similar to: data level for stepwise

Hello all, I have a question on manova in R: I'm using the function "manova()" from the stats package. Is there anything like a stepwise (backward or forward) manova in R (like there is for regression and anova). When I enter: step(Model1, data=Mydata) R returns the message: Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k, : no 'drop1'

Hi R, Does the "step" function used to perform stepwise regression has the option to specify the entry/exit significance levels for the independent variables? (This is similar to the 'slentry' and 'slstay' option in 'Proc reg' of SAS.). Or do we have any other package which does the above? Thanks. Thanks and Regards, Shubha This e-mail may

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

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

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

Yes, I am a newbie at R, but it is not the complex commands in R that have me baffled, but simple data commands. For example, why does something like: > plot(Girth ~ Height) *not* work after a command that allegedly loads the data: > data(trees) with the error message: Error in eval(expr, envir, enclos) : Object "Girth" not found but does work after the command: >

Hi, I know that is not possible make a stepwise procedure using REML in R, I can use ML for this. For nested design it may be very dangerous due the difference in variance structure, mainly in a splitplot design. ML make significative variables that REML dont make. I read an article that is made a stepwise procedure using GENSTAT. from article: "Terms were dropped from a model in a

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

Dear forum members, hope you can understand my unprofessionell English. I have never been working with RStudio before (even not with R), so I immediately need some help, because I want to carry out an automated multiple stepwise linear regression between temperatures and different surface parameters. Data can be found in several .xls- or .csv-files within one folder. Files are named

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

I use the step() function occasionally, and I think I understand its objective, proper use, and limitations. Now I see stepwise model selection being used in what seems to be an unusual way, and I wonder if it is right or wrong. May I describe? Genetic mapping tries to find where in an animal's genome are genetic elements that influence a particular physical trait. Say there are 100

 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]]

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

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 <-

Hi, Is there any facility in R to perform a stepwise process on a model, which will remove any highly-correlated explanatory variables? I am told there is in SPSS. I have a large number of variables (some correlated), which I would like to just chuck in to a model and perform stepwise and see what comes out the other end, to give me an idea perhaps as to which variables I should focus on. Thanks

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

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

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