mombf {mombf} | R Documentation |
mombf
computes moment Bayes factors to test whether a subset of
regression coefficients are equal to some user-specified value.
imombf
computes inverse moment Bayes factors.
zellnerbf
computes Bayes factors based on the Zellner-Siow
prior (used to build the moment prior).
mombf(lm1, coef, g, prior.mode, theta0, logbf = FALSE) imombf(lm1, coef, g, prior.mode, nu = 1, theta0 , method='adapt', nquant=100, B = 10^5)
lm1 |
Linear model fit, as returned by lm1 . |
coef |
Vector with indexes of coefficients to be
tested. e.g. coef==c(2,3)
and theta0==c(0,0) tests coef(lm1)[2]=coef(lm1)[3]=0 . |
g |
Vector with prior parameter values. See dmom and
dimom for details. |
prior.mode |
If specified, g is determined by calling
g2mode . |
theta0 |
Null value for the regression coefficients. Defaults to 0. |
logbf |
If logbf==TRUE the natural logarithm of the Bayes
factor is returned. |
nu |
Prior parameter for the inverse moment prior. See
dimom for details. Defaults to nu=1 , which Cauchy-like
tails. |
method |
Numerical integration method to compute the bivariate
integral (only used by imombf ).
For method=='adapt' , the inner integral is evaluated (via integrate ) at a series of
nquant quantiles of the residual variance posterior distribution, and then
averaged as described in Johnson (1992).
Set method=='MC' to use Monte Carlo integration. |
nquant |
Number of quantiles at which to evaluate the integral
for known sigma . Only used if method=='adapt' . |
B |
Number of Monte Carlo samples to estimate the inverse moment
Bayes factor. Only used if method=='MC' . |
These functions actually call momunknown
and
imomunknown
, but they have a simpler interface.
See dmom
and dimom
for details on the moment and inverse
moment priors.
The Zellner-Siow g-prior is given by dmvnorm(theta,theta0,n*g*V1).
mombf
returns the moment Bayes factor to compare the model where
theta!=theta0
with the null model where theta==theta0
. Large values favor the
alternative model; small values favor the null.
imombf
returns
inverse moment Bayes factors.
zellnerbf
returns Bayes factors based on the Zellner-Siow g-prior.
David Rossell
See http://rosselldavid.googlepages.com for technical reports. For details on the quantile integration, see Johnson, V.E. A Technique for Estimating Marginal Posterior Densities in Hierarchical Models Using Mixtures of Conditional Densities. Journal of the American Statistical Association, Vol. 87, No. 419. (Sep., 1992), pp. 852-860.
momunknown
,
imomunknown
and zbfunknown
for another interface to compute Bayes
factors. momknown
, imomknown
and zbfknown
to compute Bayes factors assuming that the dispersion parameter
is known, and for approximate Bayes factors for
GLMs. mode2g
for prior elicitation.
##compute Bayes factor for Hald's data data(hald) lm1 <- lm(hald[,1] ~ hald[,2] + hald[,3] + hald[,4] + hald[,5]) # Set g so that prior mode for standardized effect size is at 0.2 prior.mode <- .2^2 V <- summary(lm1)$cov.unscaled gmom <- mode2g(prior.mode,prior='Mom') gimom <- mode2g(prior.mode,prior='iMom') # Set g so that interval (-0.2,0.2) has 5% prior probability # (in standardized effect size scale) priorp <- .05; q <- .2 gmom <- c(gmom,priorp2g(priorp=priorp,q=q,prior='Mom')) gimom <- c(gmom,priorp2g(priorp=priorp,q=q,prior='iMom')) mombf(lm1,coef=2,g=gmom) #moment BF imombf(lm1,coef=2,g=gimom,B=10^5) #inverse moment BF zellnerbf(lm1,coef=2,g=1) #BF based on Zellner's g-prior