similar to: p values for a GEE model

Hi to all, I found in the R-help archive how to calculate the p-value for a gee result: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/74150.html but there are two questions (I am afraid they are basic questions ...) 1. why is the result multiplicated with 2 2. how could I decide between lower.tail =TRUE and FALSE: example:

Hi all, I am trying to run R CMD check on a package which passes R CMD INSTALL. The check stops because of a parsing problem in the example of a given function at this line: return(res[res$ID %in% list$targetGeneSets,]) The code is ok, since it runs if I paste it in R. Is this a known parsing issue in R CMD check? Thanks, Adi > sessionInfo() R version 2.13.0 (2011-04-13) Platform:

Dear all, I need to fit a gee model with an auto-regressive correlation structure and I faced some problems. I attach a simple example: ####################################################### library(gee) library(geepack) # I SIMULATE DATA FROM POISSON DISTRIBUTION, 10 OBS FOR EACH OF 50 GROUPS set.seed(1) y <- rpois(500,50) x <- rnorm(500) id <- rep(1:50,each=10) # EXAMPLES FOR

Dear List: I found an error when I called the 'gee' function. I cannot solve and explain it. There are no errors when I used the 'geeglm' function. Both functions fit the gee model. The project supervisor recommends me to use the 'gee' function. But I cannot explain to him why this error happens. Would you help me solve this problem? I appreciate your help. In

I'm running some GEE models but when I request the summary(pcb.gee) all I get are rows and rows of intercorelations and they fill up the screen buffer so I can not even scroll back to see what else might be in the summary. How do I get the summary function to NOT print the intercorrelations? Thanks, -- Dean Sonneborn Programmer Analyst Department of Public Health Sciences University of

Hi I got two questions about the nnet function in R. I would be thankful to have an answer. 1) Does the function intrinsically normalize the X and Y matrices before the training, or normalization should be done by the user. 2) I need to understand the $wts matrix. I do imagine that it is a single column transformation of the two matrices of weighs (input to hidden Ninputs+1 x Nodes) and

I am having some difficulties to locate the standard error from GEE output. -----------sample output using list (geemodel)------------------------ Link: Identity Variance to Mean Relation: Gaussian Correlation Structure: Exchangeable Call: gee(formula = days.sick1 ~ bmi + age + gender + surveyround2 + surveyround3, id = childid, data = dat, family = gaussian,

Does anyone know how could I get the significant codes from mixed model output fitted with a GEE package? The output I got is the following: GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA gee S-function, version 4.13 modified 98/01/27 (1998) Model: Link: Logit Variance to Mean Relation: Binomial Correlation Structure: Exchangeable Call: gee(formula = bru

Hi everyone, According to R reference manual, the nnet function uses the BFGS method of optim to optimize the neural network parameters. I would like, when calling the function nnet to tell the optim function not to produce the tracing information on the progress of the optimization, or at least to reduce the frequency of the reports. I tried the following: a) nnet default > x<-rnorm(20)

Hi everyone, I am trying to build a R library called nnNorm but I have some troubles checking and installing it. Here is the setup: If I don't use a inst\doc directory(with the vignettes files) in my source, the install step is working fine: C:\test>RCMD INSTALL nnNorm ---------- Making package nnNorm ------------ adding build stamp to DESCRIPTION installing R files installing

Dear all, Is it possible to incorporate a nested design in GEE? I have measurements on trees that where measured in two years. The trees are nested in plots. Each plot contains 24 trees. The number of plots is 72. Hence we would expect 2 * 24 * 72 = 3456 data points. A few are missing, so we end up wih 3431 data points. This is what I have tried until now. #assuming independence between trees

*Dear R community* * * *I have two questions on data subsample manipulation. I am starting to use R again after a long brake and feel a bit rusty.* * * *I want to select a subsample of data for males and females separately* * * library(foreign) Datatemp <- read.spss("H:/Skjol/Data/HL/t1and2b.sav", use.value.labels = F) > table(Datatemp$sex) 1 2 3049 3702

I have included data at the bottom of this email. It can be read in by highlighting the data and then using this command: dat <- read.table("clipboard", header = TRUE,sep="\t") I can obtain solutions with both of these: library(gee) fit.gee<-gee(score ~ chem + time, id=id, family=gaussian,corstr="exchangeable",data=dat) and library(yags) fit.yags <-

I am trying to use gee() to calculate the robust standard errors for a logit model. My dataset (zol) has 195019 observations; winner, racebl, raceas, racehi are all binary variables. ID is saved as a vector of length 195019 with alternating 0's and 1's. I get the following error message. I also tried the same command with corstr set to "independence" and got the same

Hi A.K Regarding my question on comparing normal/ obese/overweight with blood pressure change, I did finally as per the first suggestion of stacking the data and creating a normal category . This only gives me a obese not obese 14, but when I did with the wide format hoping to get a obese14,normal14,overweight 14 Vs hibp 21, i could not complete any of the models. This time I classified obese=1

Hello Fellow R Users,I have spent the last week trying to find a work around to this problem and I can't seem to solve it. I simply want to plot my GEE model result with 95% confidence bands. I am using the geepack package to run a basic GEE model involving nestling weights, to a Gaussian distribution, with "exchangeable" error structure. I am examining how nestling weight varies

Dear R-helpers, I am using the package "gee" to run a marginal model. Here is the output. In my simulated data, both x and z are time-varying, so I include their interaction terms with time indicator (i.e. tind=0, if time 1, and 1 if time 2) The data is simulated, so the true parameter of z both at time 1 and time 2 is 5, which is very close from the model output for time 1, z =

Dear R users, Is there a simple way to use the effect function (library(effects)) with a GEE estimated model? library(gee) library(MASS) library(effects) attach(epil) b = gee(y ~ lbase*trt + lage + V4, family=poisson, id=subject, corstr="exchangeable") plot(effect("lbase*trt", b)) # Errore in effect("lbase*trt", b) : nessun metodo applicabile per

Dear all: Y=a+bX1+cX2 In the Gee (Generalized Estimation Equation) arguments: The arument Corstr has sveral choices: "independence" "fixed" "stat_M_dep" "non_stat_M_dep" "exchangeable" "AR-M" "unstructured" What does each term mean? How do I choose among them? How do I know the correlation structure of

Hi all, I have fitted a gee model with the gee package and included restricted cubic spline functions. Here is the model: chol.g <- gee(SKIN ~ rcs(CHOLT, 3), id=ID, data=chol, family=binomial(link="logit"), corstr="exchangeable") To extract the log odds I use: predict.glm(chol.g, type = "link") Now I want to compute the logg odds for specific CHOLT values