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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

I was wandering if anybody ever tried to compare the classification accuracy of nnet to other (rpart, tree, bagging) models. From what I know, there is no reason to expect a significant difference in classification accuracy between these models, yet in my particular case I get about 10% error rate for tree, rpart and bagging model and 80% error rate for nnet, applied to the same data. Thanks.

Friends, Is there a Partial Least Squares package implemented in R? Thanks, Lana [[alternative HTML version deleted]]

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

Hello, Does anybody know if the outscale option of randomForest yields the standarized version of the outlier measure for each case? or the results are only the raw values. Also I have notice that this measure presents very high variability. I mean if I repeat the experiment I am getting very different values for this measure and it is hard to flag the outliers. This does not happen with two other

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

Hi, I would like to find some methods about feature selection, but I only know the package "randomForest" after searching for a while. Could you recommend some other packages of feature selection? Thank you very much. Sincerely yours, Chad Yang. ==========================================================

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'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

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 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

Hi all, I originally posted this to the bioconductor group, but maybe it's better suited to the r-help... I'm using som() to partition samples of gene expression data into clusters. The point is to classify control vs. experimental cases (sample clustering). The original matrix was 22283 x 8. The 8 samples have 4 controls and 4 experimentals. I transposed the matrix so that its dim

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? ***************************************************************************************************

Hi, Is there any package for logistic model selection using BIC and Mallow's Cp statistic? If not, then kindly suggest me some ways to deal with these problems. Thanks. -- View this message in context: http://www.nabble.com/Stepwise-logistic-model-selection-using-Cp-and-BIC-criteria-tf4464430.html#a12729613 Sent from the R help mailing list archive at Nabble.com.

Dear all, I have a question regarding the use of stepAIC and polynomial (quadratic to be specific) terms in a binary logistic regression model. I read in McCullagh and Nelder, (1989, p 89) and as far as I remember from my statistics cources, higher-degree polynomial effects should not be included without the main effects. If I understand this correctly, following a stepwise model selection based

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

Dear All, Probably it is not highly relevant question: Why do stepwise regression functions in R (step() or stepAIC()) add/delete categorical variables as a set? For example, I have a four-level factor variable d, so dummies are d1,d2,d3, as stepwise regression operates d, adding or removing, d1,d2,d3 are simultaneously added/removed. What's the concern here if operating dummies individually?

Hello I am not really a statistic person, so it's possible i did something completely wrong... if this is the case: sorry... I try to get the best GLM model (with the lowest AIC) for my dataset. Therefore I run a stepAIC (in the "MASS" package) for my GLM allowing only two-variable-interactions. For the output (summary) I got a model with 7 (of 8) variabels and 5 interactions and

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

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