mlm.spike.slab.prior {BoomSpikeSlab}  R Documentation 
Creates a spike and slab prior for use with mlm.spike.
MultinomialLogitSpikeSlabPrior( response, subject.x, expected.subject.model.size = 1, choice.x = NULL, expected.choice.model.size = 1, max.flips = 1, nchoices = length(levels(response)), subject.dim = ifelse(is.null(subject.x), 0, ncol(subject.x)), choice.dim = ifelse(is.null(choice.x), 0, ncol(choice.x)))
response 
The response variable in the multinomial logistic regression. The response variable is optional if nchoices is supplied. If 'response' is provided then the prior means for the subject level intercpets will be chosen to match the empirical values of the response. 
subject.x 
The design matrix for subjectlevel predictors. This can be NULL or of length 0 if no subjectlevel predictors are present. 
expected.subject.model.size 
The expected number of nonzero coefficients – per choice level – in the subject specific portion of the model. All coefficients can be forced into the model by setting this to a negative number, or by setting it to be larger than the dimension of the subjectlevel predictors. 
choice.x 
The design matrix for choicelevel predictors. Each row of this matrix represents the characteristics of a choice in a choice occasion, so it takes 'nchoices' rows to encode one observation. This can be NULL or of length 0 if no choicelevel predictors are present. 
expected.choice.model.size 
The expected number of nonzero coefficients in the choicespecific portion of the model. All choice coefficients can be forced into the model by setting this to a negative number, or by setting it to be larger than the dimension of the choicelevel predictors (for a single response level). 
max.flips 
The maximum number of variable inclusion indicators
the sampler will attempt to sample each iteration. If

nchoices 
Tne number of potential response levels. 
subject.dim 
The number of potential predictors in the subjectspecific portion of the model. 
choice.dim 
The number of potential predictors in the choicespecific portion of the model. 
An object of class IndependentSpikeSlabPrior
, with
elements arranged as expected by mlm.spike
.
Steven L. Scott
Tuchler (2008), "Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling", Journal of Computational and Graphical Statistics, 17 76 – 94.