"PO SU" <rhelpmaill...@163.com> writes: > suppose data has 20 columns > traindata <- data[ 1:10, 1:10] > testdata <- data[11:15,1:10] > pls.fit <- plsr(y~x, ncomp = 5, data = traindata, method= "simpls", scale = > FALSE, model = TRUE, validation = "CV") > ok, i get some result, the srange thing happens when i redo the plsr, i mean, > i use > > traindata <- data[ 1:10, 1:20] > testdata <- data[11:15,1:20] > pls.fit <- plsr(y~x, ncomp = 5, data = traindata, method= "simpls", scale = > FALSE, model = TRUE, validation = "CV") > > > I get the same result as the first one!!!
The reason is probably that you ask plsr() to use the coloumn of traindata called "x" as the predictor. Then it will only use that coloumn, no matter how many coloumns traindata contains. The usual way of using plsr() is to have a data.frame with a _matrix_ as the predictor "coloumn", for instance like this: mydata <- data.frame(y = some_vector, X = I(some_matrix)) mymodel <- plsr(y ~ X, ..., data = mydata) If you want to have the predictors as separate vectors, you must name all of them in the formula (y ~ x1 + x2 + x3 + ...), or you can use the following shortcut to regress y on all the remaining coloumns: plsr(y ~ ., ..., data = mydata) -- Regards, Bjørn-Helge Mevik ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
