similar to: Singularity in a regression?

Dear list: I am interested in the following sort of problem, as is found frequently in the field of QSAR. I have biological activity as a function of chemical structure, with structure defined in a categorical manner in that the SUBSTITUENT is the levels of the POSITION factor. For example, data from Kubinyi (http://www.kubinyi.de/dd-12.pdf) for this type of analysis is presented as follows:

Dear [R] Users, I have implemented a linear model with this syntax: model<- lm (var_dependent ~ var_indipendent + factor + var_indipendent : factor, dataframe) anova (model) Response: var_dependent Df Sum Sq Mean Sq F value Pr(>F) var_indipendent 1 20.5522 20.5522 87.8701 1.167e-14 *** factor 1 0.1060

Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

Hi I have tryed to do the meta-regression in metafor package, but I would like to get the standardized coefficients for each variable, however in command:   Ø  res<-rma.uni (yi, vi, method="REML", mods=~cota+DL+uso+gadiente+idade, data= turbidez)   I just have the coefficients no standardized (estimate) of the multiple regression. What I need to do? Thanks Fernanda Melo

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation

Hi all, I have updated my GSoS proposal: "FastPolly: Reducing LLVM-Polly Compiling overhead" (https://gist.github.com/tanstar/5441808). I think the pass ordering problem you discussed early can be also investigated in this project! Is there any comment or advice about my proposal? I appreciate all your help and advice. Thanks, Star Tan Proposal:

On 04/26/2013 05:08 AM, tanmx_star wrote: > Hi all, Hi, thanks for the update and sorry for the delay in reviewing. I just had a look at your proposal. > I have updated my GSoS proposal: "FastPolly: Reducing LLVM-Polly Compiling overhead" (https://gist.github.com/tanstar/5441808). I think the pass ordering problem you discussed early can be also investigated in this project!