similar to: COMPAR.GEE Output

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, How can one write the output of a GLM/GEE model to a text file, such that the results appear in text file in the same (or similar) format they appear in the R console? For instance, I have the following model: Traitresult <- compar.gee(Y~X, data = data, family = "binomial", phy = tree, scale.fix = TRUE, scale.value = 1) How would I write "Traitresult" to a text

Hello, Is there an easy function for switching list to matrix. My list is of genetic distances between species pairs: A A 0 A B .5 A C .25 B C .5 and I want a distance matrix such as: A B C A 0 .5 .25 B .5 0 .5 C .25 .5 0 for use in a mantel test. Thank you for the help! cheers, charlie -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Charles G. Willis Department of Organismic

Hello, I am trying to test for non-linearity in a set of non-parametric data. Furthermore, I would like to do so in a multi-variate model. Frankly, I don't even know if this is possible, and if it is possible, I don't know if it is implemented in an R package. Any advice would greatly appreciated. Thank you! Charlie -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Charles G.

Hello, I am trying to preform a comparative analysis using the partial mantel method. However, i am having trouble creating compatible matrices. I can create matrices from my numerical data using the 'distance(x, method)' function from ecodist, but the row/column names are out put as numbers. While when i create a phyo-distance matrix using 'cophenetic(x)' from APE, the row/column

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,

Hi, I'm attempting a comparative analysis using the function compare.gee, package (ape), which uses phylogeny as a correlation matrix in gee (package gee), in R version 2.2.0 on a Windows XP PC. I'm looking to model the relationship between a continuous explanatory variable and a binary response variable for 600 species, taking into account the phylogeny of those species. Here's

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'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

Greetings, I have a very, very, simple research question. I want to predict one dichotomous variable using another dichotomous variable. Straightforward, right? The issue is that the dataset has two issues causing some complications for me. 1) The subjects are not independent -- they are sibling pairs. Every person in the dataset has a sibling in the dataset. This needs to be treated a

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 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,]

Hi, The standard error from logistic regression is slightly different from the naive SE from GEE under independence working correlation structure. Shouldn't they be identical? Anyone has insight about this? Thanks, Qiong a<-rbinom(1000,1) b<-rbinom(1000,2,0.1) c<-rbinom(1000,10,0.5) summary(gee(a~b, id=c,family="binomial",corstr="independence"))$coef

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,

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

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 <-

Hi all, 1. Can nonlinear models be used in gee? For example, I have a dataset which contains 2 variables x and y, I wrote data(ex) atttach(ex) a<-100 b<- -0.5 c<-4.5 d<-20 Then: a. y~gee(y~d+(a-d)/(1+(x/c)^b)) Error in terms.formula(formula, data = data) : invalid power in formula b. y~gee(y~d+(a-d)/(1+(x/c)) Error in model.frame(formula, rownames, variables, varnames, extras,

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

Hello, I would like to try to fit a GEE with user-specified link function. I read through a couple of thread on the list, for example http://tolstoy.newcastle.edu.au/R/help/04/12/9768.html#start and http://tolstoy.newcastle.edu.au/R/help/06/04/25298.html. I noticed that they are all 6 or more years old and the answer is very clear for GLM, however for GEE I am still not sure. There are two