dear experts, I reproduced an experiment (questionnaire) some times. The result of the experiment is a vector of 5 factors, say (A,B,C,D,E). In the original article the result is given in 5 pairs of mean and stDev for A .. E, e.g. mean_A=37.4 and sd_A=8.1. The interval for A,B,C,D,E values is 0..50. The original data frame is not available.
For a comparison of my results L=(A',B',C',D',E') with the original G=(A,B,C,D,E) we can interpret that smaller sd-values are 'better'. But for the means the interpretation is a little bit complicated: a smaller mean value of A or B or E is 'better', but a bigger mean value for C or D is 'better'. To construct a quantified value of being 'better' and to rank my data L vs. the data G, I wrote a kind of an signed distance-function. Here is my simple code and an small example run: R version 2.7.1 (2008-06-23) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 > L<-c(32.8,5.3, 26.3,9.0, 35.1,6.2, 33.4,6.3, 22.9,12.9) > G<-c(37.4,8.1, 30.6,9.7, 32.0,7.9, 29.7, 9.0, 17.1,10.8) > sigdist<- function (L,G) sqrt( sign( G[1]-L[1] )*(G[1]-L[1])^2 + sign( G[2]-L[2] )*(G[2]-L[2])^2 + sign( G[3]-L[3] )*(G[3]-L[3])^2 + sign( G[4]-L[4] )*(G[4]-L[4])^2 - sign( G[5]-L[5] )*(G[5]-L[5])^2 + sign( G[6]-L[6])*(G[6]-L[6])^2 - sign( G[7]-L[7] )*(G[7]-L[7])^2 + sign( G[8]-L[8] )*(G[8]-L[8])^2 + sign( G[9]-L[9] )*(G[9]-L[9])^2 + sign( G[10]-L[10] )*(G[10]-L[10])^2 ) > sigdist(L,G) [1] 6.588627 I like to interpret the positive value 6.588 that 'the L vector is better then G vector w.r.t. sigdist'. My questions are: 1. are there build-in functions in R calculating some (distance?)value with the possibility of a similar interpretation? 2. are there other ideas for a ranking of the experimental results L and G? Any comments, critique or hints are very welcome. Sincerely Wolfgang ______________________________________________ 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.