similar to: Prediction interval of Y using BMA

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

R lets one lock an environment with both an R function, base::lockEnvironment, and a C function, R_LockEnvironment, but, as far as I can tell, no corresponding function to unlock an environment. Is this omission on principle or just something that has not been done yet? I ask because several packages, including the well-used R6 and rlang packages, fiddle with some bits in with SET_ENVFLAGS and

Hi, I'm trying to using pspline in bic.surv{BMA}. ############################# library(BMA) library(survival) data(veteran) test.bic.surv<- bic.surv(Surv(time,status) ~ karno+pspline(age,df=3)+diagtime+prior, data = veteran, factor.type = TRUE) summary(test.bic.surv, conditional=FALSE, digits=2) ############################# The results are:

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) *

Hi ! I have been using BMA (bayesian model Averaing) package for modeling purposes, but was faced with a problem of incorporating offset term in the Poisson regression of disease rates. It looks like bic.glm does not accept offset keyword like glm ? Any ways to solve the problem using wt option in BMA? Janne Pitk?niemi -- Department of Public Health P.O.Box 41 (Mannerheimintie 172) 00014

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

HI, List I want to know weather any body has used BMA Package for model averaging for prediction of future values.....What are the paprmeters we have to suplly to the model. Any script for the same. thanks in advance ANIL KUMAR( METEOROLOGIST) LRF SECTION NATIONAL CLIMATE CENTER ADGM(RESEARCH) INDIA METEOROLOGICAL DEPARTMENT SHIVIJI NAGAR PUNE-411005 INDIA MOBILE +919422023277

Hello! I have data containing a large number of probabilities (about 60) of nonzero coefficients to predict 10 different independent variables (in 10 different BMA models). i've arranged these probabilities in a matrix like so: (IV1) (IV2) (IV3) ... p(b0) p(b0) p(b0) p(b1) p(b1) p(b1) p(b2) p(b2) p(b2) ... where p(b1) for independent variable 1 is p(b1 !=

Dear R users, I'm trying to save the results of separate logistic regression analyses of a dataset according to a categorical factor. For that, I first used a "by" function such as following : temp <- by( data, data$categorical.factor , function(x) summary( glm( formula = data$var1 ~ data$var2 , data = x , family = binomial("logit")))) and it worked perfectly well

I think we can mark this bug closed and check in the patch. Anyone have any concerns/questions before I do? Roy -------- Original Message -------- Subject: [ wxruby-Bugs-6144 ] error: cast from ?void*? to ?int? loses precision Date: Sun, 15 Oct 2006 08:37:31 -0400 (EDT) From: <noreply at rubyforge.org> To: noreply at rubyforge.org Bugs item #6144, was opened at 2006-10-15 00:04

Dear R users, I have just upgraded R from 2.11 to 2.12 on Ubuntu 9.04 (see more informations at the end) from the cran apt-get repository. One of the new things concerning the install.packages() function is stated here : install.packages() and remove.packages() with lib unspecified and multiple libraries in .libPaths() inform the user of the library location used with a message

The article on BMA (Bayesian model averaging) presents most valuable tools for model selection, but I find one detail confusing in Example 1. In page 4 of RNews 5/2, second paragraph says that the probability of Time variable not being in the model is 0.445. It seems to me that the figure should be 1 - 0.445 = 0.555, because p!=0.445 is the prob. of Time variable being in the model. The plot in

Hello R Group, Below is the code I submit: library("BMA") X <- read.table("C:/Documents and Settings/Administrator/Desktop/coding.txt",header=TRUE) Y <- read.table("C:/Documents and Settings/Administrator/Desktop/1DCS.txt",header=TRUE) IGout<- iBMA.glm(X, Y, glm.family= gaussian(), verbose = TRUE, thresProbne0 = 5 ) summary(IGout) IGout I

On 2019-12-27 04:34, Duncan Murdoch wrote: > On 26/12/2019 11:14 p.m., Spencer Graves wrote: >> Hello, All: >> >> >> ? ????? The default "simulate" method for lm and glm seems to ignore the >> sampling variance of the parameter estimates;? see the trivial lm and >> glm examples below.? Both these examples estimate a mean with formula = >>

Hi, I used the bic.surv function, S-PLUS functions developed by Chris Volinsky http://www.research.att.com/~volinsky/bma.html, without problems with S-PLUS. I have to use it with R but I am face with a problem: this function call a fortran routine named "leaps" (answer <- .Fortran("leaps", arguments)). I loaded the leaps library, and the leaps function work well with my R,

Hello, All: ????? The default "simulate" method for lm and glm seems to ignore the sampling variance of the parameter estimates;? see the trivial lm and glm examples below.? Both these examples estimate a mean with formula = x~1.? In both cases, the variance of the estimated mean is 1. ??? ??????? * In the lm example with x0 = c(-1, 1), var(x0) = 2, and

Robust Covariance Estimation Software via Nearest Neighbor Variance Estimation (NNVE) Software to carry out robust covariance estimation by Nearest Neighbor Variance Estimation (NNVE) [Wang and Raftery (2002, J. Amer. Statist. Ass.)] is now available for R and Splus. In the simulation studies published in JASA, this had mean squared error at least 100 times smaller than that of other leading

Robust Covariance Estimation Software via Nearest Neighbor Variance Estimation (NNVE) Software to carry out robust covariance estimation by Nearest Neighbor Variance Estimation (NNVE) [Wang and Raftery (2002, J. Amer. Statist. Ass.)] is now available for R and Splus. In the simulation studies published in JASA, this had mean squared error at least 100 times smaller than that of other leading

To whom it may be concernedDear all: i am a statistics of Humboldt university in berlin. I need R-2.4 for my data analysis, but unfortunately, it does not work. The installation works smoothly, but when I double click on the shortcut or from the start menu, the error warning jumps out. My computer is in german system and so do the error information. The following is the error warning and its

????? This issue arose for me in simulations to estimate confidence, prediction, and tolerance intervals from glm(., family=poisson) fits embedded in a BMA::bic.glm fit using a simulate.bic.glm function I added to the development version of Ecfun, available at "https://github.com/sbgraves237/Ecfun".? This is part of a vignette I'm developing, available at