Description Usage Arguments Details Value Author(s) References See Also Examples
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).
1 2 3 4 |
lm1 |
Linear model fit, as returned by |
coef |
Vector with indexes of coefficients to be
tested. e.g. |
g |
Vector with prior parameter values. See |
prior.mode |
If specified, |
baseDensity |
Density upon which the Mom prior is
based. |
nu |
For |
theta0 |
Null value for the regression coefficients. Defaults to 0. |
logbf |
If |
method |
Numerical integration method to compute the bivariate
integral (only used by |
nquant |
Number of quantiles at which to evaluate the integral
for known |
B |
Number of Monte Carlo samples to estimate the T Mom and the inverse moment
Bayes factor. Only used in |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 | ##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 interval (-0.2,0.2) has 5% prior probability
# (in standardized effect size scale)
priorp <- .05; q <- .2
gmom <- priorp2g(priorp=priorp,q=q,prior='normalMom')
gimom <- 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
|
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