similar to: Stepwise

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 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...

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

Is there a colinearty function implemented in R? I have tried help.search("colinearity") and help.search("collinearity") and have searched for "colinearity" and "collinearity" on http://www.rpad.org/Rpad/Rpad-refcard.pdf but with no success. Many thanks in advance, Peter Lauren.

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 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, a colleague ran a stepwise discriminant analysis twice in a row and got different results, suggesting some "sochasticity" in the algorithms involved. I looked at her data and found that there was a lot of collinearity, so that I reckoned that maybe "stepclass" (klaR) cannot find a clear winner when trying to include a new variable and makes a random choice. Is that true?

Hi All, I have questions about MANOVA which I am still not sure if appropriately I should use it. For example I have a data set like this: BloodPressure (BP) Weight Height 120 115 165 125 145 198 156 99 176 I know that BloodPressure is correlated with both Weight and Height, however colinearity exists between Weight and Height. When I use BP = Weight + Height

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

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, in the paper "Avoiding the effects of concurvity in GAM's .." of Figueiras et al. (2003) it is mentioned that in GLM collinearity is taken into account in the calc of se but not in GAM (-> results in confidence interval too narrow, p-value understated, GAM S-Plus version). I haven't found any references to GAM and concurvity or collinearity on the R page. And I

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 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

 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]]

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

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 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

I'm sorry to all those who are tired of seeing my email appear in need of help. But, I've never coded in any program before, so this has been a difficult process for me. Is there a simple function to test for colinearity in R? I'm running a logistic regression and a linear regression. Thanks for the help! [[alternative HTML version deleted]]

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