similar to: need help with understanding stepAIC

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]]

Dear R-help I'm working on a large dataset which I have divided into 20 subsets based on similar features. Each subset consists of observations from different locations and I wish to use the location as a random effect. For each group I want to select regressors by a stepwise procedure and include a random effect on the intercept. I use stepAIC() and lme(). (The lmer()-function doesn't

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Dear R users, Would it be difficult to change the code of stepAIC (from the MASS library) to use AICc instead of AIC? It would be great to know of someone has tried this already. Best wishes Christoph.

I am confused about why the following occurs: > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 4.0

Based on the stepAIC help, I have assumed that it only was for lm, aov, and glm models. I gather from the following correspondence that it also works with lme models. Thomas Lumley 07:40 a.m. 28/04/03 -0700 4 Re: [R] stepAIC/lme problem (1.7.0 only) Prof Brian Ripley 04:19 p.m. 28/04/03 +0100 6 Re: [R] stepAIC/lme problem (1.7.0 only) Prof Brian Ripley 06:09 p.m. 29/04/03 +0100 6 Re: [R]

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

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

Hi, First of all, thanks for the great work on R in general, and MASS in particular. It's been a life saver for me many times. However, I think I've discovered a bug. It seems that, when I use weights during an initial least-squares regression fit, and later try to add terms using stepAIC(), it uses the weights when looking to remove terms, but not when looking to add them:

I can use stepAIC on an lme object in 1.6.2, but I get the following error if I try to do the same in 1.7.0: Error in lme(fixed = resp ~ cov1 + cov2, data = a, random = structure(list( : unused argument(s) (formula ...) Does anybody know why? Here's an example: library(nlme) library(MASS) a <- data.frame( resp=rnorm(250), cov1=rnorm(250), cov2=rnorm(250),

Hello, I'm trying to figure out how to run the stepAIC function starting with the NULL model. I can call the null model (e.g., lm(y ~ NULL)), but using this object in stepAIC doesn't seem to work. The objective is to calculate AICc. This can be done if stepAIC can be run starting with the NULL model; the (2p(p-1)/(n-p-1))to get AICc would be added to the final step AIC value. Can

Hi - I use stepAIC to automatically select the model. The stepAIC was applied on polr as follow:objPolr <- polr(formula=myformula, data=dat, method=METHOD);objPolr.step <- stepAIC(objPolr, trace=T);Then R complaints that it doesn't know about 'dat' when it executes the second line. Below is the exact error that I got when executing the stepAIC line above:Error in eval(expr,

Failed? What was the error message? Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Aug 22, 2017 at 8:17 AM, Stephen O'hagan <SOhagan at manchester.ac.uk> wrote: > I'm trying to use boot.stepAIC for

Dear List, I am having problems running stepAIC with a negative binomial regression model.  I am working with data on manta ray abundance, using 20 predictor variables.  Predictors include variables for location (site), time (year, cos and sin of calendar day, length of day, percent lunar illumination), oceanography (sea surface temp mean and std, sea surface height mean and std), weather (cos

Hello, I am using the function stepAIC (library MASS) to run a backward elimination on my linear regression. The new model stepAIC calculates contains coefficients that have a Pr(>|t|) value below 0.1, but I'd like to have only coefficients with 0.001 or below. How can I change the stop criterion for stepAIC, so that it is more strict? There is a parameter "steps", but it is

dear R users, I have fit the lm() on a mtrix of responses. i.e M1 = lm(cbind(R1,R2)~ X+Y+0). When i use summary(M1), it shows details for R1 and R2 separately. Now i want to use stepAIC on these models. But when i use stepAIC(M1) an error message comes saying that dropterm.mlm is not implemented. What is the way out to use stepAIC in such cases. regards,

The error is "the model fit failed in 50 bootstrap samples Error: non-character argument" Cheers, SOH. On 22/08/2017 17:52, Bert Gunter wrote: > Failed? What was the error message? > > Cheers, > > Bert > > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along > and sticking things into it." > -- Opus (aka

Hi, i'm trying to apply StepAIC with coxph...but i have the same error: stepAIC(fitBMT) Start: AIC=327.77 Surv(TEMPO,morto==1) ˜ VOD + SESSO + ETA + ........ Error in dropterm.default(fit,scope$drop, scale=scale,trace=max(0, : number of rows in use has changed: remove missing values? anybody know this error?? Thanks. Michele [[alternative HTML version deleted]]

Dear All, I am trying to use stepAIC for an lmer object but it doesn't work. Here is an example: x1 <- gl(4,100) x2 <- gl(2,200) time <- rep(1:4,100) ID <- rep(1:100, each=4) Y <- runif(400) <=.5 levels(Y) <- c(1,0) dfr <- as.data.frame(cbind(ID,Y,time,x1,x2)) fm0.lmer <- lmer(Y ~ time+x1+x2 + (1|ID), data = dfr, family = binomial)

I am trying to replicate the first example from stepAIC from the MASS package with my own dataset but am running into error. If someone can point where I have gone wrong, I would appreciate it very much. Here is an example : set.seed(1) df <- data.frame( x1=rnorm(1000), x2=rnorm(1000), x3=rnorm(1000) ) df$y <- 0.5*df$x1 + rnorm(1000, mean=8, sd=0.5) # pairs(df); head(df) lo <-