[R] Variable selection in clusters using 1-R2 ratio

Folks, I am looking for a means for calculating the 1-R^2 ratio for variable selection to mimic the values of PROC VARCLUS in SAS. While there may be better methods for variable selection, we are trying to duplicate published results at this time. To date, I have been unable to find a way to o

[R] variable selection problem

Hello I am trying to run vignette example for the MaxentVariableSelection package, but something wrong is happening. I can't figure out. Here is the code: maxentPath = ("/home/anderson/R/x86_6

Re: [R] variable selection using residual difference

Suggestion: Don't do this! I suggest that you consult with a local statistician or post to a statistical website like stats.stackexchange.com for what might be sensible procedures for variable selection (a complex and controversial topic!) and why what you propose is or is not a good idea (don't

[R] variable selection using residual difference

I have the following example dataset set.seed(2001) n <- 100 Y <- c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0) X1 <- sample(x=c(0,1,2), size

Re: [R] Variable Selection for Logistic Regression

Lasso is an obvious choice by it may also be interesting to look at the variable importance from a random forest model On 17 Dec 2015 17:28, "Manish MAHESHWARI" wrote: > Hi, > > I have a dataset with approx 400K Rows and 900 columns with a single > dependent variable of 0/1 flag. The independent

[R] Variable Selection for Logistic Regression

Hi, I have a dataset with approx 400K Rows and 900 columns with a single dependent variable of 0/1 flag. The independent variables are both categorical and numerical. I have looked as SO/Cross Validated Posts but couldn't get an answer for this. Since I cannot try all possible combinations of

Re: [R] Variable selection from given data

Untested and with the presumption that you have the appropriate package loaded: sapply(1:15, function(x) qr(y ~ ., df[-grep(paste0(“x”,i)]) ) > On Oct 30, 2014, at 11:14 PM, Preetam Pal wrote: > > Hi, > > I am doing quantile regression of y on a set of 15 explanatory variables x1, > x2,x

[R] Variable selection from given data

Hi, I am doing quantile regression of y on a set of 15 explanatory variables x1, x2,x15. I want to run 15 regression models where in the i-th model, y would be regressed on all x variables except x-i. Then I would compare the results. At this point, I donot know how to create the 15 data se

Re: [R] Variable selection based on both training and testing data

ndy > Subject: RE: [R] Variable selection based on both training and testing data > To: "'Jin Minming'" , "r-help@r-project.org" > > Date: Monday, 30 January, 2012, 13:39 > Variable section is part of the > training process-- it chooses the model. 

Re: [R] Variable selection based on both training and testing data

rom: r-help-boun...@r-project.org > [mailto:r-help-boun...@r-project.org] On Behalf Of Jin Minming > Sent: Monday, January 30, 2012 8:14 AM > To: r-help@r-project.org > Subject: [R] Variable selection based on both training and > testing data > > Dear all, > > The va

[R] Variable selection based on both training and testing data

Dear all, The variable selection in regression is usually determined by the training data using AIC or F value, such as stepAIC. Is there some R package that can consider both the training and test dataset? For example, I have two separate training data and test data. Firstly, a regression mode

[R] variable selection in linear regression

Hello With due respect, have a nice time. I would like to ask some command in R. It is regarding variable selection in linear regression. In R, there is one rebuild function called "step" which selecting variables according to AIC. let say i have data [y, x1,x2,x3,x4] we start with y~b

[R] variable selection in linear regression

Hello With due respect, have a nice time. I would like to ask some command in R. It is regarding variable selection in linear regression. In R, there is one rebuild function called "step" which selecting variables according to AIC. let say i have data [y, x1,x2,x3,x4] we start with y~b0 i

[R] Variable selection with quadratic terms

Hi, I am trying to do stepwise variable selection with the function step. I want to allow in the model all main effects, double interactions and quadratic terms. However, I would like to respect the hierarchy of the terms, i.e. a quadratic term can be added to the model only if the main effect

[R] Variable selection in NLME or LME4

Good morning I am learning about NLME and LME4, using Pinheiro and Bates and other materials from Douglas Bates, but I have not seen anything on how to do variable selection sensibly in this type of model. In OLS regression, I frequently use the lasso, but googling did not reveal a method for

[R] variable selection

Hi there, Can anyone suggest some packages in R doing variable selections in predictive modeling besides randomForest? Faster, better. Any also in clustering analysis? Thanks, Weiwei -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..."

Re: [R] variable selection in logistic

p-boun...@r-project.org > >> > [mailto:r-help-boun...@r-project.org > <mailto:r-help-boun...@r-project.org > > ><mailto:r-help-boun...@r-project.org > <mailto:r-help-boun...@r-project.org>>] > On >Behalf Of Fr

Re: [R] variable selection in logistic

;designed to provide the information to do so. You don't >> get >>something for >>nothing. >>Cheers, >>Bert Gunter >>Genentech Nonclinical Biostatistics >>-Original Message--

Re: [R] variable selection in logistic

Behalf Of Frank E Harrell Jr Sent: Wednesday, September 02, 2009 9:07 PM To: annie Zhang Cc: r-help@r-project.org <mailto:r-help@r-project.org> <mailto:r-help@r-project.org <mailto:r-help@r-project.org>> Subject: Re: [R]

Re: [R] variable selection in logistic

cannot overcome the "as many > >>variables as samples" problem either. Entropy rules. If what is > >>sought is a > >>way to determine the "truly important" variables, then the study must > >> be > >>designed to pro

