similar to: Re gression with Intersection Constraint

Hi, I am working with CART regression now to predict a probability; the response is binary. Could anyone tell me in which cases it is better to use mean square error for splitting nodes and when mean absolute error should be preferred. I am now using the default (MSE) version and I can see that the obtained optimal tree is very different from the tree with the least mean absolute error. Thanks in

Hello In STATA there is command which has regression with clustering option. Can anyone tell me what is the command for the same in R for example in STATA its regress Height Weight, cluster(id) Thanks in Advance Regards Sunita -- View this message in context: http://www.nabble.com/Regression---cluster-option-in-STATA-tp25241948p25241948.html Sent from the R help mailing list archive at

Hi everyone! I'm new to R, and I'm stuck on a problem I don't know how to approach. I have calculated a regression in the form of M ~ D + O + S, and I would like to take this regression and test it with other samples, 5 at a time(5 meaning 5 set, each consisting M, D, O, and S of a specific date). I assume I'll need a for loop. Right now, My data of M, D, O, and S are all stored in

Please explain me as what it means and how this analysis can be done using R and which library(ies) are needed. Thanks -- View this message in context: http://www.nabble.com/Regression-using-age-and-Duration-of-disease-as-a-continous-factors-tp24574133p24574133.html Sent from the R help mailing list archive at Nabble.com.

Hi, I'm sure these are basic problems so I apologise in advance for my ignorance. I have a dataset with X, Y, and a Factor with 4 levels. I am trying to figure out how to use lm() to find the r2 value, slope and intercept of an X~Y regression, for each level of the Factor. Is there a way to do this automatically without having to specify different subsets of the factor? my code so far:

i have a series of regressions i need to run where everything is the same except for the dependent variable, e.g.: lm(y1 ~ x1+x2+x3+x4+x5, data=data) lm(y2 ~ x1+x2+x3+x4+x5, data=data) lm(y3 ~ x1+x2+x3+x4+x5, data=data) is it possible to run all these regs with a single command? given that the bulk of the work for linear regressions is inverting a matrix that depends only on the independent

I have a large dataset grouped by a factor and I want to perform a regression on each data subset based on this factor. There are many ways to do this, posted here and elsewhere. I have tried several. However I found one method posted on the R wiki which works exactly as I want, and I like the elegance and simplicity of the solution, but I don't understand how it works. Its all in the formula