similar to: Coxph and loglik converged before variable X

Hello, My question concerns model selection, stepAIC(), add1(), and coxph(). In Venables and Ripley (3rd Ed) pp389-390 there is an example of using stepAIC() for the automated selection of a coxph model for VA lung cancer data. A statistics question: Can partial likelihoods be interpreted in the same manner as likelihoods with respect to information based criterion and likelihood ratio tests?

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 R-Help, I am trying to perform forward selection on the following coxph model: >my.bpfs <- Surv(bcox$pfsdays, bcox$pfscensor) > b.cox <- coxph(my.bpfs ~ Cbase + Abase + Cbave + CbSD + KPS + gender + as.factor(eor) + Age)>stepAIC(b.cox, scope=list(upper =~ Cbase + Abase + Cbave + CbSD + KPS + gender + as.factor(eor) + Age, lower=~1), direction= c("forward")) However

Is it appropriate to use stepAIC (library MASS) with coxph objects (from library survival5) that use "cluster(id)"? It is my understanding that, when using "cluster(id)", we can test for sets of terms by using the methods in Wei et al., (1989; JASA, 84: 1065-1073), or as explained in pp. 53 and ff. of the survival.ps document. But if we use a likelihood ratio test instead

Full_Name: John E. Kolassa Version: Version 1.8.1 OS: Solaris Submission from: (NULL) (128.6.76.36) Stepwise model selection for coxph appears to fail with method="exact". The code step(coxph(Surv(1:100,rep(1,100))~factor(rep(1:4,25)),method="exact")) produces the error message Start: AIC= 733.07 Surv(1:100, rep(1, 100)) ~ factor(rep(1:4, 25)) Error in

Hi, I'm having a bit of trouble with using StepAIC with a coxph model. Can anybody tell me if there is anything wrong with what I am doing here (I've removed a few of the variables for the purpose of this email, I had about 20 before): start<- coxph(Surv(entryage2,age_at_death_yr,death)~finih5+prev+magegp+zpluralgp ,data=project_model1) fmAIC1 <-

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]

Full_Name: Jerome Asselin Version: 1.3.1 OS: MacOS 9.2 Submission from: (NULL) (142.103.173.46) The step() function attempts to calculate the deviance of fitted models even if does not really need it. As a consequence, the step() function gives an error when it is used with coxph(). (There is currently no method to calculate the deviance of coxph() fits.) The code below gives an example of how

Hi, I am now running a cross-validation using coxph coupled with stepAIC for model selection, is there anyway to suppress the output? It's too much. -Jack [[alternative HTML version deleted]]

Dear R-users I have a probelm running stepAIC in R1.7.1 I wrote a program which used stepAIC as a part of it, and it worked fine while I was using the previous version of R1.7.0. However, I found the program did not work any more. Now, R produces a message which tells "Error in as.data.frame.default(data) : can't coerce function into a data.frame" every time I run the part of

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

SImplify your call to lm using the "." argument instead of manipulating formulas. > strt <- lm(y1 ~ ., data = dat) and you do not need to explicitly specify the "1+" on the rhs for lm, so > frm2<-as.formula(paste(trg," ~ ", paste(xvars,collapse = "+"))) works fine, too. Anyway, doing this gives (but see end of output)" bst <-

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:

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

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 want to fit multiple cox models using the coxph() function. To do this, I use a for-loop and save the relevant results in a separate matrix. In the example below, only two models are fitted (my actual matrix has many more columns), one gives a warning message, while the other does not. Right now, I see all the warning message(s) after the for-loop is completed but have no idea which model

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

OK, here's the problem. Continuing with your example: strt1 <- lm(y1 ~1, dat) strt2 <- lm(frm1,dat) > strt1 Call: lm(formula = y1 ~ 1, data = dat) Coefficients: (Intercept) 41.73 > strt2 Call: lm(formula = frm1, data = dat) Coefficients: (Intercept) 41.73 Note that the formula objects of the lm object are different: strt2 does not evaluate the formula. So

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.

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