DDplot: Graphical representation of difficulty and (generalized)...
In kitdouble/ShinyIRT: Test and Item Analysis via Shiny

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

Plots difficulty and (generalized) discrimination for items ordered by difficulty.

1	DDplot(data, item.names, k = 3, l = 1, u = 3)

`data`	numeric: binary data matrix or data frame. See Details.
`item.names`	character: the names of items.
`k`	numeric: number of groups to which may be data.frame x divided by the total score. Default value is 3. See Details.
`l`	numeric: lower group. Default value is 1. See Details.
`u`	numeric: upper group. Default value is 3. See Details.

The data is a matrix or data frame whose rows represents examinee answers ("1" correct, "0" incorrect) and columns correspond to the items. The item.names argument stands for names of items. If not specified, the names of dataset columns are used. Generalized discrimination is computed as follows: The function takes data on individuals, computes their total test score and then divides individuals into k groups. The lower and upper group are determined by l and u parameters, i.e. l-th and u-th group where the ordering is defined by increasing total score.

Generalized discrimination is calculated by gDiscrim function, generalized version of discrim function in psychometric package.

Adela Drabinova
Institute of Computer Science, The Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
drabinova@cs.cas.cz

Lubos Stepanek
First Faculty of Medicine, Charles University
lubomir.stepanek@lf1.cuni.cz

Patricia Martinkova
Institute of Computer Science, The Czech Academy of Sciences
martinkova@cs.cas.cz

Martinkova, P., Stepanek, L., Drabinova, A., Houdek, J., Vejrazka, M., & Stuka, C. (2017). Semi-real-time analyses of item characteristics for medical school admission tests. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems.

gDiscrim, discrim

## Not run: 
# loading 100-item medical admission test data set
data(dataMedical)
data <- dataMedical[, 1:100]

# Difficulty/Discrimination plot of dataMedical data set
DDplot(data)

# Difficulty/Discrimination plot of dataMedical data set
# discrimination based on 5 groups, comparing 4th and 5th
DDplot(data, k = 5, l = 4, u = 5)

## End(Not run)