Package for Bayesian Model Averaging in linear models and
generalized linear models using stochastic or
deterministic sampling without replacement from posterior
distributions. Prior distributions on coefficients are
from Zellner's g-prior or mixtures of g-priors
corresponding to the Zellner-Siow Cauchy Priors or the
mixture of g-priors from Liang et al (2008)
bas.glmto omit missing data.
confint.coef.bas. See the help files for an example or the vignette.
bas.glmto implement Bayes Fatcors based on the likelihood ratio statistic's distribution for GLMs.
A vignette has been added at long last! This illustrates several of the new features in
BAS such as
typeto specify estimator in fitted.bas and replaced with
fitted()are compatible with other S3 methods.
basto avoid NAMESPACE conficts with other libraries
diagnostic()function for checking convergence of
basobjects created with
method = "MCMC""
plot.basthat appears with Sweave
coef.bmawhen there is just one predictor
bmato avoid name conflicts with other packages
- added weights for linear models - switched LINPACK calls in bayesreg to LAPACK finally should be faster - fixed bug in intercept calculation for glms - fixed inclusion probabilities to be a vector in the global EB methods for linear models
- added intrinsic prior for GLMs - fixed problems for linear models for p > n and R2 not correct
- added phi1 function from Gordy (1998) confluent hypergeometric function of two variables also known as one of the Horn hypergeometric functions or Humbert's phi1 - added Jeffrey's prior on g - added the general tCCH prior and special cases of the hyper-g/n. - TODO check shrinkage functions for all
- new improved Laplace approximation for hypergeometric1F1 - added class basglm for predict - predict function now handles glm output - added dataframe option for newdata in predict.bas and predict.basglm - renamed coefficients in output to be 'mle' in bas.lm to be consistent across lm and glm versions so that predict methods can handle both cases. (This may lead to errors in other external code that expects object$ols or object$coefficients) - fixed bug with initprobs that did not include an intercept for bas.lm
- added thinning option for MCMC method for bas.lm - returned posterior expected shrinkage for bas.glm - added option for initprobs = "marg-eplogp" for using marginal SLR models to create starting probabilities or order variables especially for p > n case - added standalone function for hypergeometric1F1 using Cephes library and a Laplace aproximation -Added class "BAS" so that predict and fitted functions (S3 methods) are not masked by functions in the BVS package: to do modify the rest of the S3 methods.
- added bas.glm for model averaging/section using mixture of g-priors for GLMs. Currently limited to Logistic Regression - added Poisson family for glm.fit
- cleaned up MCMC method code
- removed internal print statements in bayesglm.c - Bug fixes in AMCMC algorithm
- fixed glm-fit.R so that hyperparameter for BIC is numeric
- added new AMCMC algorithm
- bug fix in bayes.glm
- added C routines for fitting glms
- fixed problem with duplicate models if n.models was > 2^(p-1) by
- save original X as part of object so that fitted.bma gives the
correct fitted values (broken in version 0.80)
- Added `hypergeometric2F1` function that is callable by R - centered X's in bas.lm so that the intercept has the correct
predict.bma to center newdata using the mean(X)
- Added new Adaptive MCMC option (method = "AMCMC") (this is not
stable at this point)
-Allowed pruning of model tree to eliminate rejected models
- Added MCMC option to create starting values for BAS (`method = "MCMC+BAS"`)
-Cleaned up all .Call routines so that all objects are duplicated or
allocated within code
- fixed ch2inv that prevented building on Windows in bayes glm_fit
- fixed fortran calls to use F77_NAME macro - changed allocation of objects for .Call to prevent some objects from being overwritten.
- fixed EB.global function to include prior probabilities on models - fixed update function
- fixed predict.bma to allow newdata to be a matrix or vector with the
column of ones for the intercept optionally included. - fixed help file for predict - added modelprior argument to bas.lm so that users may now use the beta-binomial prior distribution on model size in addition to the default uniform distribution - added functions uniform(), beta-binomial() and Bernoulli() to create model prior objects - added a vector of user specified initial probabilities as an option for argument initprobs in bas.lm and removed the separate argument user.prob