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

Description Usage Arguments Details Value Author(s) Examples

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

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

`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

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.

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

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)