similar to: leaps and biglm

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

Dear R-help This is a follow-up to my previous post here: http://groups.google.com/group/r-help-archive/browse_thread/thread/d9b6f87ce06a9fb7/e9be30a4688f239c?lnk=gst&q=dobomode#e9be30a4688f239c I am working on developing an open-source automated system for running batch-regressions on very large datasets. In my previous post, I posed the question of obtaining VIF's from the output of

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 trying to figure out why 'biglm' can handle large data set... According to the R document - "biglm creates a linear model object that uses only p^2 memory for p variables. It can be updated with more data using update. This allows linear regression on data sets larger than memory." After reading the source code below? I still could not figure out how 'update'

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, 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 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 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 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 all, I am working on a large dataset and need to use biglm() to perform OLS regressions. I have detected significant ARCH effects which I try to account for using the Newey-West correction. So far, I have worked with NeweyWest() in the sandwich package. NeweyWest() however seems to be unable to handle an object of class "biglm". Looking into the code, I figured out that

biglm fits linear and generalized linear models to large data sets, using bounded memory. What's New: generalized linear models. -thomas Thomas Lumley Assoc. Professor, Biostatistics tlumley at u.washington.edu University of Washington, Seattle _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch

biglm fits linear and generalized linear models to large data sets, using bounded memory. What's New: generalized linear models. -thomas Thomas Lumley Assoc. Professor, Biostatistics tlumley at u.washington.edu University of Washington, Seattle _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch

Hi, I'm trying to compile the package biglm, but when I build it with R CMD build biglm, it failed : C:\LOCAL\c-dutang\code\R\biglm2>R CMD build biglm * checking for file 'biglm/DESCRIPTION' ... OK * preparing 'biglm': * checking DESCRIPTION meta-information ...C:/DOCUME~1/c-dutang/Local: Can't op n C:/DOCUME~1/c-dutang/Local: No such file or directory

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

Hi R-list members, Could somebody explain to me the meaning of the '.' in the formula SumTL~. below? I could not find it in the help pages. I'm guessing it is substituted by v1+v2+v3+.. for all independent variables vi. Furthermore, I would like to add interaction effects to the model, is this also possible with the '.'? > library(leaps) >

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

G'morning What does the error message "Error in x %*% coef(object) : non- conformable arguments" indicate when calculating the response values for newdata with a model from bigglm (in package biglm), and how can I debug it? I am attempting to do Monte Carlo simulations, which may explain the loop in the code that follows. After the code I have included the output, which shows that

Thank you very much for the example. I think interactively I could get something. But my obstacle is to write an R script that processes my set of data automatically. My difficulty is to extract the information that appears on the screen, when R is operated interactively, from a scripts. Let me go over some steps to make sure I am doing things right. Assume my data have been read into the matrix

I've been working with a rather large data set (~10M rows), and while biglm works beautifully for generating coefficients, it does not report an r-squared. It does report RSS. Any idea on how one could coax an R-squared out of biglm? Thanks in advance for any help with this! Bryan Lim Lecturer Department of Finance University of Melbourne [[alternative HTML version deleted]]