similar to: stepAIC

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)

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

Dear all, I have run the confint() function according to below and I get the following error message: > confint(stepAIC.glm.spe.var.konn2.abund, level=0.95) Waiting for profiling to be done... Error: missing value where logical needed In addition: Warning message: NaNs produced in: sqrt((fm$deviance - OriginalDeviance)/DispersionParameter) or > confint(stepAIC.glm.spe.var.konn2.abund,

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),direction = c("backward")) Start: AIC=3151.41

Hi List, Being new to R, I am trying to apply boot.stepAIC() for Model selection by bootstrapping the stepAIC() procedure. I had gone through the discussion in various thread on the variable selection methods. Understood the pros and cons of various method, also going through the regression modelling strategies in rms. I want to read Final model or Formula or list of variables automatically

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.

Folks, I'm using stepAIC(MASS) to do some automated, exploratory, model selection for binomial and Poisson glm models in R 1.3. Because I wanted to experiment with the small-sample correction AICc, I dug around in the code for the functions glm.fit stepAIC dropterm.glm addterm.glm extractAIC.glm and came across something I just don't understand. stepAIC() passes dropterm.glm() a

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

Hi, I'm a very new user of R and I hope not to be too "basic" (I tried to find the answer to my questions by other ways but I was not able to). I have 12 response variables (species growth rates) and two environmental factors that I want to test to find out a possible relation. The sample size is quite small: (7<n<12, depending on each species-case). I performed a

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms

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

I'm having trouble using binomial(link="cloglog") with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 <- GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need binomial(link="cloglog"), and this generates an error for me: >

Dear all, I'm building binomial mixed-model using lme4 package. I'm able to obtain outputs properly except when I include two particular variables: date (from 23 to 34; 1 being to first sampling day) and Latitude (UTM/100 000, from 55.42 to 56.53). No "NA" is any of those variables. In those cases, I get the warning message: "nlminb failed to converge" I tried to

Hello! I was reading R-intro and I have some suggestions: R-intro.html#A-sample-session rm(fm, fm1, lrf, x, dummy) suggestion rm(fm, fm1, lrf, x, y, w, dummy) The next section will look at data from the classical experiment of Michaelson and Morley to measure the speed of light. file.show("morley.tab") mm <- read.table("morley.tab") suggestion mm <- data(morley)

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

Dear All, I am looking for an R function or any other reference to generate a series of correlated Binomial (not a Bernoulli) data. The "bindata" library can do this for the binary not the binomial case. Thank you, Bernard --------------------------------- [[alternative HTML version deleted]]

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) thanks, Aimin Yan

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 detect the data matrix, and the program is

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 all, I've tried to reproduce the contour plot that appears in the book of Venables and Ripley, at page 255. Is a F-statistic surface and a confidence region for the regression parameters of a non-linear model. It uses the stormer data that are in the MASS package. I haven't been able to reproduce the plot either in R ( version 1.5 ) and S. It makes the axes and it puts the