Stores the output of Bayesian variable selection, as produced by
function modelSelection.
The class extends a list, so all usual methods for lists also work for
msfit objects, e.g. accessing elements, retrieving names etc.
Some additional methods are provided for printing information on screen,
computing posterior probabilities or sampling from the posterior of
regression coefficients, as indicated below.
Objects from the Class
Typically objects are automatically created by a call to modelSelection.
Alternatively, objects can be created by calls of the form
new("msfit",x) where x is a list with the adequate
elements (see slots).
Slots
The class extends a list with elements:
postSample
matrix with posterior samples for the model
indicator. postSample[i,j]==1
indicates that variable j was included in the model in the MCMC
iteration i
postOther
postOther
returns posterior samples for parameters other than the model
indicator, i.e. basically hyper-parameters.
If hyper-parameters were fixed in the model specification, postOther will be empty.
margpp
Marginal posterior probability for inclusion of each
covariate. This is computed by averaging marginal post prob for
inclusion in each Gibbs iteration, which is much more accurate than
simply taking colMeans(postSample)
.
postMode
Model with highest posterior probability amongst all those visited
postModeProb
Unnormalized posterior prob of posterior mode (log scale)
postProb
Unnormalized posterior prob of each visited model (log
scale)
coef
Estimated coefficients (via posterior mode) for highest
posterior probability model
family
Residual distribution, i.e. argument family
when calling modelSelection
p
Number of variables
Methods
show
signature(object = "msfit"): Displays general information about the object.
postProb
signature(object = "msfit"): Extracts
posterior model probabilities.
rnlp
signature(object = "msfit"): Obtain posterior
samples for regression coefficients.
Author(s)
David Rossell
References
Johnson VE, Rossell D. Non-Local Prior Densities for Default Bayesian Hypothesis Tests. Journal of the Royal Statistical Society B, 2010, 72, 143-170
Johnson VE, Rossell D. Bayesian model selection in high-dimensional
settings. Journal of the American Statistical Association, 107, 498:649-660.
providing 
.