[R] plot discriminant analysis
I'm confused on how is the right way to plot a discriminant analysis made by lda function (MASS package). (I had attached my data fro reproduction). When I plot a lda object : X - read.table(data, header=T) lda_analysis - lda(formula(X), data=X) plot(lda_analysis) #the above plot is completely different to: plot(predict(lda_analysis)$x, col=palette()[predict(lda_analysis)$class]) that should be the same graph than the first? In the second case, I use predict function to obtain the LD1 and LD2 coordinates of lda_analysis (predict(lda_analysis)$x) and it's respective class (predict(lda_analysis)$class), but it seems that the classes are different: table(X$G3, predict(lda_analysis)$class) BG M B 2903 G0 26 2 M 40 46 any clues? Regards, __ 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.
Re: [R] plot discriminant analysis
Hi, I did it with Iris - data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp = rep(c(s,c,v), rep(50,3))) train - sample(1:150, 75) table(Iris$Sp[train]) z - lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train) Then I did plot(z,xlim=c(-10,10),ylim=c(-10,10)) before drawing points(predict(z)$x, col=palette()[predict(z)$class],xlim=c(-10,10),ylim=c(-10,10)) and all the points are superimposed. The only difference I found was the different x- and y-axis when I drew them separately, i.e. plot(z) plot(predict(z)$x, col=palette()[predict(z)$class]) Alain Alejo C.S. wrote: I'm confused on how is the right way to plot a discriminant analysis made by lda function (MASS package). (I had attached my data fro reproduction). When I plot a lda object : X - read.table(data, header=T) lda_analysis - lda(formula(X), data=X) plot(lda_analysis) #the above plot is completely different to: plot(predict(lda_analysis)$x, col=palette()[predict(lda_analysis)$class]) that should be the same graph than the first? In the second case, I use predict function to obtain the LD1 and LD2 coordinates of lda_analysis (predict(lda_analysis)$x) and it's respective class (predict(lda_analysis)$class), but it seems that the classes are different: table(X$G3, predict(lda_analysis)$class) BG M B 2903 G0 26 2 M 40 46 any clues? Regards, __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- Alain Guillet Statistician and Computer Scientist SMCS - Institut de statistique - Université catholique de Louvain Bureau c.316 Voie du Roman Pays, 20 B-1348 Louvain-la-Neuve Belgium tel: +32 10 47 30 50 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] plot discriminant analysis
Hi Alain, thanks for the fast response. I've the same results with iris data, but when I use my data (mentioned in the first message), I have different results. Regards, Alejo 2009/10/14 Alain Guillet alain.guil...@uclouvain.be Hi, I did it with Iris - data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp = rep(c(s,c,v), rep(50,3))) train - sample(1:150, 75) table(Iris$Sp[train]) z - lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train) Then I did plot(z,xlim=c(-10,10),ylim=c(-10,10)) before drawing points(predict(z)$x, col=palette()[predict(z)$class],xlim=c(-10,10),ylim=c(-10,10)) and all the points are superimposed. The only difference I found was the different x- and y-axis when I drew them separately, i.e. plot(z) plot(predict(z)$x, col=palette()[predict(z)$class]) Alain Alejo C.S. wrote: I'm confused on how is the right way to plot a discriminant analysis made by lda function (MASS package). (I had attached my data fro reproduction). When I plot a lda object : X - read.table(data, header=T) lda_analysis - lda(formula(X), data=X) plot(lda_analysis) #the above plot is completely different to: plot(predict(lda_analysis)$x, col=palette()[predict(lda_analysis)$class]) that should be the same graph than the first? In the second case, I use predict function to obtain the LD1 and LD2 coordinates of lda_analysis (predict(lda_analysis)$x) and it's respective class (predict(lda_analysis)$class), but it seems that the classes are different: table(X$G3, predict(lda_analysis)$class) BG M B 2903 G0 26 2 M 40 46 any clues? Regards, __ 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. -- Alain Guillet Statistician and Computer Scientist SMCS - Institut de statistique - Université catholique de Louvain Bureau c.316 Voie du Roman Pays, 20 B-1348 Louvain-la-Neuve Belgium tel: +32 10 47 30 50 [[alternative HTML version deleted]] __ 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.
Re: [R] plot discriminant analysis
On Oct 14, 2009, at 12:24 PM, Alejo C.S. wrote: Hi Alain, thanks for the fast response. I've the same results with iris data, but when I use my data (mentioned in the first message), You are apparently under the false impression that the data made it through the listserv. Read the Posting Guide to find out why that impression is false. I have different results. Regards, Alejo 2009/10/14 Alain Guillet alain.guil...@uclouvain.be Hi, I did it with Iris - data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp = rep(c(s,c,v), rep(50,3))) train - sample(1:150, 75) table(Iris$Sp[train]) z - lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train) Then I did plot(z,xlim=c(-10,10),ylim=c(-10,10)) before drawing points(predict(z)$x, col=palette()[predict(z)$class],xlim=c(-10,10),ylim=c(-10,10)) and all the points are superimposed. The only difference I found was the different x- and y-axis when I drew them separately, i.e. plot(z) plot(predict(z)$x, col=palette()[predict(z)$class]) Alain Alejo C.S. wrote: I'm confused on how is the right way to plot a discriminant analysis made by lda function (MASS package). (I had attached my data fro reproduction). When I plot a lda object : X - read.table(data, header=T) lda_analysis - lda(formula(X), data=X) plot(lda_analysis) #the above plot is completely different to: plot(predict(lda_analysis)$x, col=palette()[predict(lda_analysis) $class]) that should be the same graph than the first? In the second case, I use predict function to obtain the LD1 and LD2 coordinates of lda_analysis (predict(lda_analysis)$x) and it's respective class (predict(lda_analysis)$class), but it seems that the classes are different: table(X$G3, predict(lda_analysis)$class) BG M B 2903 G0 26 2 M 40 46 any clues? Regards, __ 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. -- Alain Guillet Statistician and Computer Scientist SMCS - Institut de statistique - Université catholique de Louvain Bureau c.316 Voie du Roman Pays, 20 B-1348 Louvain-la-Neuve Belgium tel: +32 10 47 30 50 [[alternative HTML version deleted]] __ 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. David Winsemius, MD Heritage Laboratories West Hartford, CT __ 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.
