I don't see anything obviously wrong, but I seem to remember that SPSS uses equal prior probabilities as the default whereas lda() uses "the class proportions for the training set." That might explain the difference.
---------------------------------------------- David L Carlson Associate Professor of Anthropology Texas A&M University College Station, TX 77843-4352 > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-bounces@r- > project.org] On Behalf Of Roberto > Sent: Wednesday, August 01, 2012 5:20 PM > To: r-help@r-project.org > Subject: [R] Different results between lda(mass) and spss discriminant > analysis > > Hi all, > I obtained a strage result with LDA (MASS) function in R with NIR data. > I tried both CV (leave one out cross validation) and splitting my data > in > odd (training) and even (prediction) sets. > In all the cases the minimum error was near to 0. > > Due to the strange result, I tried with SPSS IBM software and it give > me > around 11% of minimum error with and without leave one out cross > validation. > > Maybe the problem is a my error in my script? > > Someone can check it pls? > > data <- "data\\raw_data.csv" > > r <- read.csv(data, header = T) > sound <- r[1:844,] > unsound <- r[845:2195,] > > even_s <- seq(nrow(sound)) %% 2 > even_u <- seq(nrow(unsound)) %% 2 > t <- rbind(sound[even_s == 1,], unsound[even_u == 1,]) > p <- rbind(sound[even_s != 1,], unsound[even_u != 1,]) > > fit <- lda(samples ~., data = t) > ct <- table(p[, 1], predict(fit, p[,-1])$class) > errors <- 1-diag(prop.table(ct, 1)) > min.err <- 1-sum(diag(prop.table(ct))) > > fit_cv <- lda(samples ~., data = r, CV =T) > ct_cv <- table(r[,1], fit_cv$class) > errors_cv <- 1-diag(prop.table(ct_cv, 1)) > min.err_cv <- 1-sum(diag(prop.table(ct_cv))) > > thank you for your help! > > Best regards, > Roberto > > > > -- > View this message in context: http://r.789695.n4.nabble.com/Different- > results-between-lda-mass-and-spss-discriminant-analysis-tp4638763.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. ______________________________________________ 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.