Re: [R] variable selection in logistic

t >> be >>designed to provide the information to do so. You don't get >>something for >>nothing. >> >>Cheers, >> >>Bert Gunter >>Genentech Nonclinical Biostatistics >> >> >>-Original Message- &g

Re: [R] variable selection in logistic

elp-boun...@r-project.org> [mailto:r-help-boun...@r-project.org <mailto:r-help-boun...@r-project.org>] On Behalf Of Frank E Harrell Jr Sent: Wednesday, September 02, 2009 9:07 PM To: annie Zhang Cc: r-help@r-project.org <mailto:r-help@r-project.org> Subj

Re: [R] variable selection in logistic

: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > On > Behalf Of Frank E Harrell Jr > Sent: Wednesday, September 02, 2009 9:07 PM > To: annie Zhang > Cc: r-help@r-project.org > Subject: Re: [R] variable selection in logistic > > annie Zhang wrote: > >

Re: [R] variable selection in logistic

unter Genentech Nonclinical Biostatistics -Original Message- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Frank E Harrell Jr Sent: Wednesday, September 02, 2009 9:07 PM To: annie Zhang Cc: r-help@r-project.org Subject: Re: [R] variable selection in logisti

Re: [R] variable selection in logistic

>>  You mean the backward and forward stepwise selection is bad? You also >> suggest the penalized logistic regression is the best choice? Is there any >> function to do it as well as selecting the best penalty? >>  Annie > > All variable selection is bad unless its in the context of penalization.

Re: [R] variable selection in logistic

annie Zhang wrote: Hi, Frank, You mean the backward and forward stepwise selection is bad? You also suggest the penalized logistic regression is the best choice? Is there any function to do it as well as selecting the best penalty? Annie All variable selection is bad unless its in the con

Re: [R] variable selection in logistic

Hi, Frank, You mean the backward and forward stepwise selection is bad? You also suggest the penalized logistic regression is the best choice? Is there any function to do it as well as selecting the best penalty? Annie On Wed, Sep 2, 2009 at 7:41 PM, Frank E Harrell Jr wrote: > David Winsemiu

Re: [R] variable selection in logistic

David Winsemius wrote: > > > On Sep 2, 2009, at 9:36 PM, annie Zhang wrote: > >> Hi, R users, >> >> What may be the best function in R to do variable selection in >> logistic >> regression? > > PhD theses, and books by famous statisticians have been pursuing the > answer to that question

Re: [R] variable selection in logistic

David Winsemius wrote: On Sep 2, 2009, at 9:36 PM, annie Zhang wrote: Hi, R users, What may be the best function in R to do variable selection in logistic regression? PhD theses, and books by famous statisticians have been pursuing the answer to that question for decades. I have the sam

Re: [R] variable selection in logistic

On Sep 2, 2009, at 9:36 PM, annie Zhang wrote: Hi, R users, What may be the best function in R to do variable selection in logistic regression? PhD theses, and books by famous statisticians have been pursuing the answer to that question for decades. I have the same number of variable

[R] variable selection in logistic

Hi, R users, What may be the best function in R to do variable selection in logistic regression? I have the same number of variables as the number of samples, and I want to select the best variablesfor prediction. Is there any function doing forward selection followed by backward elimination in st

Re: [R] variable selection for multiple regression model

ermountain Healthcare [EMAIL PROTECTED] 801.408.8111 > -Original Message- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > project.org] On Behalf Of 1072 > Sent: Saturday, October 18, 2008 12:07 PM > To: r-help@r-project.org > Subject: [R] variable selection f

[R] variable selection for multiple regression model

Does anyone know how can I run all subsets regression model and get adjusted R square, Cp, and PRESSp for each model? -- View this message in context: http://www.nabble.com/variable-selection-for-multiple-regression-model-tp20049313p20049313.html Sent from the R help mailing list archive at N

Re: [R] Variable Selection for data reduction and discriminant anlaysis

Hi Gareth, >> My data is transformed to the clr or alr under Aitchison geometry, so I >> am essentially working >> in Euclidean space. Great: glad to hear it. >> Has anyone had experience doing stepwise LDA?? I can't for the life of >> me find any help >> online about where to start. A bett

Re: [R] Variable Selection for data reduction and discriminant anlaysis

Thanks Mark, I failed to mention that i'm working within a compositional framework. I didn't want to confuse things. My data is transformed to the clr or alr under Aitchison geometry, so I am essentially working in Euclidean space. Has anyone had experience doing stepwise LDA?? I can't for t

Re: [R] Variable Selection for data reduction and discriminant anlaysis

There are some pointers to packages for variable selection in the task view for Chemometrics and Computational Physics at http://cran.r-project.org/web/views/ChemPhys.html On Sun, 21 Sep 2008, Gareth Campbell wrote: > Hello all, > > I'm dealing with geochemical analyses of some rocks. > > If I us

Re: [R] Variable Selection for data reduction and discriminant anlaysis

Hi Gareth, >> If I use the full composition (31 elements or variables), I can get >> reasonable separation of my 6 sources. A word of advice: You need to be exceptionally careful when analyzing compositional data. Taking compositions puts your data values into a constrained/bounded space (genera

[R] Variable Selection for data reduction and discriminant anlaysis

Hello all, I'm dealing with geochemical analyses of some rocks. If I use the full composition (31 elements or variables), I can get reasonable separation of my 6 sources. Then when I go onto do LDA with the 6 groups, I get excellent separation. I feel like I should be reducing the variables to

[R] Variable selection in R

Disclaimer : Short of having local statistical expertise at hand, I'm using this list because I use R for variable selection in the context of linear multiple regression but the questions I have relate more to basic statistics than to R per se. Please redirect me to another appropriate list if su