bms {BMS}  R Documentation 
Given data and prior information, this function samples all possible model combinations via MC3 or enumeration and returns aggregate results.
bms(X.data, burn = 1000, iter = NA, nmodel = 500, mcmc = "bd", g = "UIP", mprior = "random", mprior.size = NA, user.int = TRUE, start.value = NA, g.stats = TRUE, logfile = FALSE, logstep = 10000, force.full.ols = FALSE, fixed.reg = numeric(0), data = NULL, randomizeTimer = TRUE)
X.data 
a data frame or a matrix, with the dependent variable in the first column, followed by the covariates. 
burn 
The (positive integer) number of burnin draws for the MC3 sampler, defaults to 1000. (Not taken into account if mcmc="enumerate") 
iter 
If mcmc is set to an MC3 sampler, then this is the number of iteration draws to be sampled (ex burnins), default 3000 draws. 
nmodel 
the number of best models for which information is stored (default 500). Best models are used for convergence analysis between likelihoods and MCMC frequencies, as well as likelihoodbased inference. 
mcmc 
a character denoting the model sampler to be used. 
g 
the hyperparameter on Zellner's gprior for the regression coefficients. 
mprior 
a character denoting the model prior choice, defaulting to "random": 
mprior.size 
if 
user.int 
'interactive mode': print out results to console after ending the routine and plots a chart (default TRUE). 
start.value 
specifies the starting model of the iteration chain. For instance a specific model by the corresponding column indices (e.g. starting.model=numeric(K) starts from the null model including
solely a constant term) or 
g.stats 

logfile 
setting 
logstep 
specifies at which number of posterior draws information is written to the log file; default: 10 000 iterations 
force.full.ols 
default 
fixed.reg 
indices or variable names of 
data 
an optional 
randomizeTimer 
default 
Ad mcmc
:
Interaction sampler: adding an ".int" to an MC3 sampler (e.g. "mcmc="bd.int") provides for special treatment of interaction terms.
Interaction terms will only be sampled along with their component variables: In the colnumn names of X.data, interaction terms need to be
denominated by names consisting of the base terms separated by #
(e.g. an interaction term of base variables "A"
, "B"
and "C"
needs column name "A#B#C"
). Then variable "A#B#C"
will only be included in a model if all of the component variables ("A", "B", and "C") are included.
The MC3 samplers "bd
", "rev.jump
", "bd.int
" and "rev.jump.int
", iterate away from a starting model by adding, dropping or swapping (only in the case of rev.jump) covariates.
In an MCMC fashion, they thus randomly draw a candidate model and then move to it in case its marginal likelihood (marg.lik.) is superior to the marg.lik. of the current model.
In case the candidate's marg.lik is inferior, it is randomly accepted or rejected according to a probability formed by the ratio of candidate marg.lik over current marg.lik. Over time, the sampler should thus converge to a sensible distribution. For aggregate results based on these MC3 frequencies, the first few iterations are typically disregarded (the 'burnins').
Ad g
and the hyperg prior: The hyperg prior introduced by Liang et al. (2008) puts a prior distribution on the shrinkage factor g/(1+g), namely a Beta distribution Beta(1, 1/21)
that is governed by the parameter a. a=4 means a uniform prior distribution of the shrinkage factor, while a>2 close to 2 concentrates the prior shrinkage factor close to one.
The prior expected value is E(g/1+g)) = 2/a. In this sense g="hyper=UIP"
and g="hyper=BRIC"
set the prior expected shrinkage such that it conforms to a fixed UIPg (eqng=N) or BRICg (g=max(K^2,N) ).
A list of class bma
, that may be displayed using e.g. summary.bma
or coef.bma
. The list contains the following elements:
info 
a list of aggregate statistics: 
arguments 
a list of the evaluated function arguments provided to 
topmod 
a 'topmod' object containing the best drawn models. see 
start.pos 
the positions of the starting model. If bmao is a'bma' object this corresponds to covariates bmao$reg.names[bmao$start.pos]. If bmao is a chain that resulted from several starting models (cf. 
gprior.info 
a list of class 
mprior.info 
a list of class 
X.data 
data.frame or matrix similar to 
reg.names 
character vector: the covariate names to be used for X.data (corresponds to 
bms.call 
the original call to the 
The models analyzed are Bayesian normalgamma conjugate models with improper constant and variance priors akin to Fernandez, Ley and Steel (2001): A model M can be described as follows, with ε ~ N(0,σ^2 I):
y= α + X β + ε
f(β  σ, M, g) ~ N(0, g σ^2 (X'X)^1)
Moreover, the (improper) prior on the constant f(α) is put proportional to 1. Similarly, the variance prior f(σ) is proportional to 1/σ.
