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Dear members, My name is Seungho Huh. I am a statistician who tries to use the Kernel PLS method in a classification problem. I am sending this email to ask you something about the "kernelpls" function in R (pls.pcr package). I would like to obtain the predicted Y values for test data, using the Kernel PLS method. Let's take the example in the R help: > data(NIR) > attach(NIR) > NIR.kernelpls <- mvr(Xtrain, Ytrain, 1:6, validation = "CV", method=&...

...(LOO and Leave-10-Out) values for each component for my model. I was running into a few problems so I played with the example a little and the results do not match up with the comments in the help pages. $ library(pls) $ data(NIR) $ testing.plsNOCV <- plsr(y ~ X, 6, data = NIR, method="kernelpls", validation="none") $ NIR.plsCV <- plsr(y ~ X, 6, data = NIR, CV=TRUE, method="kernelpls") $ testing.plsCV <- crossval(testing.plsNOCV) $ R2(NIR.plsCV) (Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 0.0000 0.9812 0.9...

...problems in a more `natural' way. HTH, Andy > From: Huh, Seungho > > Dear members, > > My name is Seungho Huh. I am a statistician who tries to use > the Kernel > PLS method in a classification problem. I am sending this email to ask > you something about the "kernelpls" function in R (pls.pcr package). > > I would like to obtain the predicted Y values for test data, using the > Kernel PLS method. Let's take the example in the R help: > > > data(NIR) > > attach(NIR) > > NIR.kernelpls <- mvr(Xtrain, Ytrain, 1:6, validati...

...9; argument. The three functions are all exported from the name space. In the 'pre namespace' era, I took inspiration from lm() and implemented it like this: plsr <- function(..., method = pls.options()$plsralg) { cl <- match.call() cl$method <- match.arg(method, c("kernelpls", "widekernelpls", "simpls", "oscorespls", "model.frame")) cl[[1]] <- as.name("mvr") res <- eval(cl, parent.frame()) ... Recently, Prof. Brian Ripley kindly pointed out that this doesn...

...need help with a partial least square regression in R. I have read both the vignette and the post on R bloggers but it is hard to figure out how to do it. Here is the script I wrote: library(pls) plsrcue<- plsr(cue~fb+cn+n+ph+fung+bact+resp, data = cue, ncomp=7, na.action = NULL, method = "kernelpls", scale=FALSE, validation = "LOO", model = TRUE, x = FALSE, y = FALSE) summary(plsrcue) and I got this output, where I think I can choose the number of components based on RMSEP, but how do I choose it? Data: X dimension: 33 7 Y dimension: 33 1 Fit method: kernelpls Number of com...

Error in mvr(Kd_nM ~ qsar, ncomp = 6, data = my, validation = "CV", method = "kernelpls") :   Invalid number of components, ncomp How I can fix this? [[alternative HTML version deleted]]

...english in plain text. Here is my subroutine and data: --------------------------------------------------- vmat<-as.matrix(read.table("vmat")) Xm<-vmat[1:22,1:20] hemd<-read.table("threehem",header=T) Ym<-as.matrix(hemd[,2]) gvdw.pls<-plsr(Ym ~ Xm,6,method="kernelpls") rsltv<-predict(gvdw.pls,comps=6) plot(Ym,rsltv,type="n",xlab="Actividad + Biol??gica",xlim=c(4.6,6),ylim=c(4.8,6),ylab=" Act. + Biol.(Pred.)",main="QSAR Ligaci??n de Derivados de la Artemisina con + Hemina",sub="Descriptores de Coeficientes...

Version 1.2-1 of the pls package is now available on CRAN. This is mainly a bugfix-release. If you fit multi-response models, you are strongly engouraged to upgrade! The main changes since 1.2-0 are - Fixed bug in kernelpls.fit() that resulted in incorrect results when fitting mulitresponse models with fewer responses than predictors - Changed default radii in corrplot() - It is now possible to select the radii of the circles in corrplot See the file CHANGES in the sources for all changes. The pls package implem...

Hello, I have a data set with 15 variables (first one is the response) and 1200 observations. Now I use pls package to do the plsr as below. trainSet = as.data.frame(scale(trainSet, center = T, scale = T)) trainSet.plsr = mvr(formula, ncomp = 14, data = trainSet, method = "kernelpls", model = TRUE, x = TRUE, y = TRUE) from the model, I wish to know the values of Xvar (the amount of X-variance explained by each number of components) and Xtotvar (total variance in X). Because the trainSet has been scaled before training, I think Xtotvar should...

Version 1.2-1 of the pls package is now available on CRAN. This is mainly a bugfix-release. If you fit multi-response models, you are strongly engouraged to upgrade! The main changes since 1.2-0 are - Fixed bug in kernelpls.fit() that resulted in incorrect results when fitting mulitresponse models with fewer responses than predictors - Changed default radii in corrplot() - It is now possible to select the radii of the circles in corrplot See the file CHANGES in the sources for all changes. The pls package implem...

Hi I'm performing a PLS This is my data present in a file Year Y X2 X3 X4 X5 X6 1960 27.8 397.5 42.2 50.7 78.3 65.8 1960 29.9 413.3 38.1 52 79.2 66.9 1961 29.8 439.2 40.3 54 79.2 67.8 1961 30.8 459.7 39.5 55.3 79.2 69.6 1962 31.2 492.9 37.3 54.7 77.4 68.7 My R-code Data <- read.csv("C:/TestData.csv") variable=names(Data)[4:8] dataset=NULL dataset$X=NULL len=length(variable)

...id type (list) for variable 'h' I can get the plsr function to work with scalars for both predictor and response. Can anyone tell me where I have gone wrong on the pls input? apls <- plsr(w ~ h, data = pls) summary(apls) Data: X dimension: 10000 1 Y dimension: 10000 1 Fit method: kernelpls Number of components considered: 1 TRAINING: % variance explained 1 comps X 100.000 w 2.293 -- View this message in context: http://r.789695.n4.nabble.com/Dataframes-in-PLS-package-tp4405798p4405798.html Sent from the R help mailing list archive at Nabble.com.