Usage
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)))Arguments
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
subject.x
The design matrix for subject-level predictors.
This can be NULL or of length 0 if no subject-level
predictors are present.
expected.subject.model.size
The expected number of non-zero
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
choice.x
The design matrix for choice-level 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
choice-level pre
expected.choice.model.size
The expected number of non-zero
coefficients in the choice-specific 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 choice-lev
max.flips
The maximum number of variable inclusion indicators
the sampler will attempt to sample each iteration. If
max.flips <= 0<="" code=""> then all indicators will be sampled.
nchoices
Tne number of potential response levels.
subject.dim
The number of potential predictors in the
subject-specific portion of the model.
choice.dim
The number of potential predictors in the
choice-specific portion of the model.