similar to: Contrast in GEE

Hi, there, I am having trouble using 'lrm' function in package 'Design'. Basically, the ' . ' after ' ~ ' wouldn't work. Here are some sample codes: > temp <- data.frame(a=c(rep(0,3),rep(1,3)),b=rnorm(6),c=c('a','b','c','a','b','c')) > lrm(a~.,data=temp) Error in terms.formula(formula, specials =

Hi, all, I am wondering if it is possible to set parameters of 'rpart' and 'tree' such that they will produce the exact same tree? Thanks. Auston Wei Statistical Analyst Department of Biostatistics and Applied Mathematics The University of Texas MD Anderson Cancer Center Tel: 713-563-4281 Email: wwei@mdanderson.org [[alternative HTML version deleted]]

Hi, List, I am using function 'cph' in package 'Design'. I have run into this error message but could not find documentation after looking for a long time. Could someone help me out? What kind of problem it is in my data set and how to fix it? Thanks a lot! Auston Error in fitter(X, Y, strata = Strata, offset = offset, weights = weights, :NA/NaN/Inf in foreign function

Hi, List, I used the following codes to generate ps plots but foo.ps contains nothing. Would someone please point out what is wrong with my codes? Thanks a million! postscript('foo.ps') par(mfrow=c(2,1)) par(mfg=c(1,1)) hist(rnorm(100),col='blue') par(mfrow=c(2,2)) par(mfg=c(2,1)) hist(rnorm(50),col='blue') par(mfg=c(2,2)) hist(rnorm(60),col='blue') dev.off()

Hi, I would like to fit a GLM model with GEE on clustered data. I tried to use gee in the GEE package on a twin data set. All cluster are of size 2. I removed the missing data and ordered by IDENTIF2 first. library(gee) mod.pc <- gee(Y ~ X1 + X2 , id = IDENTIF2, family = binomial, corstr = "unstructured", data = na.omit(df)) gives the following result : Beginning Cgee S-function,

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

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 users Recently I received advice from this fine group on gee() and sample weights One suggestion was to use geeglm() I hope someone can help me to solve a problem that arises when converting a code from gee to geeglm. *Here is a code that I wrote with the original data, not weighted: * > m1 <- gee( Bin ~ educ+agemean+ residencysize + yearx , id = rad09 , data = Males,

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

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

I'm not sure if the gee package is still actively maintained, but I for one find it extremely useful. However, I've come across a few infelicities that I'm hoping could be resolved for future versions. Hope it's okay to list them all in one post! They are: (1) AR(1) models don't fit when clustsize = 1 for any subject, even if some subjects have clustsize > 1. (2) If the

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'm running GEE using geepack. I set corstr = "ar1" as below: > m.ar <- geeglm(L ~ O + A, + data = firstgrouptxt, id = id, + family = binomial, corstr = "ar1") > summary(m.ar) Call: geeglm(formula = L ~ O + A, family = binomial, data = firstgrouptxt, id = id, corstr = "ar1") Coefficients:

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

Hi, my dependent variable is a proportion ("prob.bind"), and the independent variables are factors for group membership ("group") and a covariate ("capacity"). I am interested in the effects of group, capacity, and their interaction. Each subject is observed on all (4) levels of capacity (I use capacity as a covariate because the effect of this variable is normatively

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 ~

Hi, having used both the gee pacakge and the geepack package, i am unsure of how to interpret the results. Here are the results from the geeglm function from the geepack package > gee2<-geeglm(data$erythema~data$product, data = data, id=subject, > family=binomial, corstr="independence") Warning message: In model.response(mf, "numeric") : using

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, 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:

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 =