the plot. If NULL an internal default will be used

par.strip.text

plotting argument passed to lattice

par.settings

plotting argument passed to lattice

auto.key

plotting argument passed to lattice

...

additional arguments to be passed to lattice and coef()

Details

Note that these types of plots should only be used for unidimensional tests with monotonically increasing item response functions. If monotonicity is not true for all items, however, then these plots may serve as a visual diagnostic tool so long as the majority of items are indeed monotonic.

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06

Examples


# \donttest{

SAT12[SAT12 == 8] <- NA
data <- key2binary(SAT12,
   key = c(1,4,5,2,3,1,2,1,3,1,2,4,2,1,5,3,4,4,1,4,3,3,4,1,3,5,1,3,1,5,4,5))

# test plot
empirical_plot(data)

empirical_plot(data, type = 'hist')

empirical_plot(data, type = 'hist', breaks=20)


# items 1, 2 and 5
empirical_plot(data, c(1, 2, 5))

empirical_plot(data, c(1, 2, 5), smooth = TRUE)

empirical_plot(data, c(1, 2, 5), type = 'boxplot')


# replace weird looking items with unscored versions for diagnostics
empirical_plot(data, 32)

data[,32] <- SAT12[,32]
empirical_plot(data, 32)

empirical_plot(data, 32, smooth = TRUE)


# }