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.
