ds_mcf: Forced multiple choice data analysis
In dualScale: Dual Scaling Analysis of Data

ds_mcf

R Documentation

Forced multiple choice data analysis

Description

Forced multiple choice data analysis

Usage

ds_mcf(input, crit, solutions = NULL, mode = c("rad", "act"))

Arguments

`input`	A data set with valid data
`crit`	Used to determine a criterion item for forced classification analysis
`solutions`	Optional argument. A number of intended solutions
`mode`	Correction mode to incorrect data.

Details

There are three types of outputs: Forced classification of the criterion item (type A); dual scaling of non-criterion items by ignoring the criterion item (type B); dual scaling of non-criterion items after eliminating the influence of the criterion item (type C). These three types correspond to, respectively, dual scaling of data projected onto the subspace of the criterion item, dual scaling of non-criterion items, and dual scaling of data in the complementary space of the criterion item.

Value

`call`	Call with all of the specified arguments are specified by their full names
`orig_data`	Initial data
`crit_item`	The criterion item for forced classification
`item_op_lbl`	Item options labels
`sub_lbl`	Subjects options labels
`solutions_mcf`	Maximum possible solutions for forced multiple choice
`solutions_mc`	Maximum possible solutions for multiple choice
`info_x`	Distribution of component information according to output
`out_x`	Results obtained according to output
`item_stat_x`	Item statistics according to output (Not type C)
`rij_x`	Inter item correlation according to output (Not type C)
`proj_opt_x`	Projected option weights according to output
`proj_sub_x`	Projected subject scores according to output
`norm_opt_x`	Normed option weights according to output
`norm_sub_x`	Normed subject scores according to output
`match_missmatch`	Match-mismatch tables
`predict`	Percentage of correct classification
`comp_cont`	Component contamination
`tot_cont`	Total contamination