classical: Classical Item Analysis
In wasabi1989/MiscPsycho: Miscellaneous Psychometric Analyses

Description Usage Arguments Author(s) Examples

Provides p-values, standard errors of p-values, and point-biserial correlations for multiple choice test items. The standard errors can be design-consistent to reflect the sampling design

classical(...)
## Default S3 method:
classical(data, designSE = FALSE, group, na.rm = TRUE, use = 'everything', ...)
## S3 method for class 'formula'
classical(formula, data, na.action, subset, designSE = FALSE, group, na.rm = TRUE, use = 'everything', ...)

`formula`	an object of class `"formula"` (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under `Details`.
`data`	an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `classical` is called.
`na.action`	a function which indicates what should happen when the data contain `NA`s. Defaults to `getOption("na.action")`.
`na.rm`	logical. Should missing values be removed?
`use`	See `cor` function
`subset`	an optional vector specifying a subset of observations to be used.
`designSE`	logical. If `TRUE` the function returns design-consistent standard errors to capture clustering in the population
`group`	the grouping variable. Used only if designSE = TRUE
`...`	not implemented

Harold C. Doran

xx <- simRasch(200,10)
itemDat <- xx$dat
itemDat$group <- gl(10,20)
(aa <- classical(~V1+V2+V3+V4+V5+V6+V7+V8+V9+V10, data = itemDat, design = TRUE, group = group))
summary(aa)
classical(itemDat[,1:10])