similar to: Rulefit with R and missing values

Hello LIST, In using Rulefit I;ve bee nabel to fit a model using rulefit without incident. However, when trying to use intnull and interact things go astray. > rf=rulefit(x,"N", cat.vars="H", > not.used=c("G","T"),huber=0.9,path.speed=1); [snip] RuleFit model 2/26/2008 2:17p ave(abs(error)) terms path steps 84.16 110

R version 2.8.1 (2008-12-22) on Linux 64-bit I am trying to run 'rulefit' function (Rule based Learning Ensembles). but I got the following error - > rulefit(x,y) Warning: This program is an suid-root program or is being run by the root user. The full text of the error or warning message cannot be safely formatted in this environment. You may get a more descriptive message by running

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program

I wish to pass a vector ``y'', some of whose entries are NAs to a fortran subroutine which I am dynamically loading and calling by means of .Fortran(). The subroutine runs through the vector entry by entry; obviously I want to have it do one thing if y[i] is present and a different thing if it is missing. The way I am thinking of proceeding is along the xlines of: ymiss <- is.na(y)

Webinar signup: Advances in Gradient Boosting: the Power of Post-Processing December 14, 10-11 a.m., PST Webinar Registration: http://2.salford-systems.com/gradientboosting-and-post-processing/ Course Outline: * Gradient Boosting and Post-Processing: o What is missing from Gradient Boosting? o Why post-processing techniques are used? * Applications Benefiting from

Webinar: Advances in Gradient Boosting: the Power of Post-Processing TOMORROW: December 14, 10-11 a.m., PST Webinar Registration: http://2.salford-systems.com/gradientboosting-and-post-processing/ Course Outline: I. Gradient Boosting and Post-Processing: o What is missing from Gradient Boosting? o Why post-processing techniques are used? II. Applications Benefiting from Post-Processing:

Hello!   I would like to sample 30 % of cases (with at least 1 value lower than 3) and among them I want to set all values lower than 3 (within selected cases) as NA (NMAR- Not missing at random). I managed to sample cases, but I don’t know how to set values (lower than 3) as NA.   R code:   x <-

Hi all, sorry for my english, but I don't speak yours language. I'm trying to use bart() and rbf(). The package I'm using now is "BayesTree" and "neural", respectively. I could create the models, but I can't predict my test data. Does anyone have such an experience? Any advice is appreciated! Thank you in advanced!. Andr? -- View this message in

Hi there, I have a statistics question on a classification problem: Suppose I have 1000 binary variables and one binary dependent variable. I want to find a way similar to PCA, in which I can find a couple of combinations of those variables to discriminate best according to the dependent variable. It is not only for dimension reduction, but more important, for finding best way to construct