similar to: GLM Empirical Likelihood

Dear list, I am fitting a logistic multi-level regression model and need to test the difference between the ordinary logistic regression from a glm() fit and the mixed effects fit from glmer(), basically I want to do a likelihood ratio test between the two fits. The data are like this: My outcome is a (1,0) for health status, I have several (1,0) dummy variables RURAL, SMOKE, DRINK, EMPLOYED,

Dear R-users Problem: Given the following system of ordinary differential euqations dM/dt = (-n)*M-h*M dS/dt = n*M-h*S+u*R dA/dt = h*S-q*A dI/dt = q*A-p*I dJ/dt = h*M-v*J dR/dt=p*I+v*J-u*R where M,S,A,I,J,R are state variables and n,h,u,q,p,v parameters. I'm able to calculate the likelihood value based on the solutions M,S,A,I,J,R of the ODE's given the data, but without an explicit

dear List: glm(a~b+c,family=binomial,data=x)->fit deviance(fit) returns the same as the residual deviance. I don't not know much about logistic regression.Some book tells that: " Deviance (likelihood ratio statistic): Deviance = -2log( likelihoodof the currentmodel /likelihoodof thesaturated model) Note: (1). The current model is the model of interest. (2). The saturated model

Hi all, I have run a (glm) analysis where the dependent variable is the gender (family=binomial) and the predictors are percentages. I get a warning saying "fitted probabilities numerically 0 or 1 occurred" that is indicating that quasi-separation or separation is occurring. This makes sense given that one of these predictors have a very influential effect that is depending on a

As suggested in my earlier message, I have a large population of independent variables and a binary dependent outcome. It is expected that only a few of the independent variables actually contribute to the outcome, and I'd like to find those. If it wasn't already obvious, I am *not* a statistician. Not even close. :-) Statistician colleagues have suggested that I use logistic

I would like to try a likelihood ratio test in place of waldtest. Ideally I'd like to provide two glm models, the second a submodel of the first, in the style of lrt (http://www.pik-potsdam.de/~hrust/tools/farismahelp/lrt.html). [lrt takes farimsa objects] Does anyone know of such a likelihood ratio test? Chris Elsaesser, PhD Principal Scientist, Machine Learning SPADAC Inc. 7921

I'm applying logistic regression to a moderate sized data set for which I believe Wald based confidence intervals on B coefficients are too conservative. Some of the literature recommends using confidence intervals based on the likelihood ratio in such cases, but I'm having difficulty locating a package that can do these. Any help would be immensely appreciated. Best, Jeff Hanna --

Hi all, Where f(x) is a logistic function, I have data that follow: g(x) = f(x)*.5 + .5 How would you suggest I modify the standard glm(..., family='binomial') function to fit this? Here's an example of a clearly ill-advised attempt to simply use the standard glm(..., family='binomial') approach: ######## # First generate some data ######## #define the scale and location of

Is there any way of getting log likelihood values for each iteration from poisson glm model? Many thanks.. Phil. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:

Leandra Desousa <sousa <at> ims.uaf.edu> writes: >> > I am using 'R' version 2.2.1 and 'S-PLUS' version 6.0; and I loaded the >> > MASS library in 'S-PLUS'. >> > >> > I am running a logistic regression using glm: >> > >> > >summary(mydata.glm) >> > Call: >> > glm(formula = COMU ~

Hello, I've been looking in the on-line manuals and searching past posts but can't find an answer to this question. I'd like to define my own variance function in a GLM. The function glm(formula, family=quasi(var="var function")) lets me choose from a selection of built in variances, but I want to define my own function for the variance. Is there an S-plus

Dear R users, I am using 'R' version 2.2.1 and 'S-PLUS' version 6.0; and I loaded the MASS library in 'S-PLUS'. I am running a logistic regression using glm: --------------------------------------------------------------------------- > mydata.glm<-glm(COMU~MeanPycUpT+MeanPycUpS, family=binomial, data=mydata)

I have run the following logistic regression model: options(contrasts=c("contr.treatment", "contr.poly")) m <- glm(wolf.cross ~ null.cross + feature, family = "binomial") where: wolf.cross = likelihood of wolves crossing a linear feature null.cross = proportion of random paths that crossed a linear feature feature = CATEGORY of linear feature with 5 levels:

# 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

I am currently running a generalized linear mixed effect model using glmer and I want to estimate how much of the variance is explained by my random factor. summary(glmer(cbind(female,male)~date+(1|dam),family=binomial,data= liz3")) Generalized linear mixed model fit by the Laplace approximation Formula: cbind(female, male) ~ date + (1 | dam) Data: liz3 AIC BIC logLik deviance 241.3

Greetings, everybody. Can I ask some glm questions? 1. How do you find out -2*lnL(saturated model)? In the output from glm, I find: Null deviance: which I think is -2[lnL(null) - lnL(saturated)] Residual deviance: -2[lnL(fitted) - lnL(saturated)] The Null model is the one that includes the constant only (plus offset if specified). Right? I can use the Null and Residual deviance to

Hello R users I ran more than 100 logistic regression analyses. Some of the analyses gave me this kind warning below. ########################################################### 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,

Hello everyone. I want to write some likelihood functions to use with optim. For example AR-structures, I have written a couple of functions that appear to get the job done. But, since I am new to R, I wanted to ask if there are any references in this topic. Thanks in advance, ******************************************* Antonio Paredes USDA- Center for Veterinary Biologics Biometrics Unit

Dear all, I have a question about the glm. When the events of an observation is 0, the logit function on it is Inf. I wonder how the glm solve it. An example: Treat Events Trials A 0 50 B 7 50 C 10 50 D 15 50 E 17 50 Program: treat <- factor(c("A", "B", "C", "D", "E")) events <- c(0, 7, 10, 15,

I want to do a logistic regression without an intercept term. This option is absent from glm, though present in some of the inner functions glm uses. I gather glm is the standard way to do logistic regression in R. Hoping it would be passed in, I said > r <- glm(brain.cancer~epilepsy+other.cancer, c3, > family=binomial(link="logit"), intercept=FALSE) which produced