# mc.good.designs: mc.good.design In MConjoint: Conjoint Analysis through Averaging of Multiple Analyses

## Description

given a set of m cards, find "good" designs with cards rows

## Usage

 ```1 2``` ```mc.good.designs(orig.set, cards = NULL, slack = 1, tol = 0.2, no.replace = TRUE, size = 100, max.trials = 1e+06) ```

## Arguments

 `orig.set` a design of length m `cards` The number of cards in each "good" design found. `slack` How much the number of each factor can vary in a "good" design `tol` The largest cross correlation in a "good" design `no.replace` Sample without replacement: TRUE or FALSE `size` The number of "good" designs to find `max.trials` The maximum number of designs to look at

## Details

The function takes samples with `cards` rows from the `orig.design`. For each sample it checks whether the design is "good". A design is said to be good if it is balanced (for each factor each level occurs about the same number of times, the maximum difference is `slack`) and the different factors are uncorrelated (maximum cross correlation is `tol`). Sampling continues (with or without replacement depending on `no.replace`) until one of `size` good designs are found, all designs have been checked, or `max.trials` designs have been checked. If fewer than `size` design are found then a warning is printed.

## Value

A despack with the following field filled

 `cards` set equal to `orig.set` `samps` a list of samples, the row numbers of the corresponding designs `designs` the good designs found

William Hughes

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```data(hire.questionaire) #default mc.good.designs(hire.questionaire\$design) #look for 7 card designs, with the cross correlation tolerance increased to .3 #mc.good.designs(hire.questionaire\$design,7,tol=.3) ```

MConjoint documentation built on May 1, 2019, 7:56 p.m.