excessProb.pb {BMAmevt} | R Documentation |
Double Monte-Carlo integration.
excessProb.pb(
post.sample,
Nmin.intern = 100,
precision = 0.05,
from = NULL,
to = NULL,
thin = 100,
displ = FALSE,
thres = rep(500, 5),
known.par = FALSE,
true.par
)
post.sample |
The posterior sample. |
Nmin.intern |
The minimum number of MC iteration in the internal loop (excess probability, conditional to a parameter). |
precision |
The desired precision for the internal MC estimate |
from |
Integer or |
to |
Integer or |
thin |
Thinning interval. |
displ |
logical. Should a plot be produced ? |
thres |
A multivariate threshold |
known.par |
Logical |
true.par |
The true parameter from which the data are issued. |
A list made of
A vector of estimated excess probabilities, one for each element of the thinned posterior sample.
the estimated threshold excess probability: mean estimate.
The estimated standard deviation of the mean estimate (where the Monte-Carlo error is neglected)
The estimated standard deviation of the posterior sample (where the Monte-Carlo error is neglected)
The three lower 0.1
quantiles of, respectively, the conditional mean estimates and of the upper and lower bounds of the Gaussian (centered) 80
% confidence intervals around the conditional estimates.
The three upper 0.9
quantiles
the mean estimate conditional to the true parameter: a vector of size three: the mean estimate , and the latter +/- the standard deviation of the estimate