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Hopefully simple question: What is the best way to name, and treat factor columns for data that has lots of columns? This is my column list: id pID50 D.1 D.2 D.3 D.4 D.5 , etc. all the way to D.185 I was under the impression from several R examples in pls that if you name your columns like above, you should be able to simply call all the D factors with "D", instead of going in and

Hello, I am having some trouble using a model I created from plsr (of train) to analyze each invididual R^2 of the 10 components against the test data. For example: mice1 <- plsr(response ~factors, ncomp=10 data=MiceTrain) R2(mice1) ##this provides the correct R2 for the Train data for 10 components ## Now my next objective is to calculate my model's R2 for each component on the

For example: When I take a sample of 5 from 10 columns, I want to exclude from the sample column 1 from being chosen. How do I do this? -- View this message in context: http://r.789695.n4.nabble.com/Want-to-exclude-a-column-when-using-sample-function-tp2287988p2287988.html Sent from the R help mailing list archive at Nabble.com.

e.g. I have a big data set called "combined", and then a small sample of "combined" called "miceSample". I wish to delete "miceSample" from "combined" to create a new smaller data set and store it into a new object. combined <- rbind(scaleMiceTrain, scaleMiceTest) miceSample <- sample(combined[,-c(1,2)],nvars, replace=FALSE) How do I do

iterations <- 100 nvars <- 4 combined <- rbind(scaleMiceTrain, scaleMiceTest) reducedSample <- combined reducedSample <- subset(reducedSample, select = -pID50) reducedSample <- subset(reducedSample, select = -id) for (i in 1:iterations) { miceSample <- sample(combined[,-c(1,2)],nvars, replace=FALSE) miceSample$pID50 <- combined$pID50 miceTestSample <-