Given a single value or a vector of weights (i.e. prior probabilities that the parameter is nonzero) and sampling standard deviations (sd equals 1 for Cauchy prior), find the corresponding threshold(s) under the specified prior.
tfromw(w, s = 1, prior = "laplace", bayesfac = FALSE, a = 0.5) laplace.threshzero(x, s = 1, w = 0.5, a = 0.5)
cauchy.threshzero(z, w)
Parameter value passed to laplace.threshzero objective
function.
Prior weight or vector of weights.
A single value or a vector of standard deviations if the
Laplace prior is used. If w is a vector, must have the same
length as w. Ignored if Cauchy prior is used.
Specification of prior to be used; can be
"cauchy" or "laplace".
Specifies whether Bayes factor threshold should be used instead of posterior median threshold.
Scale factor if Laplace prior is used. Ignored if Cauchy prior is used.
The putative threshold vector for cauchy.threshzero.
The value or vector of values of the estimated threshold(s).
The Bayes factor method uses a threshold such that the posterior
probability of zero is exactly half if the data value is equal to the
threshold. If bayesfac is set to FALSE (the default) then
the threshold is that of the posterior median function given the data
value.
The routine carries out a binary search over each component of an
appropriate vector function, using the routine vecbinsolv.
For the posterior median threshold, the function to be zeroed is
laplace.threshzero or cauchy.threshzero.
For the Bayes factor threshold, the corresponding functions are
beta.laplace or beta.cauchy.
# NOT RUN {
tfromw(c(0.05, 0.1), s = 1)
tfromw(c(0.05, 0.1), prior = "cauchy", bayesfac = TRUE)
# }
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