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Dear all, I am trying to calculate the fitted values using a ridge model (lm.ridge(), MASS library). Since the predict() does not work for lm.ridge object, I want to get the fitted_value from the coefficients information. The following are the codes I use: fit = lm.ridge(myY~myX,lambda=lamb,scales=F,coef=T) coeff = fit$coef However, it seems that "coeff" (or "fit$coef") is

Dear all, Does anyone knwo how to add error bar to the data point in a simple xyplot? Right now, I have a .dat file including both the data points and their standard deviation and read it in R. I tried function arrows(). But it didn't gave me any arrows. Thanks for the help! Jeny

Dear all, I have a question about how to get a matrix by combining a large number of columns from a data file. Suppose I read a file which have 1000 columns by: test = read.table("dat.txt", header=F) I know I could use "cbind()". It's easy to do when the number of columns is small (i.e. cbind(test$V1, test$V2)). But how about build a matrix "X" by combine the

Dear all, I am trying to my dataframe for the PLS analysis using the PLS package. However I have some trouble generating the correct dataframe. The main problem is how to use one name to represent several columns in the dataframe. The example dataframe in PLS package is called "sensory". I cannot directly read the data file since it's a binary file. If I use

Hello list. I'm trying to rebuild the 2.6.9.67.0.20.EL kernel, but it fails even without modifications. How did I try it? Created a (non-root) build environment (not a mock ) Installed the kernel.scr.rpm and did a rpmbuild -ba --target=`uname -m` kernel-2.6.spec 2> prep-err.log | tee prep-out.log The build failed at the end: Processing files: kernel-xenU-devel-2.6.9-67.0.20.EL Checking

Dear all, I am using different multiple regression models (OLS and principal component regression (PCR)) to make prediction of my test set. And those models come from the same training set, except that the number of variables or descriptors (columns of X) used in OLS is less than those used in PCR. And I use square correlation coefficient (r^2) and root mean square to see the relationship