similar to: gee niggles

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

Dear all, I am working on modifying the R working matrix to commodate some other correlations that not included in the package. I am having problem to locate where the R matrix are defined for regular matrices, i.e. independence, exchangeable, AR and unstructure. it might have something within .C("Cgee",but don't understand it well enough to know. Can you anyone help? /*gee source

Hi A quick question. Standard errors reported by gee/yags differs from the ones in geeglm (geepack). require(gee) require(geepack) require(yags) mm <- gee(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable") mm2 <- geeglm(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable", std.err = "san.se") mm3 <- yags(breaks ~

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 =

Hello, I want to fit a GEE with a user-defined link function. For the user-defined link-function I still read http://finzi.psych.upenn.edu/R/Rhelp01/archive/6555.html and http://finzi.psych.upenn.edu/R/Rhelp02a/archive/25727.html. Only for testing purposes I added a new link function (corlogit) in make.link (as well as in binomial) with exactly the same code as logit before using my code.

Hi, I am trying to install gee on our server but I get the error below. I do not have root on this machine so no control on how R was installed itself. It looks like it cannot find blas libs, the only ones i can find on the machine are: /usr/lib64/libblas.so.3 -> libblas.so.3.0.3 /usr/lib64/libblas.so.3.0 -> libblas.so.3.0.3 /usr/lib64/libblas.so.3.0.3 and : $ R CMD config BLAS_LIBS

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

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,

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

We are currently analyzing data on children clustered in day care-centers (DCC). We have tried to use geepack and gee libraries to estimate an overall incidence rate for absences (=number of absences/risk time) by specifying geese(number.absences ~ offset(log(risktime)), id=day.care.id, family=poisson("log"), data=dcc, corstr="exch",

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

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

Hi, I am trying to fit a ordinal GEE model using ordgee {geepack}. In order to check the validity of the function, I specified the correlation structure as independence (i.e. constr = "independence") and compared the result with that using polr {MASS}. Because a GEE model with an independent working correlation structure is equivalent to an ordinary GLM model, we would expect the same

Dear all, It looks like the parameters var-cov matrix returned by gee() is not adjusted for the scale parameter: > fm1 <- gee(nbtrp ~ strate * saison + offset(log(surf)), family = poisson, data = Eff2001, + id = loc, tol = 1e-10, corstr = "exchangeable") [1] "Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27" [1] "running glm to get initial

I'm using the function gee from the library(gee) gee(Y~X,id=clust.id,corstr="exchangeable",b=tmc$coef,family=binomial(link=logit),silent=T) Every time it runs, it dutifully prints out Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 user's initial regression estimate [,1] [1,] -4.5278335 [2,] -0.2737999 [3,] -0.9528306 [4,] 0.9393861 [5,]

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

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

Hello all, I am unsure of how to interpret the output from a Generalized Estimating Equation analysis of an ordinal response. I hope someone can enlighten me. The analysis was done using package 'repolr'. The data consists of a Score on a 3-point scale from 56 Subjects after repeatedly washing their hands with soap. Two soap Products were tested, each panelist washed 10 times = 10