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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)) in addtion, I try these two models, it seems they are same. what is the difference between these two model. Is

I have a dataset like this: p aa index x y z sdx sdy sdz delta as ms cur sc 1 821p MET 1 -5.09688 32.8830 -5.857620 1.478200 1.73998 0.825778 13.7883 126.91 92.37 -0.1320180 111.0990 2 821p THR 2 -4.07357 28.6881 -4.838430 0.597674 1.37860 1.165780 13.7207 64.09 50.72 -0.0977129 98.5319 3 821p GLU 3 -5.86733 30.4759

Dear R users, I'm pretty new on using lmer package. My response is binary and I have fixed treatment effect (2 treatments) and random center effect (7 centers). I want to test the effect of treatment by fitting 2 models: Model 1: center effect (random) only Model 2: trt (fixed) + center (random) + trt*center interaction. Then, I want to compare these 2 models with Likelihood Ratio Test.

What does thoes this means??? warn:module:load_builtin_dll failed to load .so lib for builtin L"C:\\windows\\system32\\msvfw32.dll": /home/xekhz/.wine/dosdevices/c:/windows/system32/msv fw32.dll: invalid ELF header

I am trying to create a NetCDF file with bathymetry (a matrix) and then to add a grid type (an integer) as variables. This is the relevant part of the code: library(ncdf) wfile="my_file.nc" msv=-10 grdtp=2 # Dimensions d1=dim.def.ncdf("lon","degrees",as.double(lon)) d2=dim.def.ncdf("lat","degrees",as.double(lat)) # Variables

if I want to put fig1plot to the left, figYPplot to the right figYAaplot on the bottom. How to modify the following cod to do these? \begin{figure}[H] \centering \begin{minipage}[t]{0.5\textwidth} \centering <<label=fig1plot,fig=TRUE,echo=FALSE>>= <<fig1plot>> @ %\caption{Caption 1} \end{minipage} \begin{minipage}[t]{0.5\textwidth} \centering

I have been timing a particular model fit using lmer on several different computers and came up with a peculiar result - the model fit is considerably slower on a dual-core Athlon 64 using Goto's multithreaded BLAS than on a single-core processor. Here is the timing on a single-core Athlon 64 3000+ running under today's R-devel with version 0.995-5 of the Matrix package. >

I'm estimating two models for data with n = 179 with four clusters (21, 70, 36, and 52) named siteid. I'm estimating a logistic regression model with random intercept and another version with random intercept and random slope for one of the independent variables. fit.1 <- lmer(glaucoma~(1|siteid)+x1 +x2,family=binomial,data=set1,method="ML",

>>>>> "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

I run the following code, all other is ok, but plot(m.svm,p5.new,As~Cur) is not ok Anyone know why? install.packages("e1071") library(e1071) library(MASS) p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") p5.new<-subset(p5,select=-Ms) p5.new$Y<-factor(p5.new$Y) levels(p5.new$Y) <- list(Out=c(1), In=c(0)) attach(p5.new)

I am trying to fit a logistic regression model for this data set. Firstly, I want to plot P(Y=1) vs As and P(Y=1) vs Aa. Does any body know how to do these in R. Thanks, Aimin > p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") > str(p5) 'data.frame': 1030 obs. of 6 variables: $ P : Factor w/ 5 levels "821p","8ABP",..: 1

Hi, I am using lmer to analyze habitat selection in wolverines using the following model: (me.fit.of <- lmer(USED~1+STEP+ALT+ALT2+relM+relM:ALT+(1|ID)+(1|ID:TRKPT2),data=vdata, control=list(usePQL=TRUE),family=poisson,method="Laplace")) Here, the habitat selection is calaculated using a so-called discrete choice model where each used location has a certain number of alternatives

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

Dear list, I am needing to extract the estimate of overdispersion (deviance / residual degrees of freedom or c-hat) from multiple model objects - so they can then be used to compare the extent of overdispersion among alternative models as well as calculate qausi-AIC values. I have been unable to do this, despite consulting a number of manuals and searching the R-help. I am imaging that in

I'm trying to understand how to plot individual growth curve trajectories, with the overall mean trajectory superimposed (preferably in a slightly thicker line, maybe in black) over the individual trajectories. Using the sleepstudy data in lme4, here is the code I have so far: library(lme4) library(lattice) xyplot(Reaction ~ Days, data = sleepstudy, group = Subject, type = 'l')

Full_Name: Dale Barr Version: 2.5.1 (patched) OS: Ubuntu linux x86_64 Submission from: (NULL) (138.23.70.108) When RMySQL is loaded in before lme4, the summary() function for lmer objects in the lme4 packages produces the following error: Error in printMer(object) : no slot of name "status" for this object of class "table" When RMySQL is loaded AFTER lme4, however, no such

I am using mvs 2012 but the .sln provided is of msv 2010 so on opening .sln it tries to update libraries which is successful but I am unable to build (compile) it as it gives series of errors. I am pretty new to these things so can anyone tell me if I am doing anything wrong or how to create(.lib and .dll) files so I can use them in my project. Please forgive me if I have asked something foolish.

Dear all, I found a problem with 'lme4'. Basically, once you load the package 'aod' (Analysis of Overdispersed Data), the functions 'lmer' and 'glmer' don't work anymore: library(lme4) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data

Dear R Users, I want to fit GLMM with lmer with binomial data and a one-way random effects model with an overall mean and random effects. From R help, Laplace is slower than PQL, but more accurate. When I fit my model with Laplace method with control = list (usePQL = FALSE)), for most data sets it works well, but for some I get an error message (Error in if (any(sd < 0))

R Users, Emine Bayman sent this out earlier and we do not think it went through. Appologies if it did. We want to fit GLMM with lmer with binomial data and a one-way random effects model (overall mean is a fixed effect and there are random effects for each binomial). We are using the Laplace method. We are simulating multiple data sets and use the Laplace method with control = list