similar to: PRESS criterion in leaps

Hello, I am using the 'regsubsets' function (from leaps package) to get the best linear models to explain 1 variable from 1 to 5 explanatory variables (exhaustive search). Is there anyone who can tell me on which criterion is based the 'regsubsets' function ? Thank you. samuel Samuel BERTRAND Doctorant Laboratoire de Biomecanique LBM - ENSAM - CNRS UMR 8005

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 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

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

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

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)

Hi, I'd like to run regsubsets for model selection by exhaustive search. I have a list with 20 potential explanatory variables, which represent the real and the imaginary parts of 10 "kinds" of complex numbers: x <- list(r1=r1, r2=r2, r3=r3, ..., r10=r10, i1=i1, i2=i2, i3=i3, ..., i10=i10) Is there an easy way to constrain the model search so that "r"s and

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

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, does anyone know if there is a way to easily extract p-values from the regsubsets() function? Thanks, James Stegen p.s. this is a reposting due to me not putting in a useful subject heading... -- James C. Stegen NSF Postdoctoral Fellow in Bioinformatics University of North Carolina Chapel Hill, NC 919-962-8795 stegen at email.unc.edu http://www.unc.edu/~stegen/index.html

Hello List, I was wondering if a package or piece of code exists that will allow all possible subsets regression model selection within program R. I have already looked at step(AIC) which does not test differing combinations of variables within a model as far as I can tell. In addition I tried to use the leaps command, but that does not use the criterion I am looking for. Any help or advice

Hi, I'm new to R and this mailing list, so I will attempt to state my question as appropriately as possible. I am running R version 2.7 with Windows XP and have recently been exploring the use of the function regsubsets in the leaps package in order to perform all-subsets regression. So, I'm calling the function as:

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, I have encountered a problem which may be arising from details of my data and or the statistics I am trying to do, or may be arising due to the way leaps works internally. Unfortunately, I am not yet savvy enough to tell why. I can say that this statement works (or at least works to the degree I expect): b <- regsubsets(FUND~.,data=all, intercept=TRUE, nbest=1, nvmax=8, really.big=T,

Dear R-help, I have used the regsubsets function from the leaps package to do subset selection of a logistic regression model with 6 independent variables and all possible ^2 interactions. As I want to get information about the statistics behind the selection output, I?ve intensively searched the mailing list to find answers to following questions: 1. What should I do to get the statistics

Hello, I have a question about doing ALL possible subsets regression with a general linear model. My goal is to produce cumulative Akaike weights for each of 7 predictor variables-to obtain this I need R to: 1. Show me ALL possible subsets, not just the best possible subsets 2. Give me an AIC value for each model (instead of a BIC value). I have tried to do this in library(RcmdrPlugin.HH),

Hello, I am pretty new to R, I have always used SAS and SAS products. My target variable is binary ('Y' and 'N') and i have about 14 predictor variables. My goal is to compare different variable selection methods like Forward, Backward, All possible subsests. I am using misclassification rate to pick the winner method. This is what i have as of now, Reg <- glm (Graduation ~.,

I would like to run a loop in R. I have never done this before, so I would be very grateful for your help ! 1. I have a sample set: 25 objects. I would like to draw 1 object from it and use it as a test set for my future external validation. The remaining 24 objects I would like to use as a training set (to select a model). I would like to repeat this process until all 25 objects are used as a

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:

Colleagues, please help me with the simple question. How can I find R2 and p while doing best subsets regression? Also how can I see B and p for coefficients? Maybe you can advice me detailed manual, because in manual for the package this is not described. Thank you