Description Details Author(s) Examples

The Multiple Conjoint Analysis package changes the meaning and use of traditional holdout cases. Rather than using the holdout cases to check a single design, the "holdouts" are used to create a large set of designs, each of which is analyzed. The average result is used

Package: | MConjoint |

Type: | Package |

Version: | 0.1 |

Date: | 2013-05-14 |

License: | GPL-3 |

The use of the routines centers around something I call a "despack" a design package. A despack contains despack$cards: a list of the m cards for which ranks are obtained; despack$designs: a list of designs each with n cards drawn from the list of m cards; despack$samples, a list of samples of length n, drawn from 1:m, corresponding to the cards used in the design; despack$coeffs: a list of matrices of linear coefficients; despack$all.utils: a list of lists of utility values, on for each column of the coeffs matrices; despack$all.imps, a list of matrices of importances, one column for each utility; despack$utils: a list of utilities (average taken over first index of the list of lists; despack$imps: the average of the list of importance matices

Start with a data set, full.design, with all possible cards. (This may be the full factorial design (all combinations of levels)) or some combinations may be removed.

Obtain a "good" design of n cards (for information on what makes a design good see the documentation for mc.good.desgins). To this you add extra.cards cards in such a way that you maximize the number of subsets of the m=n + extra.cards of length n that lead to "good" designs.

Both operations can be done by calling

`orig.design = mc.get.initial.design(full.design)`

`orig.design$design`

will be the m cards for which you will collect
data

You then obtain your data, `data`

, a matrix with each column corresponding the the ranks
given to the cards by one subject. Then run

`despack = good.designs(orig.design$design)`

This will give an initial despack, with $cards, $samples, and $designs

Fill the other elements of despack by calling

`despack=M.Conjoint(despack,data)`

This will print a summary with the utilites and the importances averaged over the subjects (an operation that may or may not be useful)

William Hughes

Maintainer: William Hughes <William.Hughes@rogers.com>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
# A simple conjoint problem. Managers can make hiring descisions
# based on the factors
# University: Prestige, Excellent, Good; Sex: Male, Female;
# Dress: smart, messy; Hair: long, short.
# We want to determine the importance of these factors.
# We interview two managers. The first picks first by
# University, then by sex, male over female, then
# by dress, smart over messy, and does not care about hair
# length. The second is like the first except that
# this manager picks female over male.
# start with the full factorial design
data(hire.candidates)
#get a questionaire
hire.questionaire = mc.get.initial.design(hire.candidates,max.trials=10)
#collect the data
data(hire.data)
#get a design pack for the analyis
hire.despack=mc.good.designs(hire.questionaire$design, size=20)
#do the conjoint analysis
hire.despack=M.Conjoint(hire.despack,hire.data)
# (note this illustrates the danger of averaging utilities.
# The average utility for both Male and Female is small, but
# Sex is important to both managers)
``` |

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