similar to: bug in step()?

The birth weight example from ?stepAIC in package MASS runs well as indeed it should. However when I change stepAIC() calls to step() calls I get warning messages that I don't understand, although the output is similar. Warning messages: 1: In model.response(m, "numeric") : using type="numeric" with a factor response will be ignored (and three more the same.) Checked

Hi all, I've experienced this problem using step() and stepAIC() when a name of a regressors has blanks in between (R:R1.7.0, os: w2ksp4). Please look at the following code: "x" <- c(14.122739306734, 14.4831100207131, 14.5556459667089, 14.5777151911177, 14.5285815352327, 14.0217803203846, 14.0732571632964, 14.7801310180502, 14.7839362960477, 14.7862217992577)

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 all,   I’m using "step" and "stepAIC" for stepwise regression. After each step, I would like to make an additional calculation based on the independent variables that have been selected until this step and their corresponding weights. Where do I have to add this calculation?   And a second question: Is it possible, to define a certain limit of factors for the regression,

Hello, I am now running a multiple linear regression program, but I do not know the difference between the command step and stepAIC. Thanks. Maggie [[alternative HTML version deleted]]

Hello, I have some trouble using step() and stepAIC() functions. I'm predicting recruitment against several factors for different plant species using a negative binomial glm. Sometimes, summary(step(model)) or summary(stepAIC(model) does not select the best model (lowest AIC) but just stops before. For some species, step() works and stepAIC don't and in others, it's the opposite.

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

Exec. Summary: step() basically ``fails'' whereas MASS' stepAIC() does work This may not be a bug in the strictest sense, but at least something for the wish list. Unfortunately I have no time currently to investigate further myself but want to be sure this won't be forgotten: The example is using a real data set with 216 observations on 9 variables -- where we have

Hello dear R mailing list members. I have recently became curious of the possibility applying model selection algorithms (even as simple as AIC) to regressions of large datasets. I searched as best as I could, but couldn't find any reference or wrapper for using step or stepAIC to packages such as biglm. Any ideas or directions of how to implement such a concept ? Best, Tal --

Your example works on 2 of the 3 systems I tried (and in fact the warnings you got on stepAIC are spurious). It's all down to rounding errors. I've added a fuzz in the termination test. B On Wed, 16 Jul 2003 maechler@stat.math.ethz.ch wrote: > Exec. Summary: > step() basically ``fails'' whereas MASS' stepAIC() does work > > This may not be a bug in the

Dear All, For logistic regression models: is it possible to use validate (rms package) to compute bias-corrected AUC, but have variable selection with AIC use step (or stepAIC, from MASS), instead of fastbw? More details: I've been using the validate function (in the rms package, by Frank Harrell) to obtain, among other things, bootstrap bias-corrected estimates of the AUC, when variable

I can only assume I'm betraying my ignorance here, but this is not what I would expect. I'm getting the following from a stepwise selection (with both step and stepAIC): > step(lm(sqrt(Grids)~ SE + Edge + NH), scope=~ (Edge + SE + NH)^2) Start: AIC= 593.56 sqrt(Grids) ~ SE + Edge + NH Df Sum of Sq RSS AIC <none> 2147.0 593.6 + Edge:NH 1

Thank you for Prof.Ripley's suggestion. I fixed the program by adding a lower scope, and the program ran, but I still got warning messages, and don't know what is going on, would this affect my results? ... Step: AIC= 12337.74 Surv(tlfup, cen) ~ MI[[j]]$trt + MI[[j]]$agem40 + MI[[j]]$agem40sq + mhtypeed1 + mhtypeed2 Df AIC <none> 12338 -

I have been learning how to use these functions and would like to know the following as I have so far been unable to find the answers in the documentation. 1) What stopping rules are used ? 2) Can the stopping rules be changed? 3) Can the results of each step be stored as objects in R and if so how ? 4) Is there a worked example somewhere of using the keep= argument ? Many thanks in

Do any R functions incorporate a sample sample size correction (e.g., Burnham and Anderson 1998). Thanks, Hank Stevens Martin Henry H. Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/botany/bot/henry.html http://www.muohio.edu/ecology

Hello all, I am trying to use stepwise procedure to select covariates in Cox model and use bootstrap to repeat stepwise selection, then record how many times variables are chosen by step() in bootstrap replications. When I use step() (or stepAIC) to do model selection, I got errors. Here is the part of my code for (j in 1:mm){ #<--mm=10 for (b in 1:nrow(reg.bs)){ #<--bootstrap 10

Would anyone help me solve my problem with R, please? I am very new to R. I am doing logistic regression analysis on the presence/absence of salamanders using several predictor variables, as shown below. I have checked my data, but I didn't find any 'NA' or empty cells. When I used step() or stepAIC to select significant predictor variables, <none>s appear to places where

Hi all, I'm fitting GLM's using the step or stepAIC procedures and I would like to save the AIC of the intermediate models. I would appreciate very much information about how todo this. Best wishes Germ??n L??pez

hello I built logistic regression model. To model check I used stepAIC. But I don't know how it is interpreted . I could not any find any explanation about it For instance which model is preferable ? What are the critarias to choose beter model I will appreciate if you give me an explanation ? models --------- > lo1.step$anova Stepwise Model Path Analysis of Deviance Table Initial

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