Description Usage Arguments Value Examples
This function computes attributes' importance. First I prepare the list conjoint.results, after that I compute the sample's mean. It uses sum zero contrasts to estimate part worths; ratings is a data frame of clients (rows) by bundles rated (colums); bundles is a data frame of bundles (rows) by attributes (colums); design.l is a list with the conjoint design (attributes and levels) Frist we prepare data
1 | importance.of.attributes(ratings, bundles, design.l, rank = 0)
|
ratings |
a data frame with all clients' ratings |
bundles |
a data frame with all product profiles rated by clients |
design.l |
a list with attributs and their levels |
rank |
if rank==1, then transform a ranking into utilities |
conjoint.list a list with three data frames: 1) fit, with the model fited for each individual, 2) part.worths, with the estimated part worths for all individuals 3) imp, wiht the attributes' importance for each individual
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | data(MDSConjointData)
names(MDSConjointData)
names(MDSConjointData$osc)
osc<-MDSConjointData$osc
osc.imp<-importance.of.attributes(osc$ratings, osc$bundles, osc$design)
names(osc.imp)
head(osc.imp$fit)
head(osc.imp$part.worths)
head(osc.imp$imp)
names(MDSConjointData$tire)
tire<-MDSConjointData$tire
tire.imp<-importance.of.attributes(tire$ratings, tire$bundles, tire$design)
names(tire.imp)
head(tire.imp$fit)
head(tire.imp$part.worths)
head(tire.imp$imp)
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