similar to: stepwise with F statistics

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

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

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

 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 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

Sorry, I have wrote a wrong subject in the first email! Regards, Linda ---------- Forwarded message ---------- From: linda Porz <linda.porz@gmail.com> Date: 2011/5/25 Subject: combined odds ratio To: r-help@r-project.org Cc: r-help-request@stat.math.ethz.ch Dear all, I am looking for an R function which does stepwise selection cox model in r (delta chisq likelihood ratio test) similar

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

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

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 all, I am wondering why the step() procedure in R has the description 'Select a formula-based model by AIC'. I have been using Stata and SPSS and neither package made any reference to AIC in its stepwise procedure, and I read from an earlier R-Help post that step() is really the 'usual' way for doing stepwise (R Help post from Prof Ripley, Fri, 2 Apr 1999 05:06:03

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

Dear list, I wanna select the significant variables relative to bird distribution, using stepwise method. However, the result is always the best-fit model. Please kindly suggest if it is possible to show the selection process. Thank you Elaine [[alternative HTML version deleted]]

Hi, I would like to fit a linear model where some but not all explanators are chosen stepwise - ie I definitely want to include some terms, but others only if they are deemed significant (by AIC or whatever other approach is available). For example if I wanted to definitely include x1 and x2, but only include z1 and z2 if they are significant, something like this: df <-

I am using stepAIC for stepwise regression modeling. Is there a way to change the entry and exit alpha levels for the stepwise regression using stepAIC ? Many thanks, Berthold Berthold Stegemann Bakken Research Center Maastricht The Netherlands [[alternative HTML version deleted]]

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Hello, How can I run a backward stepwise regression with part of the variables fixed, while the others participate in the backward stepwise analysis? Thank you, Einat -- View this message in context: http://r.789695.n4.nabble.com/Stepwise-analysis-with-fixed-variables-tp4650015.html Sent from the R help mailing list archive at Nabble.com.

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 manually doing a form of stepwise regression in a mixed model but with many variables, it is time consuming. I thought I'd try to use an automated approach. stepAIC gave me false convergence when I used it with my model, so I thought it can't be hard to set up a basic program to do it based on the p-values. Thus I tried a couple of (very) crude options: 1) trying to

Hello, I have the data set of 1 + 49 variables. One of them is binary other are continous. I would like to be able to fit the model with all 49 variables and then run stepwise model selction. I'd appriciate some code snippets...