thanks, I have reduce the number of descriptors, and the erroe is none, my major is qsar, but what is the criterion to select descritors, and how many descriptors should be selected, It is a problem, I calculate my descriptors troungh E-dragon, and apply the wonderful package caret,but my result is poor, how can i improve my performance?
Max is an expert in this field I think ,can you give me some suggestion in how can I well learn QSAR and build the perfect models based on nonlinear and linear. Here, only myself do QSAR research study lonely, and I have no some software to calculate descriptors except free ons, I just know e-dragon, have others? and good tools to do QSAR? thank you again. kevin! Max Kuhn wrote: > > Your data set has 217 predictors and 166 samples. If you read the > vignette on feature selection for this package, you'll see that the > default ranking mechanism that it uses for linear models requires a > linear model fit. The note that: > > > prediction from a rank-deficient fit may be misleading > > should tell you something. If it doesn't: the model fit is over > determined and there is no unique solution, so many of the parameter > estimates are NA. > > Either create a modified version of lmFuncs that suits your needs or > remove variables prior to modeling (or try some other method that > doesn't require more samples than predictors, such as the lasso or > elasticnet). > > Max > > On Fri, Jan 1, 2010 at 10:14 PM, bbslover <dlu...@yeah.net> wrote: >> >> I am learning the package "caret", after I do the "rfe" function, I get >> the >> error ,as follows: >> >> Error in `[.data.frame`(x, , retained, drop = FALSE) : >> undefined columns selected >> In addition: Warning message: >> In predict.lm(object, x) : >> prediction from a rank-deficient fit may be misleading >> >> >> I try to that manual example, that is good, my data is wrong. I do not >> know >> what reanson? >> >> my code is : >> >> subsets<-c(1:5,10,15,20,25) >> ctrl<-rfeControl(functions=lmFuncs, method = "cv", >> verbose=FALSE,returnResamp="final") >> lmProfile<-rfe(trainDescr,trainY,sizes=subsets,rfeControl=ctrl) >> >> before it, I have do some pre-process and my data is in the attachment. >> >> Please help me. thank you! >> >> kevin http://n4.nabble.com/file/n996068/trainDescr.txt trainDescr.txt >> http://n4.nabble.com/file/n996068/trainY.txt trainY.txt >> -- >> View this message in context: >> http://n4.nabble.com/Please-help-me-Error-in-data-frame-x-retained-drop-FALSE-undefined-columns-selected-tp996068p996068.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. >> > > > > -- > > Max > > ______________________________________________ > 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. > > -- View this message in context: http://n4.nabble.com/Please-help-me-Error-in-data-frame-x-retained-drop-FALSE-undefined-columns-selected-tp996068p997526.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.