similar to: SAS proc reg stepwise procedure in R

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

Dear R-helpers, I am trying to do a stepwise procedure in which I want to force some variables in the model. I have searched around and it seems that only leaps package allows to force the variable in the stepwise procedure. I use the leaps package and use the regsubsets(lm1, force.in = 1, data) to force 1 variable in the model. However, the force.in command only allow me to force 1 variable

Dear R users, I have some environmental variables and I need to find the best combination of them in order to separate two main groups (coded 1 and 2). I have performed a discriminant analysis using the stepclass function as a method for selecting the most relevant environmental variables. The problem is that this function includes a parameter (start.vars) and my results change a lot when I

Hi, I try to make a simple for loop with a if else statement (First example - Below) and extend it to a more complex loop (Second example). However, my results #First example: x=c(1,2) t=for(i in 1:length(x)){ if (x==1){a=x+1}else if (x==2){a=x} } Returned from R: Warning messages: 1: the condition has length > 1 and only the first element will be used in: if (x == 1) { 2: the condition has

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

Is there a function in R which performs stepwise estimation in ways similar to SAS/STATA (i.e., allows the analyst to specify the significance levels for removal/addition of terms). I've been asked to evaluate two final models: one resulting from a backwards selection in R (stepAIC) and one resulting from a backwards selection using PROC LOGISTIC in SAS. The final terms are slightly

On 31/03/15 03:54, Sketch wrote: > On Mon, 30 Mar 2015, Rowland Penny wrote: > >> On 30/03/15 16:33, Luca Olivetti wrote: >>> Right, the correct solution would be to package samba in a way that >>> doesn't break sssd. >> >> You could also say that sssd should be packaged in a way that works >> with the sernet packages. > > I'm with

Dear all, I tried to use "step" function to do model selection, but I got an error massage. What I don't understand is that data as data.frame worked well for my other programs, how come I cannot make it run this time. Could you please tell me how I can fix it? ***************************************************************************************************

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 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

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

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

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 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]]

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