similar to: where to get the "leaps" package

Hello I am trying to use all subsets regression on a test dataset consisting of 11 trails and 46 potential predictor variables. I would like to use Mallow's Cp as a selection criterion. The leaps function would provide the required output but does not work with this many variables (see below). The alternative function regsubsets should be used, but I am not able to define the function in

Hi In trying to fit a linear model , I use the leaps() function to determine wich predictors I should include in my model. I would like to plot the Mallow's Cp criteria against p with the indexes of selected model variates as points labels Is there already such a function? (I could not find it) Thanks Anne [[alternative HTML version deleted]]

Dear List members I am using the regsubsets function to select a few predictor variables using Mallow's Cp: > sel.proc.regsub.full <- regsubsets(CO2 ~ v + log(v) + v.max + sd.v + tad + no.stops.km + av.stop.T + a + sd.a + a.max + d + sd.d + d.max + RPA + P + perc.stop.T + perc.a.T + perc.d.T + RPS + RPSS + sd.P.acc + P.dec + da.acc.1 + RMSACC + RDI + RPSI + P.acc + cov.v + cov.a +

Hello R users, I have three questions and I would be grateful if someone could give me an answer to each of these. 1) I have constracted a function that returns an output, which runs in a while( condition ){ run function } loop. I would like to know if there is a way to get the outputs in different windows, every time the function runs, so as to compare easier the results. 2) In my

Hello dear R-help members, I recently became interested in using biglm with leaps, and found myself somewhat confused as to how to use the two together, in different settings. I couldn't find any example codes for the leaps() package (except for in the help file, and the examples there are not as rich as they could be). That is why I turn to you in case you could share some good tips and

I am hoping to get some advise on the following: I am looking for an automatic variable selection procedure to reduce the number of potential predictor variables (~ 50) in a multiple regression model. I would be interested to use the forward stepwise regression using the partial F test. I have looked into possible R-functions but could not find this particular approach. There is a function

Hi, I used the bic.surv function, S-PLUS functions developed by Chris Volinsky http://www.research.att.com/~volinsky/bma.html, without problems with S-PLUS. I have to use it with R but I am face with a problem: this function call a fortran routine named "leaps" (answer <- .Fortran("leaps", arguments)). I loaded the leaps library, and the leaps function work well with my R,

Hello, Newbie here, be gentle ;) I have a reference book that discusses regression model selection using several methods - what they call 'Forward Model Selection' i.e. add one variable at a time and examining R, R^2, Mallow's C-p value, etc., 'Backward Model Selection' i.e. starting out with all the variables included and then remove them one at a time, and examining for

Hi there, This problem has been dogging me for a bit, and I'm trying to figure out why. When running the the subsets function in the leaps library, R is giving me the following error message > lvodsub <- subsets(pred, resp$LVOD) Warning message: XHAUST returned error code -999 in: leaps.exhaustive(a, really.big = really.big) but this still happens if I add the really.big option:

Hullo, I am trying to use the leaps package, & keep getting the following error: Error in leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = NCOL(x) + int, : y and x different lengths My data set is attached below. I am rather new to R, & would appreciate any help that could be given. Thanks. http://n4.nabble.com/file/n1755941/lr04.txt lr04.txt -- View this message in context:

Dear UseRs, I wanted to know that i have been using "leaps" for the proper models selection for my work. I read so many articles from internet which categorically outlined that "leaps" command for model selection should never be used as its not efficient. Moreover, as a matter of fact, i personally noticed that the accuracy of "leaps" package is very much

Hi Folks, I've got a question concerning the calculation of the Schwarz-Criterion (BIC) done by summary.regsubsets() of the leaps-package: Using regsubsets() to perform subset-selection I receive an regsubsets object that can be summarized by summary.regsubsets(). After this operation the resulting summary contains a vector of BIC-values representing models of size i=1,...,K. My problem

I'm interested in writing some model selection functions (for linear regression models, as a start), which incorporate the PRESS criterion since it, to my knowledge, is not currently implemented in any available model selection procedure. I thought it would be simplest to build on already existing functions like regsubsets in package leaps. It's easy enough to calculate the PRESS

I am generating a bunch of examples for my students and when I type R BATCH demo10.R (file demo10.R reproduced below) I get an error: ------------- Error message ------ > plot(mods$size,mods$Cp, + main="Cp versus talla modelos", + xlab=expression(p), + ylab=expression(C[p])) Error in plot.window(xlim, ylim, log, asp, ...) : need finite ylim values In

Hi, there, S+ has a function leaps which can be used for criterion based model selection. Is there an equivalent function in R? thanks in advance. Kenny Ye Assistant Professor Department of Applied Math and Statistics SUNY at Stony Brook Stony Brook, New York 11794-3600 (516)632 9344 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi i need to create a model from 250 + variables with high collinearity, and only 17 data points (p = 250, n = 750). I would prefer to use Cp, AIC, and/or BIC to narrow down the number of variables, and then use VIF to choose a model without collinearity (if possible). I realize that having a huge p and small n is going to give me extreme linear dependency problems, but I *think* these model

Dear friends. On the Bayesian averaging homepage http://www.research.att.com/~volinsky/bma.html I found some S code some of which perhaps may run in R. There was a call to an algorithm possibly within S but not supported by R 64.1: "leaps and bounds". I guess it is a minimization step. Can anyone clarify the algorithm and perhaps even give a pointer to some code ? I guess this may be

Hi all I am hoping this is just a minor problem, I am trying to implement a best subsets regression procedure on some ecological datasets using the regsubsets function in the leaps package. The dataset contains 43 predictor variables plus the response (logcount) all in a dataframe called environment. I am implementing it as follows: library(leaps)

Is there any plan to compile leaps library for windows? thanks. Kenny Kenny Ye Assistant Professor Department of Applied Math and Statistics SUNY at Stony Brook Stony Brook, New York 11794-3600 Phone (631)632-9344 Fax (631)632-8490 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

Hi, I'm using regsubsets from the leaps package to select subsets of variables. I'm calling the function as lp <- regsubsets(x,y,nbest=5,nvmax=9) Then I call plot to see which variables turned up in the models. I use the R^2 scale and see my best model had a R^2 of 0.62. However when I make a linear model using lm() with the same x my R^2 is 0.45. Should'nt I be seeing the