Function to generate empirical unidimensional item and test plots

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).

Usage

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


if (FALSE) { # \dontrun{

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)

} # }