credsubs {credsubs}  R Documentation 
credsubs
returns a credible subset pair over a finite set of
covariate points given either a sample from the posterior of the
regression surface or a function FUN(x, params)
and a sample from
the posterior of the parameters.
credsubs(
params,
design = NULL,
FUN = function(x, params) { params %*% t(x) },
cred.level = 0.95,
threshold = 0,
method = c("asymptotic", "quantile"),
step.down = TRUE,
sides = c("both", "exclusive", "inclusive"),
est.FUN = mean,
var.FUN = sd,
point.estimate = NULL,
track = numeric(0),
verbose = FALSE
)
params 
A numeric matrix whose rows are draws from the posterior distribution of either the regression surface or the parameter vector. 
design 
(Optional) A numeric matrix whose rows are covariate points over which the band is to be constructed. 
FUN 
(Optional) a function of the form 
cred.level 
Numeric; the credible level. 
threshold 
Numeric; the value of 
method 
Either "asymptotic" (default) or "quantile"; see details. 
step.down 
Logical (default 
sides 
One of "both" (default), "exclusive", or "inclusive". Which bounds should be constructed? 
est.FUN 
The function used to produce estimates of the regression
surface. Default is 
var.FUN 
The function used to quantify the variability of the
regression surface posterior. Default is 
point.estimate 
If not null, replaces the mean and sets the reference
around which the standard error is computed.
Useful for bootstrapping methods.
Treated as a row of the 
track 
A numeric vector of indices indicating which rows (default none)
of the design matrix should have the sample of the corresponding

verbose 
Logical (default 
If design is NULL (default), it is taken to be the identity matrix of dimension ncol(params), so that the rows of params are treated as draws from the posterior FUN(x, params).
The 'asymptotic' method assumes that the marginal posteriors of the FUN(x, params) are asymptotically normal and is usually significantly faster and less memoryintensive than the 'quantile' method, which makes no such assumption.
An object of class credsubs
, which contains:
exclusive
A logical vector indicating membership in the exclusive credible subset.
inclusive
A logical vector indicating membership in the inclusive credible subset.
cred.level
As provided.
threshold
As provided.
method
As provided.
step.down
As provided.
sides
As provided.
est
Posterior estimate of the regression surface.
est.FUN
As provided.
var
Summary of posterior variability of the regression surface.
var.FUN
As provided.
W
An estimate of the extremal errors.
W.crit
The critical quantile of W.
trace
The posterior samples of the regression surface
indicated by the track
argument.
### Sample from regression surface posterior
reg.surf.sample < matrix(rnorm(1000, mean=1:10), ncol=2, byrow=TRUE)
credsubs(reg.surf.sample, cred.level=0.80)
### Parametric case
design < cbind(1, 1:10)
params < matrix(rnorm(200, mean=1:2), ncol=2, byrow=TRUE)
credsubs(params, design)
### With custom function
params.sd < cbind(1 / rgamma(100, 1), params)
FUN.sd < function(x, params) { params[, 1] %*% t(x) / params[, 1] }
credsubs(params.sd, design, FUN.sd, threshold=1)