similar to: Likely cause of error (code=1) in compar.gee/gee

Dear all, I don't understand the following behaviour: Running compar.gee (in library ape ) with and without the option 'data', it give me different results Example: .... Start R .... > load("eiber.RData") > ls() [1] "gee.na" "mydata" "mytree" > library(ape) > # runnig with the option data= mydata > compar.gee(alt ~ R,

Hello, I am trying to run the APE program COMPAR.GEE with a model containing a categorical response variable and a mixture of continuous and categorical independent variables. The model runs when I have categorical (binary) response and two continuous independent variables (VAR1 and VAR2), but when I include a categorical (binary) independent variable (VAR3), I receive the following output with

Hello, Much like Charlie Wills a year ago, I am trying to run the APE program COMPAR.GEE with a model containing a categorical response variable and a continuous variable. My command code is : compar.gee(costusdata$Syndrome ~ costusdata$Stamen, phy = costustree, family = "binomial") I receive the following output with an error: 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

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

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 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 =

I am trying to learn the gee function in R. So I try to generate some data and use this function. I have the following lines: ######################################## Gee # Generating lny=10+2*Si-Si^2+eta # eta ~ N(0,1) # Si ~ U(0,11) eta <- vector(mode="numeric",100) eta <- rnorm(100) Si <- vector(mode="numeric",100) Si <- runif(100, min=0, max=11) lny <-

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 having some difficulty understanding my output (below) from GEE. the person who wrote the program included some comments about the '3-th term gives diff between hyp/ox at time..', and after created an L vector to use for a WALD test. I was wondering if someone could help me understand the GEE output, the programmers comment, how L was determined, and its use in the WALD

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,

Hi, I am fitting a GEE model using gee R package, but I am not sure how to get OR and its CI? Could anyone give me some hints? Here are some output: > gee.obj <- gee(Affection~Sibsex+Probandsex,id = FAMID,family = binomial,corstr = "independence",data =seldata) Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 running glm to get initial regression estimate

windows Vista R 2.10.1 Is it possible to get p values from gee? Summary(geemodel) does not appear to produce p values.: > fit4<- gee(y~time, id=Subject, data=data.frame(data)) Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 running glm to get initial regression estimate (Intercept) time 1.1215614 0.8504413 > summary(fit4) GEE: GENERALIZED LINEAR MODELS FOR

Hello, I am running the program COMPAR.GEE within the package APE. My dependent variable is binomial, while my independent variable is a multi-state categorical variable. The output reports an estimate for each state of the independent variable except the first one. For example, for the variable X with 3 states, the output is: intercept (estimate) X2 (estimate) X3 (estimate)

I've been comparing variables among objects (taxa) related by known trees, using phylogentically independent contrasts in the ape package, and want to move on to more complex models e.g. by using gls with appropriate correlation terms. My trees contain lots of (hard) polytomies and information about ancestors, which I've been including- creating fully dichotomous trees by using zero branch

I was wondering if someone can help me figure out the following: I have two patient datasets, ds1 and ds2. ds1 has fields "patid", "date", and "lab1". ds2 has "patid", "date", and "lab2". I want to find all the patids that have at least 2 dated records for each lab. I started by splitting each dataset by patid, to create ds1.list

I have a set of data frames ds1, ds2, ... each having the same columns and column names: ds1 <- data.frame(x=c(1,2,3,4), y=c(5,6,7,8)) ds1 <- data.frame(x=c(9,10,11,12), y=c(13,14,15,16)) ... and I would like to combine them into just one data frame like ds <- rbind(ds1, ds2, ...) Because there are so many of them, I will have to use a character array nms <-

Dear R experts, Wish you all a HAPPY NEW YEAR! How do I go about plotting (using lattice) overlays of an ID (group=ID) observed, fitted data in each panel of a multiple panel plot (each panel identified by DS1 + DS2 + DS3)? "x" variable is X1 in the accompanying section of a dataset. each individual is identified by color and Y's are identified by "pch" or "lty".

Hi, I was taken off guard by the following behavior in a lattice plot. I frequently want to add a predicted curve defined at more points than in the formula expression of xyplot. There have been numerous examples of how to do this on r-help, but I still often struggle to make this work. I just realized that specifying one of the axes on a log scale does not guarantee that the added data for a