similar to: stepwise regression

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

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

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 <-

I would like to perform a bootstrap validation of a backward stepwise logistic regression analysis, but I am a beginner with R and I am not sure of how to do it. Is there anyone that can send me a sample file in tab format (that I can modify in Excel by pasting my data) and the pertinent R algorithm? Many thanks Giuseppe -- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Dr. Giuseppe Biondi

Hi, I have the response variable 'Y' and four predictors say X1, X2, X3 and X4. Assuming all the assmptions like Y follows normal distribution etc. hold and I want to run linear multiple regression. How do I run the stepwise regression (forward as well as the backward regression). >From other software (i.e. minitab), I know only X1 and X2 are significant so my regression equation

Dear all, I would like to carry out a stepwise regression using the function step. If I use either ~ (A + B + C + D)^4 or explicitly all main effects and interactions for the scope argument, the procedure only considers the four main effects for addition or elimination in each iteration step. What did I do wrong? I'm using R version 1.1.1 on Windows NT. (I'm sorry if this is a stupid

To win*/NT users: I just recieved a mail from r-help with a virus attachment. I am on an unix system but our mail server deleted the message anyways. here it is: Received: by stat.math.ethz.ch (8.9.1/8.9.1) id RAA27614 for r-help-gang-use; Wed, 10 Jan 2001 17:01:56 +0100 (MET) Received: (from daemon at localhost) by stat.math.ethz.ch (8.9.1/8.9.1) id RAA27412 for

Dear all, Does R or S-plus or any of their packages provide any command to form any of the following procedures to find Best Regression Equation - 1. 'All Possible Regressions Procedures' (is there any automated command to perform 2^p regressions and ordering according to criteria R2(adj), mallows Cp, s2- by not setting all the regression models manually), 2. 'Backward

Dear R helpers, I'm trying to build logistic regression model large dataset 360 factors and 850 observations. All 360 factors are known to be good predictors of outcome variable but I have to find best model with maximum 10 factors. I tried to fit full model and use stepAIC function to get best model but unfortenatly, the process takes too long to complete (more than 4 hours)... Is it

Dear all, First of all - thanks to the R-users who replied my previous mail "Selecting Best Regression Equation". However, seems i've got some other problems now - My data in c:\leafbrn.txt file is- -------------------------------------- i x1 x2 x3 y 1 3.05 1.45 5.67 0.34 2 4.22 1.35 4.86 0.11 3 3.34 0.26 4.19 0.38 4 3.77 0.23 4.42 0.68 5 3.52 1.10 3.17 0.18 6 3.54 0.76 2.76

Hello, I want to know how to perform stepwise elimination of variables to svyglm thanks [[alternative HTML version deleted]]

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First of all, I have GAMs noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian (link=log),groupD,methods=REML) Then I type " summary(noxd)". and show Family: gaussian Link function: log Formula: newNO2 ~ pressure

Dear All, For logistic regression models: is it possible to use validate (rms package) to compute bias-corrected AUC, but have variable selection with AIC use step (or stepAIC, from MASS), instead of fastbw? More details: I've been using the validate function (in the rms package, by Frank Harrell) to obtain, among other things, bootstrap bias-corrected estimates of the AUC, when variable

Dear all, I have encountered a problem when perform stepwise regression. The dataset have more 9 independent variables, but 7 observation. In R, before performing stepwise, a lm object should be given. fm <- lm(y ~ X1 + X2 + X3 + X11 + X22 + X33 + X12 + X13 + X23) However, summary(fm) will give: Residual standard error: NaN on 0 degrees of freedom Multiple R-Squared: 1, Adjusted

oops, Forgot to cc to the list. Regards, Mike -----Original Message----- From: dr mike [mailto:dr.mike at ntlworld.com] Sent: 24 February 2005 19:21 To: 'Spencer Graves' Subject: RE: [R] Bayesian stepwise (was: Forward Stepwise regression based onpartial F test) Spencer, Obviously the problem is one of supersaturation. In view of that, are you aware of the following? A Two-Stage

 Hi, Is there any function available to do stepwise selection of variables in Conditional(matched) logistic regression( clogit)? step, stepwise  etc are failing in case of conditional logistic regression. Please help.  Thanks P.T. Subha [[alternative HTML version deleted]]

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

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

I need to do a classic stepwise regression based not on AIC but on F in and F out (or on R2, or R2 adjusted). I have many variables and it will very useful for me to have a fast stepwise algorithm. Does anyone know if this exists for R and where I can find that ? Thank you very much. Pascal Grandeau -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing