search for: bayesglm

Dear list, I have troubles with using Bayesian logistic model with count data in bayesglm. If I consider the following artificial data set, with a response y and a covariate x, in a form of row data (a) and count (b) : a<-data.frame(y=c(rep(1,10),rep(0,6)),x=c(rep(5,6),rep(4,4),rep(5,1),rep(4,5))) a$un<-1 b<-aggregate(a$un,list(y=a$y,x=a$x),sum) names(b)[3]<-"w"...

I have installed the arm package and its dependents (e.g MATRIX, etc), but cannot use the functions "invlogit" and "bayesglm" because it gives me the error message "Error: could not find function "invlogit" or Error: could not find function "invlogit". What could be the problem. Regards Carrington [[alternative HTML version deleted]]

Hi R bayesians, I need an advise how to resolve the two different estimates applying a traditional glm (TG) and a bayes glm (BG), and different results depending on the data formats of response data and the prior specs using bayesglm in R. I'm not familiar with bayes estimate and my colleague asked me to look into this because the EPA from France reported a quite different estimates for the follwoing ethylene data applying bayes method using MCSIM. As seen below glm give same results regardless of response data format, i....

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...ibe the difference of two standard deviations (so zeros are not a problem), but we don't actually know how to pass the right parameters to describe our data. As a third alternative, we tried modeling the ratio between standard deviations with a bayes-regularized glm model with Poisson links: `bayesglm(c(sd1, sd2) ~ c('mod1', 'mod2), weights = c(n1, n2), family = poisson()) But we are not sure whether it could be a meaningful approximation or not. We thought of a Poisson distribution because it can natively cope with zeros and the bayes regularization to avoid too extreme estimates (...

...~gelman/software/ | License: GPL (>= 2) | Packaged: Tue Nov 25 15:26:57 2008; SUYS | Built: R 2.7.1; ; 2008-12-17 21:31:22; unix | | Index: | | GO-class Function to Recall Last Source File | balance Functions to compute the balance statistics | bayesglm Bayesian generalized linear models. | bayespolr Bayesian Ordered Logistic or Probit Regression | binnedplot Binned Residual Plot | coefplot Generic Function for Making Coefficient Plot | contr.bayes.ordered Contrast Matrices | corrplot...

Dear all, I am encountering some issues with my data and need some help. I am trying to run glm analysis with a presence/absence variable as response variable and several explanatory variable (time, location, presence/absence data, abundance data). First I tried to use the glm() function, however I was having 2 warnings concerning glm.fit () : # 1: glm.fit: algorithm did not converge # 2:

Hola, Deseo simular un modelo logit con interacción, estimar sus coeficientes y comprobar si son o no parecidos al modelo teórico. Con este ejemplo obtengo que los coeficientes estimados no se asemejan mucho a los originales. ¿Se le ocurre a alguien cuál es el motivo de esta discrepancia? ¿y cómo solucionarlo? Muchas gracias Emilio logisticsimulation <- function(n){ dat <-

Hi All, I've been having a little trouble with creating a loop that will run a a series of t.tests for inspection, Below is the code i've tried, and some checks i've looked at. I've used the get(paste()) idea as i was told previously that the use of the eval should try and be avoided. I've run a single syntax to check that my systax is correct and works without any problems