[R] Different results between lda(mass) and spss discriminant analysis

[David L Carlson]

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-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Roberto
> Sent: Wednesday, August 01, 2012 5:20 PM
> To: r-help at 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
> 
> 
> 
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> results-between-lda-mass-and-spss-discriminant-analysis-tp4638763.html
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