search for: bicreg

R version: 0.62.1 (June 15, 1998) I just very naively attempted to grab the 'bicreg' package for Bayesian model selection from the StatLib library, and get it running under R. I've hit a brick wall very quickly, and as an R novice I'm not sure where to go next. Here's what happened: > bicreg(as.matrix(hiv[,c(-17,-18)]),as.matrix(hiv[,18])) Error: invalid form...

Dear Adrian and Ian (and r-helpers), I encountered a curious result in developing an example using the bicreg function in the BMA package: I noticed that pairs of models with equal R^2 and equal numbers of predictors had nevertheless different BIC values. Looking at the bicreg function, the definition of BIC appears to be the usual one, or close to it [bic <- n * log(1 - r2/100) + (size - 1) * log(n)],...

Dear friends. On the Bayesian averaging homepage http://www.research.att.com/~volinsky/bma.html I found some S code some of which perhaps may run in R. There was a call to an algorithm possibly within S but not supported by R 64.1: "leaps and bounds". I guess it is a minimization step. Can anyone clarify the algorithm and perhaps even give a pointer to some code ? I guess this may be

Hello everybody, In order to predict income for different time points, I fitted a linear model with polynomial effects using BMA (bicreg(...)). It works fine, the results are consistent with what we are looking for. Now, we would like to predict income for a future time point t_next and of course draw the prediction interval around the estimated value for this point t_next. I've found the formulae for the ponctual estimation of...

Dear R-Users, Could someone tell me what means the following message: > results <- bicreg(y, x, wt = rep(1, length(y)), strict = F, OR = 20) Error in as.double.default(y) : (list) object cannot be coerced to vector type 14 This error could be related to the fact of I trying to use a S-plus function in R? Thanks Rick Da-Silva --- -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-....

Dear friends, In R, the help of "bic.glm" tells the difference between postmean(the posterior mean of each coefficient from model averaging) and condpostmean(the posterior mean of each coefficient conditional on the variable being included in the model), But it's still unclear about the results explanations, and the artile of Rnews in 2005 on BMA still don't give more detail on