SSS_hierarchical_prior_binomial {SequenceSpikeSlab} | R Documentation |
Compute marginal posterior probabilities (slab probabilities) that data points have
non-zero mean using the general hierarchical prior algorithm, but specialized to
the Beta[kappa,lambda]-binomial prior. This function is equivalent to calling
SSS_hierarchical_prior
with
logprior = lbeta(kappa+(0:n),lambda+n-(0:n)) - lbeta(kappa,lambda) + lchoose(n,0:n),
but more convenient when using the Beta[kappa,lambda]-binomial prior and with a
minor interior optimization that avoids calculating the choose explicitly.
Description
Compute marginal posterior probabilities (slab probabilities) that data points have
non-zero mean using the general hierarchical prior algorithm, but specialized to
the Beta[kappa,lambda]-binomial prior. This function is equivalent to calling
SSS_hierarchical_prior
with
logprior = lbeta(kappa+(0:n),lambda+n-(0:n)) - lbeta(kappa,lambda) + lchoose(n,0:n),
but more convenient when using the Beta[kappa,lambda]-binomial prior and with a
minor interior optimization that avoids calculating the choose explicitly.
Usage
SSS_hierarchical_prior_binomial(
log_phi_psi,
kappa,
lambda,
show_progress = TRUE
)
Arguments
log_phi_psi |
List {logphi, logpsi} containing two vectors of the same length n
that represent a preprocessed version of the data. logphi and logpsi should contain
the logs of the phi and psi densities of the data points, as produced for instance
by |
kappa |
First parameter of the beta-distribution |
lambda |
Second parameter of the beta-distribution |
show_progress |
Boolean that indicates whether to show a progress bar |
Value
Returns a vector with marginal posterior slab probabilities that x[i]
has
non-zero mean for i=1,...,n
.