similar to: Bayesian stepwise (was: Forward Stepwise regression based onpartial F test)

I am hoping to get some advise on the following: I am looking for an automatic variable selection procedure to reduce the number of potential predictor variables (~ 50) in a multiple regression model. I would be interested to use the forward stepwise regression using the partial F test. I have looked into possible R-functions but could not find this particular approach. There is a function

Hi everyone, I am wondering if there exists a stepwise regression function for the Bayesian regression model. I tried googling, but I couldn't find anything. I know "step" function exists for regular stepwise regression, but nothing for Bayes. Thanks -- View this message in context: http://www.nabble.com/Bayesian-regression-stepwise-function--tp26013725p26013725.html Sent from

So I recognize that: 1. many people hate forward stepwise regression (i've read the archives)--but I need it 2. step() or stepAIC are two ways to get a stepwise regression in R But here's the thing: I can't seem to figure out how to specify that I want the criteria to be Mallow's Cp (and then to subsequently tell me what the Cp stat is). I know it has something to do with

Hi Community R I need to make a stepwise using F statistics as a criteria to choose variables.I have 3 independant variables and one dependant variable, and I need to choose the best model fitting to my data using F statistics. The problem is I haven't found any package to do such operation. I have found packages to choose variables using significance level like "stepback" from

Hi, Is there any package for logistic model selection using BIC and Mallow's Cp statistic? If not, then kindly suggest me some ways to deal with these problems. Thanks. -- View this message in context: http://www.nabble.com/Stepwise-logistic-model-selection-using-Cp-and-BIC-criteria-tf4464430.html#a12729613 Sent from the R help mailing list archive at Nabble.com.

Hello, I'm actually using the "Step" procedure in R for multiple regression analysis. I'm using the stepwise selection which alternates between forward selection and backward elimination (direction "both" in the step procedure). I would like to know which F-levels R is using to enter and then to remove variables? I also would like which is the procedure to change

Hi, I looking for a method that use F or Chisq test instead of AIC in a stepwise modelo selection. I try the grasp package using the grasp.step.anova, but It dont work. > library(grasp) Carregando pacotes exigidos: gam Carregando pacotes exigidos: splines Carregando pacotes exigidos: mda Carregando pacotes exigidos: class > data(anorexia,package="MASS") > > m1 <-

I have a question regarding doing forward stepwise regression using the step function and an lm model. I am trying to run a stepwise procedure that will add a predictor variable, one at a time, conduct an F test to determine whether or not to drop any previous terms, and terminate when no other predictors qualify to be added. Using the step function and looking at the example in the book, I

I'm looking for guidance on how to implement forward stepwise regression using lmStepAIC in Caret. The stepwise "direction" appears to default to "backward". When I try to use "scope" to provide a lower and upper model, Caret still seems to default to "backward". Any thoughts on how I can make this work? Here is what I tried: itemonly <-

Dear all, I cannot seem to get the R functions step or stepAIC to perform forward or stepwise regression as I expect. I have enclosed the example data in a dataframe at the end of this mail. Note rubbish is and rnorm(17) variable which I have deliberately added to the data to test the stepwise procedure. I have used wateruse.lm<-lm(waterusage~.,data=wateruse) # Fit full model

Hello all, I am trying to figure out an optimal linear model by using stepwise regression which requires partial f-test, I did some Googling on the Internet and realised that someone seemed to ask the question before: Jim Milks <jrclmilks at joimail.com> writes: > Dear all: > > I have a regression model that has collinearity problems (between > three regressor variables). I

Dear R-Help, My problem/bug came to light,when fitting a linear model using stepwise selection. I'd started with the straightfoward command step(lm(y~., dataset)) This worked fine, but because this starts with all the possible explanatory variables, it results in a model with too many explanatory variables. Hence I wanted to start with just a constant and do forward selection, to get a

Dear all, I am a newcomer to R. I intend to using R to do stepwise regression and PLS with a data set (a 55x20 matrix, with one dependent and 19 independent variable). Based on the same data set, I have done the same work using SPSS and SAS. However, there is much difference between the results obtained by R and SPSS or SAS. In the case of stepwise, SPSS gave out a model with 4 independent

Hello, I have a question pertaining to the stepwise regression which I am trying to perform. I have a data set in which I have 14 predictor variables accompanying my response variable. I am not sure what the difference is between the function "mle.stepwise" found in the wle package and the functions "step" or "stepAIC"? When would one use

Dear Gurus, Thank you in advance for your assistance. I'm trying to understand scope better when performing stepwise regression using "step." I have a model with a binary response variable and 10 predictor variables. When I perform stepwise regression I define scope=.^2 to allow interactions between all terms. But I am missing something. When I perform stepwise regression (both

Hi, S-PLUS includes the function "stepwise" which can use a variety of methods to conduct stepwise multiple linear regression on a set of predictors. Does a similar function exist in R? I'm having difficulty finding one. If there is one it must be under a different name because I get an error message when I try 'help(stepwise)' in R. Thanks for your help, Andy Taylor

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Hello, I have been using the step() function for stepwise regression and was wondering if there was a way to specify a tolerance level either using step() or another stepwise function. So far I have only found an option to specify tolerance in lm.fit() but I am not an experienced R user and am not quite sure if this command can be implemented using a stepwise function. I have tried simply

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I'm attempting to select a model using stepAIC. I want to use a forward selection procedure. I have specified a "scope" option, but must not be understanding how this works. My results indicate that the procedure begins and ends with the "full" model (i.e., all 17 independent variables)...not what I expected. Could someone please point out what I'm not