similar to: Logit Model... GLM or GEE or ??

Hello, I have a bit of a tricky puzzle with trying to implement a logit model as described in a paper. The particular paper is on horseracing and they explain a model that is a logit trained "per race", yet somehow the coefficients are combined across all the training races to come up with a final set of coefficients. My understanding is that they maximize log likelihood across the

Dear everybody! Are there algorithms for multinominal logit/probit available for R? Is it my fault that I cannot find these in CRAN? Has somebody programmed these? with best wishes Ott Toomet Ott.Toomet at mail.ee -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

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, I am trying to do a lasso regression using the lars package with the following data (see attached): FastestTime WinPercentage PlacePercentage ShowPercentage BreakAverage FinishAverage Time7Average Time3Average Finish 116.90 0.14 0.14 0.29 4.43 3.29 117.56 117.77 5.00 116.23 0.29 0.43 0.14 6.14 2.14 116.84 116.80 2.00 116.41 0.00 0.14 0.29 5.71 3.71 117.24

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

Hi I am trying to make glm() work to analyze a toy logit system. I have a dataframe with x and y independent variables. I have L=1+x-y (ie coefficients 1,1,-1) then if I have a logit relation with L=log(p/(1-p)), p=1/(1+exp(L)). If I interpret "p" as the probability of success in a Bernouilli trial, and I can observe the result (0 for "no", 1 for

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

Dear all, I am using glm function in order to estimate a logit model i.e. glm(Y ~ data[,2] + data[,3], family = binomial(link = "logit")). I also created a function that estimates logit model and I would like it to compare it with the glm function. So, does anyone know what optimizer or optimization method glm uses in order to derive the result? Thank you Dimitris -- View this

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, I am running glm logit regressions with R and I would like to test a linear combination of coefficients (H0: beta1=beta2 against H1: beta1<>beta2). Is there a package for such a test or how can I perform it otherwise (perhaps with logLik() ???)? Additionally I was wondering if there was no routine to calculate pseudo R2s for logit regressions. Currently I am calculating the pseudo R2

Hello R-users. I haven't use R for a life time and this might be trivial - I hope you do not mind. I have a questions about arguments in the Glm-function. There seems to be something that I cannot cope. The basics are ok: > y <- as.double(rnorm(20) > .5) > logit.model <- glm(y ~ rnorm(20), family=binomial(link=logit), trace = TRUE) Deviance = 28.34255 Iterations - 1

Hi All, When modeling with glm and family = binomial (link = logit) and response values of 0 and 1, I get the predicted probabilities of assigning to my class one, then I would like to compare it with my vector y which does have the original labels. How should I change the probabilities into values of zero and 1 and then compare it with my vector y to find out about the accuracy of my

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:

Hi, Does anyone know of a function in R that can handle a fractional variable as the dependent variable? The catch is that the function has to be inclusive of 0 and 1, which betareg() does not. It seems like GLM might be able to handle the fractional logit model, but I can't figure it out. How do you format GLM to do so? Best, Rachael [[alternative HTML version deleted]]

I got the following warning messages when I did a binomial logit regression using glm(): Warning messages: 1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, Can some one share your thoughts on how to

If my aim is to select a good subset of parameters for my final logit model built using glm(). What is the best way to cross-validate the results so that they are reliable? Let's say that I have a large dataset of 1000's of observations. I split this data into two groups, one that I use for training and another for validation. First I use the training set to build a model, and the the

Hello all, I've come across an online posting http://www.biostat.wustl.edu/archives/html/s-news/2001-10/msg00119.html that described how to get confidence intervals for predicted values from predict.glm. These instructions were meant for S-Plus. Yet, it generally seems to work with R too, but I am encountering some problems. I am explaining my procedure in the following and would be most

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 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'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,]