search for: stepaic

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 step...

I'm trying to use boot.stepAIC for feature selection; I need to be able to specify the name of the dependent variable programmatically, but this appear to fail: In R-Studio with MS R Open 3.4: library(bootStepAIC) #Fake data n<-200 x1 <- runif(n, -3, 3) x2 <- runif(n, -3, 3) x3 <- runif(n, -3, 3) x4 <- runif(n...

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: hills.lm <- lm(time ~ dist + climb, data = hills, weights = 1/dist2) small.hills.lm <- stepAIC(hills.lm) stepAIC(small.hills.lm, time ~ dist + climb) In the first stepAIC(), it says that the AIC for the...

I would like test AICc as a criteria for model selection for a glm using stepAIC() from MASS package. Based on various information available in WEB, stepAIC() use extractAIC() to get the criteria used for model selection. I have created a new extractAIC() function (and extractAIC.glm() and extractAIC.lm() ones) that use a new parameter criteria that can be AIC, BIC or AICc...

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/lm...

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 <- rnorm(30) > x3 <- rnorm(30) > xdata <- data.fr...

...rt <- 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 <- boot.stepAIC(strt,data = dat,B=50,alpha=0.05,direction='forward',steps=limit, scope=list(lower=frm1,upper=frm2)) > bst Summary of Bootstrapping the 'stepAIC()' procedure for Call: lm(formula = y1 ~ ., data = dat) Bootstrap samples: 50 Direction: forward Penalty: 2 * df...

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 add...

...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 feature selection; I need to be able to specify the name of the dependent variable programmatically, but this appear to fail: > > In R-Studio with MS R Open 3.4: > > library(bootStepAIC) > > #Fake data > n<-200 > > x1 <- runif(n, -3, 3) > x2 <- runif(n, -3...

...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 feature selection; I need to be able to specify the name of the dependent variable programmatically, but this appear to fail: >> >> In R-Studio with MS R Open 3.4: >> >> library(bootStepAIC) >> >> #Fake data >> n<-200 >> >> x1 <- runif...

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 <- d...

Hi, I am experiencing a baffling behaviour of stepAIC(), and I hope to get any advice/help on this. Greatly appreciate any kind advice given. I am using stepAIC() to, say, select a model via stepwise selection method. R Version : 1.7.1 Windows ME Many thanks! ***Issue : When stepAIC() is placed within a function, it seems that stepAIC() cannot d...

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 exac...

I have a question about stepAIC and extractAIC and why they can produce different answers. Here's a stepAIC result (slightly edited - I removed the warning about noninteger #successes): stepAIC(glm(formula = (Morbid_70_79/Present_70_79) ~ 1 + Cohort + Cohort2, family = binomial, data = ghs_70_79, subset = ghs_70_full),d...

...used a fixed formula, and rename it back afterwards. Thanks for your help. SGO. -----Original Message----- From: Bert Gunter [mailto:bgunter.4567 at gmail.com] Sent: 22 August 2017 20:38 To: Stephen O'hagan <SOhagan at manchester.ac.uk> Cc: r-help at r-project.org Subject: Re: [R] boot.stepAIC fails with computed formula 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: (I...

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 d...

...ula into the lm object. This can be done, e.g. via: > strt2 <- eval(substitute(lm(form,data = dat), list(form = frm1))) ## yielding > strt2 Call: lm(formula = y1 ~ 1, data = dat) Coefficients: (Intercept) 41.73 So this looks like it should fix the problem, but alas no, the boot.stepAIC call still fails with the same error message. Here's why: > identical(strt$call, strt2$call) [1] FALSE So one might rightfully ask, what the heck is going on here?! Further digging: > str(strt$call) language lm(formula = y1 ~ 1, data = dat) > str(strt2$call) language lm(formula =...

...ards. > > Thanks for your help. > SGO. > > -----Original Message----- > From: Bert Gunter [mailto:bgunter.4567 at gmail.com] > Sent: 22 August 2017 20:38 > To: Stephen O'hagan <SOhagan at manchester.ac.uk> > Cc: r-help at r-project.org > Subject: Re: [R] boot.stepAIC fails with computed formula > > 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...

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=...

This is a question at the border between stats and r. When I do a glm with many potential effects, and select a model using stepAIC, many independent variables are selected even if there are no relationship between dependent variable and the effects (all are random numbers). Do someone has a solution to prevent this effect ? Is it related to Bonferoni correction ? Is there is a ratio of independent vs number of observation...