credsubs.level {credsubs}  R Documentation 
For each covariate point, credsubs.level
computes the maximum
credible level at which a conclusion may be drawn at each point, and
what that conclusion is.
credsubs.level(
params,
design = NULL,
FUN = function(x, params) { params %*% t(x) },
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,
z.store = c("ram", "recompute", "disk")
)
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 
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 
z.store 
How should certain intermediate computations be handled? See details. 
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.
By default (z.store = "ram"
), the maximum credible level computation
stores a potentially very large amount of intermediate computation results
in memory. If not enough memory is available, z.store = "disk"
uses the ff
package to store the intermediate results on disk,
which can still be fairly quick if the storage is fast (e.g. a local SSD).
Alternatively, z.store = "recompute"
discards the intermediate
results and recomputes whenever needed. This uses minimal memory, but
is usually the slowest option.
An object of class credsubs.level
, which contains:
level
A numeric vector indicating the maximum credible level at which a conclusion may be drawn at each covariate point.
sign
A numeric vector indicating the which credible subsets
of which each covariate point is a member at the
credible level indicated by level
. Exclusive
and inclusive: 1, inclusive only: 0, neither: 1.
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.
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.level(reg.surf.sample)
### 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)