similar to: R-beta: leaps() and subsets() functions

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)

R users, I was a bit surprised to find that when I attempted to add a polynomial term to a linear model using either lm or glm as could be done in S resulted in a fit without that term included and without warning(!!), e.g. > lm(response ~ x + x^2, data). As far as I can gather, there is no poly() yet in R, and if lm/glm do not allow functions of variables as their formula arguements, is

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

In the version of R that I am running I have found two advertised features that don't appear to work correctly: Version 0.61.0 Alpha (December 21, 1997) (installed using r-base-0.61.0-2.i386.rpm) 1. mtext adj parameter is seriously offset to the right. For example: > par(oma=c(0,0,3,0)) > mtext("Try this", side=3, outer=T) # nothing is observed on x11 > mtext("Now

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

R-users, I would like to take advantage of R's mathematical typesetting capabilities in the graphical environment using text(..., expression(...)), however, I can't figure out how to include in the expression some evaluated function. For example, > x <- 1:8 > plot(1:10, 1:10) > text(5, 5, expression(sum(x[i], i == 1, length(x))) # would like "8"

R-users/developers, I am loosing tick marks that I add to a plot with axis() when plotting to the postscript device. They show up fine on the x11 device, and work with the postscript device when the default tickmarks are used (those added by plot()). I am using R-62.1 from the RedHat5.0 rpm's on i86 Linux. Do later snapshots correct this, or is this a running bug in R awaiting the

R-users/developers, I am loosing tick marks that I add to a plot with axis() when plotting to the postscript device. They show up fine on the x11 device, and work with the postscript device when the default tickmarks are used (those added by plot()). I am using R-62.1 from the RedHat5.0 rpm's on i86 Linux. Do later snapshots correct this, or is this a running bug in R awaiting the

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:

Hi, I’d like to fit one GLM model for each possible combination of inputs (i.e. exhaustive search). The package leaps can help me to generate all possible variable subsets, but I’ll appreciate your guidance as of how to generate one model for each of those possible subsets. I’m new in R! Thanks in advance for your help. library(leaps) subsets <- regsubsets( y ~ . , data=freeny, nbest=100,

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 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 +

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 =

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,

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 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

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 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:

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),

R version: 2.10.0 platform: i486-pc-linux-gnu I'm trying to perform model selection from a data.frame object (creatively named "data") using the leaps function, and I run across the following error: > leaps(data[,3:7], data[,1], nbest = 10) Error in leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = NCOL(x) + int, : character variables must be duplicated in .C/.Fortran Here is