similar to: mle.stepwise versus step/stepAIC

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

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

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

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

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, 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

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

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,

Ran a bunch of variables in R and the final result of StepAIC is as below: Why are the first 5 variables kept in the stepwise result?? Are the last 4 variables finally chosen after Stepwise? Thanks Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.315e-01 2.687e-01 0.490 0.63611 Core_CPI__ 1.290e-02 7.496e-03 1.721 0.11927 GDP_change -3.482e-03 2.075e-03 -1.678 0.12767

For example, if coefficient's p-value is less than 0.1 I want the stepwise to automatically drop that variable. Can the stepAIC be customized to do that? SAS seems to be able to customized stepwise function with p-value or cooks'd. thanks! ______________________________________________________________________ The information transmitted, including any attachments, is intended only

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

Subject: Eliminate repeated components from a vector X-Mailer: VM 7.00 under 21.4 (patch 6) "Common Lisp" XEmacs Lucid Reply-To: fjmolina at lbl.gov FCC: /home/f/.xemacs/mail/sent Does anyone know how I can eliminate repeated elements from a vector?

So I recognize that: 1. many people hate forward stepwise regression (i've read the archives)--but I need it 2. step() or stepAIC are two ways to get a stepwise regression in R But here's the thing: I can't seem to figure out how to specify that I want the criteria to be Mallow's Cp (and then to subsequently tell me what the Cp stat is). I know it has something to do with

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

Hello R-Users,   I have one binary dependent variable and a set of independent variables (glm(formula,…,family=”binomial”) ) and I am using the function stepAIC (“MASS”) for choosing an optimal model. However I am not sure if stepAIC considers significance properties like Likelihood ratio test and Wald test (see example below).     > y <- rbinom(30,1,0.4) > x1 <- rnorm(30) > x2

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 -

Hi, I am experiencing a baffling behaviour of stepAIC(), and I hope to get any advice/help on what went wrong or I'd missed. I greatly appreciate any advice given. I am using stepAIC() to, say, select a model via stepwise selection method. R Version : 1.7.1 Windows ME Many thanks and best regards, Siew-Leng ***Issue : When stepAIC() is placed within a function, it seems

Dear All, I am new to R and I would like to do the following: I want to fit a logistic model with 3 predictors and then perform a stepwise regression to select the best possible model using either the AIC/BIC criterion. I have used the stepAIC function which works fine but using this method only likely candidates are evaluated (i.e. not all the models are fitted). We should have 2^3=8 possible

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