mirt: empirical_plot – R documentation

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empirical_plot

Function to generate empirical unidimensional item and test plots

Description

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,
  smooth = FALSE,
  formula = resp ~ s(TS, k = 5),
  main = NULL,
  par.strip.text = list(cex = 0.7),
  boxplot = FALSE,
  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 a empirical test plot will be constructed instead
`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`
`boxplot`	logical; use a boxplot to display the marginal total score differences instead of scatter plots of proportions? Default is FALSE
`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 should be 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

## Not run: 

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)

#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), boxplot = TRUE)

# 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)


## End(Not run)

mirt

Multidimensional Item Response Theory

v1.33.2

GPL (>= 3)

Authors

Phil Chalmers [aut, cre] (<https://orcid.org/0000-0001-5332-2810>), Joshua Pritikin [ctb], Alexander Robitzsch [ctb], Mateusz Zoltak [ctb], KwonHyun Kim [ctb], Carl F. Falk [ctb], Adam Meade [ctb], Lennart Schneider [ctb], David King [ctb], Chen-Wei Liu [ctb], Ogreden Oguzhan [ctb]

Initial release