similar to: Feature selection

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

I am a Macintosh computer (MAC OS X Version 10.4.3) user. I would like to run R for structural equation modeling and meta-analysis but am having difficulty using the Rcmdr interface. According to the R commander installation notes for Tiger Macs ­ I need to 1) Install X11.app from Apple Install disks ­ which I have done 2) Install R.app ­ which I have done 3) Install binary package rgl from CRAN

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

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

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

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

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 ~.,

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

Hi, I would like to perform a forward selection procedure on a data set with 6 observations and 10 predictors. I tried to run it with regsubsets (I set nvmax=number of observations) but I keep getting these warning messages: Warning messages: 1: 5 linear dependencies found in: leaps.setup(x, y, wt = weights, nbest = nbest, nvmax = nvmax, 2: nvmax reduced to 5 in: leaps.setup(x, y, wt =

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

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)

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 +

Hola a todas, Quiero realizar un subset selection usando el paquete leaps, entre mis variables explicativas tengo rezagos de las mismas, por tanto tienen datos NA. sin embargo, al tratar de realizar cross validation me pide que los datos esten en formato data.frame con lo que me arroja un error al ejecutar el algoritmo. Mi pregunta es cómo puedo forzar en un data.frame en que tengo variables

Hi all: Newbies (and others!) may find useful the R Reference Card made available by Tom Short and Rpad at http://www.rpad.org/Rpad/Rpad-refcard.pdf or through the "Contributed" link on CRAN (where some other reference cards are also linked). It categorizes and organizes a bunch of R's basic, most used functions so that they can be easily found. For example, paste() is

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:

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

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

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

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),