similar to: Generalized Estimating Equation (GEE): Why is Link = Identity?

Hi, I'm analyzing my data using GEE, which looks like below: > interact <- geeglm(L ~ O + A + O:A, + data = data1, id = id, + family = binomial, corstr = "ar1") > summary(interact) Call: geeglm(formula = lateral ~ ontask + attachment + ontask:attachment, family = binomial, data = firstgroupnowalking, id = id, corstr = "ar1") Coefficients:

Hi, I'm using 'geepack' to run Generalized Estimating Equations. I'm aware that I can use anova to compare two models, but would it be possible to test QIC on R? It seems that there were similar questions a couple of years ago, but the question has not been answered yet. I'd appreciate if someone could show me the code! Thank you, Sachi [[alternative HTML version

Dear R list members, I'd like to make a graph of coefficients of the intercept, variable 1, and variable 2 (and possibly the interaction between variable 1 and variable 2). When I use the lmList function as attached below, it shows a nice coefficient graph. > PACRP.lis <- lmList(PAffect ~ CRPC + CRPT + CINT | ID, redinteract) > coef(PACRP.lis) > PACRPlis.coef <-

Hi, Probably this is a very simple question for all the programmers, but how do you change from 32-bit builds (default) to 64-bit builds? I've been trying to run Anova to compare two models, but I get the following error message: Error: cannot allocate vector of size 1.2 Gb R(3122,0xa0ab44e0) malloc: *** mmap(size=1337688064) failed (error code=12) *** error: can't allocate region ***

Dear R users, Using R lme function, I found that both fixed and random effects of variable A on variable B are significant. Now, I'd like to analyze what variables are predicting differences in the slope. In other words, I'd like to know what variables (e.g., variable C) are predicting individual differences in the effects of A on B. I have many data points for A and B for each

Dear R list members, I have a question regarding xyplot. I managed to make a xyplot of all the IDs by using the syntax below: xyplot(PA ~ CRPC + CRPT | ID, data = redinteract) Now, I'd like to make a graph of a specific ID number (e.g., only ID number 301). I thought I could use "subset", but it seems to be not working. Could anyone let me know how I can make a graph of a

Hi all, I've been running loglinear models for three-way tables: one of the variables having three levels, and the other two having two levels each. An example looks like below: > yes.no <- c("Yes","No") > switch <- c("On","Off") > att <- c("BB","AA","CC") > L <- gl(2,1,12,yes.no) > T <-

Dear R-helpers, I'm analyzing a data with hierarchical linear model. I have one level 1 predictor and one level 2 predictor, which looks like below: fm1 <- lmer(y ~ 1 + x1 + x2 + x1:x2 + (1 + x1 | id.full)) where: y is the outcome variable. x1 is the level 1 predictor variable. x2 is the level 2 predictor variable. id.full is the conditioned variable. It runs beautifully when only x1

I want to apply Nyblom-Hansen test with the strucchange package, but I don't know how is the correct way and what is the difference between the following two approaches (leeding to different results): data("longley") # 1. Approach: sctest(Employed ~ Year + GNP.deflator + GNP + Armed.Forces, data = longley, type = "Nyblom-Hansen") #results in: # Score-based CUSUM

Dear helpeRs, the following data set comes from Johnson/Wichern: Applied Multivariate Statistical Analysis, 6th ed, pp. 304-306. /X <- structure(c(9, 6, 9, 3, 2, 7), .Dim = as.integer(c(3, 2))) Y <- structure(c(0, 2, 4, 0), .Dim = as.integer(c(2, 2))) Z <- structure(c(3, 1, 2, 8, 9, 7), .Dim = as.integer(c(3, 2)))/ I would like to compute Wilk's Lambda in R, which I know is 0.0385.

Dear all R-users, I wanted to fit a Garch(1,1) model to a dataset by: >garch1 = garch(na.omit(dat)) But I got a warning message while executing, which is: >Warning message: >NaNs produced in: sqrt(pred$e) The garch parameters that I got are: > garch1 Call: garch(x = na.omit(dat)) Coefficient(s): a0 a1 b1 1.212e-04 1.001e+00 1.111e-14 Can any one

Hi I have a very big data.frame : str(fslu12) 'data.frame': 277200 obs. of 11 variables: $ V1 : num 304 304 304 304 304 ... $ V2 : num 300 300 300 300 300 300 300 300 300 300 ... $ V3 : num 10 10 10 10 10 10 10 10 10 10 ... $ V4 : num 79.1 52 30.6 37.3 63 79.1 52 30.6 37.3 63 ... $ V5 : num 0.005 0.00498 0.00496 0.00491 0.00484 ... $ V6 : num 0.01 0.00997 0.00988 0.00969

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Recently I had the chance to look at smb service response times over OpenVPN using Wireshark/TShark. Here are 2 samples. In both samples a Win XP Pro machine is connecting to a server running samba-3.3.7 and openvpn-2.0.6_9 on FreeBSD 7.2-STABLE. Location #1 =================================================================== SMB RTT Statistics: Filter: Commands Calls Min

All, I have just returned to R after a decade of absence, and it is good to see that R has become such a great success! I'm trying to bring Design of Experiments into some aspects of software performance evaluation, and to teach myself that, I picked up "Experiments: Planning, Analysis and Optimization" by Wu and Hamada. I try to reproduce an analysis in the book using lm, but

Hi Experts, I am new to R, and was wondering how to do 3D linear regression in R. In other words, I need to Fit a 3-Dimensional Line to Data Points (input). I googled before posting this, and found that it is possible in Matlab and other commercial packages. For example, see the Matlab link:

I am relatively new to R and am looking for advice, ideas or both... I have a data set that consists of pathogen population sizes on individual plant units in an experimental field plot. However, in order to estimate the pathogen population sizes I had to destroy the plant unit and could not determine if that plant unit became diseased or to what extent it would have become diseased. I

Hello, I like to use rsync to transfer over mine and other peoples ever changing mail folder onto a remote server. My Server (FreeBSD 5.4) RSYNC Version is: 2.6.6 Protocol Version 29 My Client (OSX 10.4.2) RSYNC Version is: 2.6.3 Protocol Version 28 I use this command rsync -vvv --progress --stats --recursive --times \ --perms --links \ --delete /test/my?email?folder/*

> On Oct 9, 2017, at 6:03 AM, Big Floppy Dog <bigfloppydog at gmail.com> wrote: > > Hello Ulrik, > > I apologize, but I can not see how to provide a pdf in place of the density > function which calculates a KDE (that is, something from the dataset in the > example). Can you please point to the specific example that might help? > > Here is what I get: > >

The previously posted benchnarks for gcc vs llvm-gfortran had one mistake. I was actually had the gfortran for 4.3.1 installed instead of that from gcc 4.2.4. Below are the polyhedron benchmark results for all three compilers... gfortran 4.2.4 Benchmark Compile Executable Ave Run Number Estim Name (secs) (bytes) (secs) Repeats Err % --------- -------