similar to: lmer() with no intercept

Windows 2000 (v.5.00.2195), R 1.5.1 I have an lme object, fm0, which I examine with anova() and intervals(). The anova output indicates one of the interaction terms is significant, but the intervals output shows that the single parameter for that term includes 0.0 in its 95% CI. I believe that the anova is a conditional (sequential) test; is intervals marginal or approximate? Which should I

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

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: >

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)

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

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)

The subject says it all really. Question 1. Here is some code created to illustrate my problem, can anyone spot where I'm going wrong? Question 2. The reason I'm following a manual specification of models relates to the fact that in reality I am using mgcv::gam, and I'm not aware that dredge is able to separate individual smooth terms out of say s(a,b). Hence an additional request,

Dear All: I need some guidance in calculating a goodness-of-fit statistic for a Tobit Regression model. To develop the Tobit regression, I used the tobit() method from the AER package, which is basically a simpler interface to the survreg() method. I've read about pseudo R2 and C-index and was wondering if there is a package that calculates this for me. Also, is there a reason to select

Hello R-users! I tried to find a package to run a CHOW TEST. As a reference package I found the STRUCCHANGE package. Do you know if it works well otherwise can you recommend a different one? Thanks, Bernd -- View this message in context: http://www.nabble.com/Chow-Test-tf3878416.html#a10990270 Sent from the R help mailing list archive at Nabble.com.

Hi, I am learning xyplot. I have an example dataset attached. plotdata<-read.table("plotdata.txt",sep='\t',header=T,row.names=1) head(plotdata,n=4) y x type 1 -4.309601 -0.7448405 A 2 -4.715421 0.7875994 A 3 -2.310638 0.5455310 A 4 -2.685803 10.4116868 A xyplot(y~x,groups=type,plotdata, auto.key=T) This shows different colors for different

Hi, I would like to compare to regression models - each model has a different dependent variable. The first model uses a number that represents the learning curve for reward. The second model uses a number that represents the learning curve from punishment stimuli. The first model is significant and the second isn't. I want to compare those two models and show that they are significantly

k=lm(y~x) summary(k) returns R^2=0.9994 lm(y~x) is supposed to find coef. a anb b in y=a*x+b l=lm(y~x+0) summary(l) returns R^2=0.9998 lm(y~x+0) is supposed to find coef. a in y=a*x+b while setting b=0 The question is why do I get better R^2, when it should be otherwise? Im sorry to use the word "MS exel" here, but I verified it in exel and it gives: R^2=0.9994 when y=a*x+b is used

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

I have a table like the following. I want to fit Cm to Vm like this: Cm ~ Cl+Q1*b1*38.67*exp(-b1*(Vm-Vp1)*0.03867)/(1+exp(-b1*(Vm-Vp1)*0.03867))^2+Q2*b2*38.67*exp(-b2*(Vm-Vp2)*0.03867)/(1+exp(-b2*(Vm-Vp2)*0.03867))^2 I use nls, with start=list(Q1=2e-3, b1=1, Vp1=-25, Q2=3e-3, b2=1, Vp2=200). But I always get 'singlular gradient' error like this. But in SigmaPlot I can get the result. How

>>>>> "MM" == Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Tue, 18 May 2010 12:37:21 +0200 writes: >>>>> "GaGr" == Gabor Grothendieck <ggrothendieck at gmail.com> >>>>> on Mon, 17 May 2010 09:45:00 -0400 writes: GaGr> BIC seems like something that would logically go into stats

Dear R Helpers, I have noticed that when I use lmer to analyse data, the summary function gives different values for the AIC, BIC and log-likelihood compared with the anova function. Here is a sample program #make some data set.seed(1); datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y' )))) id=rep(1:120,2); datx=cbind(id,datx) #give x1 a

Dear all, I am running a panel regression with time and location fixed effects: ### reg1 <- lm(lny ~ factor(id) + factor(year) + x1+ I(x1)^2 + x2+ I(x2)^2 , data=mydata, na.action="na.omit") ### My goal is to use the estimation for prediction. However, I have 8,500 IDs, which is resulting in very slow computation. Ideally, I would like to do the following: ### reg2 <-

I have no direct experience with such horrific models, but your formula is a mess and Google suggests the biglm package with ffdf. Specifically, you should convert your discrete variables to factors before you build the model, particularly since you want to use predict after the fact, for which you will need a new data set with the exact same levels in the factors. Also, your use of I() is

Dear Gurus, Thank you in advance for your assistance. I'm trying to understand scope better when performing stepwise regression using "step." I have a model with a binary response variable and 10 predictor variables. When I perform stepwise regression I define scope=.^2 to allow interactions between all terms. But I am missing something. When I perform stepwise regression (both