Dear R-experts, I would like to find the best variable combination which are maximises the accuracy of a cross validated reclassification. My data consists of 36 samples, equal distributed to 6 groups, and each sample are characterised by 20 variables.
/data<-data.frame(1:36,1:20) group<-(1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6)/ I tried to overcome the problem of variable selection with 1. using functions of the "klaR"-package. 2. hand picking On the one hand, I minimised wilks lambda, implemented the resulting variables into the lda-function, on the other hand, I used stepclass() forward and backward: *BUT compared to my hand picked variable selections, both functions yielded to less prediction!* (One fact, that my hand picks were "better", is the swich of inner-group accuracy by picking new variables which is not accounted in stepclass(). It could happen, that the input of a new variable increases the accuracy of one group and decreases the accuracy of an other group but the inplementation of a second variable could increases the "decreased group" again without negativ effects to other groups.) 1. What algortihms I could also use? 2. Does R offers an algorithm, which selects variables randomly? 3. Is there a function/algorithm for listing all possible variable combinations? Thank you, I´m pleased about every suggestion. Best regards Thomas -- View this message in context: http://r.789695.n4.nabble.com/random-variable-selection-algorithms-for-lda-tp4640267.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