There are several ways to speedup sampling: nmodel=10
saves only the ten best models, at most a marginal improvement. nmodels=0
does not save the best (500) models, however then posterior convergence and likelihoodbased inference are not possible.
g.stats=FALSE
saves some time by not retaining the shrinkage factors for the MC3 chain (and the best models). force.fullobject=TRUE
in contrast, slows sampling down significantly if mcmc="enumerate"
.
Martin Feldkircher and Stefan Zeugner
Feldkircher, M. and S. Zeugner (2009): Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging, IMF Working Paper 09/202.
Fernandez, C. E. Ley and M. Steel (2001): Benchmark priors for Bayesian model averaging. Journal of Econometrics 100(2), 381–427
Ley, E. and M. Steel (2008): On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regressions. working paper
Liang, F., Paulo, R., Molina, G., Clyde, M. A., and Berger, J. O. (2008). Mixtures of g Priors for Bayesian Variable Selection. Journal of the American Statistical Association 103, 410423.
SalaiMartin, X. and G. Doppelhofer and R.I. Miller (2004): Determinants of longterm growth: a Bayesian averaging of classical estimates (BACE) approach. American Economic Review 94(4), 813–835
coef.bma
, plotModelsize
and density.bma
for some operations on the resulting 'bma' object, c.bma
for integrating separate MC3 chains and splitting of sampling over several runs.
Check http://bms.zeugner.eu for additional help.
data(datafls) #estimating a standard MC3 chain with 1000 burnins and 2000 iterations and uniform model priors bma1 = bms(datafls,burn=1000, iter=2000, mprior="uniform") ##standard coefficients based on exact likelihoods of the 100 best models: coef(bma1,exact=TRUE, std.coefs=TRUE) #suppressing userinteractive output, using a customized starting value, and not saving the best # ...models for only 19 observations (but 41 covariates) bma2 = bms(datafls[20:39,],burn=1000, iter=2000, nmodel=0, start.value=c(1,4,7,30), user.int=FALSE) coef(bma2) #MC3 chain with a hyperg prior (custom coefficient a=2.1), saving only the 20 best models, # ...and an alternative sampling procedure; putting a log entry to console every 1000th step bma3 = bms(datafls,burn=1000, iter=5000, nmodel=20, g="hyper=2.1", mcmc="rev.jump", logfile="",logstep=1000) image(bma3) #showing the coefficient signs of the 20 best models #enumerating with 10 covariates (= 1024 models), keeping the shrinkage factors # ...of the best 200 models bma4 = bms(datafls[,1:11],mcmc="enumerate",nmodel=200,g.stats=TRUE) #another enumeration example: with less than 15 covariates, enumeration is the default # ...of the best 200 models data(attitude) bma4b = bms(attitude) bma4c = bms(rating ~complaints + privileges + raises, data = attitude) #using an interaction sampler for two interaction terms dataint=datafls dataint=cbind(datafls,datafls$LifeExp*datafls$Abslat/1000, datafls$Protestants*datafls$Britdatafls$Muslim) names(dataint)[ncol(dataint)1]="LifeExp#Abslat" names(dataint)[ncol(dataint)]="Protestants#Brit#Muslim" bma5 = bms(X.data=dataint,burn=1000,iter=9000,start.value=0,mcmc="bd.int") density(bma5,reg="English") # plot posterior density for covariate "English" # a matrix as X.data argument bms(matrix(rnorm(1000),100,10)) # keeping a set of fixed regressors: bms(datafls, mprior.size=7, fixed.reg = c("PrScEnroll", "LifeExp", "GDP60")) # Note that mprior.size=7 means prior model size of 3 fixed to 4 'uncertain' regressors