qsample {INLA}R Documentation

Generate samples from a GMRF using the GMRFLib implementation

Description

This function generate samples from a GMRF using the GMRFLib implementation

Usage

inla.qsample(
  n = 1L,
  Q,
  b,
  mu,
  sample,
  constr,
  reordering = INLA::inla.reorderings(),
  seed = 0L,
  logdens = ifelse(missing(sample), FALSE, TRUE),
  compute.mean = ifelse(missing(sample), FALSE, TRUE),
  num.threads = NULL,
  selection = NULL,
  verbose = inla.getOption("verbose"),
  .debug = FALSE
)

Arguments

n

Number of samples. Only used if missing(sample)

Q

The precision matrix or a filename containing it.

b

The linear term

mu

The mu term

sample

A matrix of optional samples where each column is a sample. If set, then evaluate the log-density for each sample only.

constr

Optional linear constraints; see ?INLA::f and argument extraconstr

reordering

The type of reordering algorithm to be used for TAUCS; either one of the names listed in inla.reorderings() or the output from inla.qreordering(Q). The default is "auto" which try several reordering algorithm and use the best one for this particular matrix.

seed

Control the RNG. If seed=0L then GMRFLib will set the seed intelligently/at 'random', and this is and should be the default behaviour. If seed < 0L then the saved state of the RNG will be reused if possible, otherwise, GMRFLib will set the seed intelligently/at 'random'. If seed > 0L then this value is used as the seed for the RNG.

PLEASE NOTE1: If seed!=0 then the computations will run in serial mode, over-riding whatever is set in num.threads (a warning might be issued).

PLEASE NOTE2: If the PARDISO sparse matrix library is used, continuity of the samples with respect to small changes in the precision matrix, can be expected but is not guaranteed. If this feature is required, please use the TAUCS sparse matrix library.

logdens

If TRUE, compute also the log-density of each sample. Note that the output format then change.

compute.mean

If TRUE, compute also the (constrained) mean. Note that the output format then change.

num.threads

Maximum number of threads the inla-program will use, or as 'A:B' defining the number threads in the outer (A) and inner (B) layer for nested parallelism. seed!=0 requires serial comptuations.

selection

A vector of indices of each sample to return. NULL means return the whole sample. (Note that the log-density retured, is for the whole sample.) The use of selection cannot be combined with the use of sample.

verbose

Logical. Run in verbose mode or not.

.debug

Logical. Internal debug-mode.

Value

The log-density has form -1/2(x-mu)^T Q (x-mu) + b^T x

If logdens is FALSE, then inla.qsample returns the samples in a matrix, where each column is a sample. If logdens or compute.mean is TRUE, then a list with names sample, logdens and mean is returned. The samples are stored in the matrix sample where each column is a sample, and the log densities of each sample are stored as the vector logdens. The mean (include corrections for the constraints, if any) is store in the vector mean.

Author(s)

Havard Rue hrue@r-inla.org

Examples


 g = system.file("demodata/germany.graph", package="INLA")
 Q = inla.graph2matrix(g)
 diag(Q) = dim(Q)[1L]
 x = inla.qsample(10, Q)
 ## Not run: matplot(x)
 x = inla.qsample(10, Q, logdens=TRUE)
 ## Not run: matplot(x$sample)

 n = 3
 Q = diag(n)
 ns = 2

 ## sample and evaluate a sample
 x = inla.qsample(n, Q=Q, logdens=TRUE)
 xx = inla.qsample(Q=Q,  sample = x$sample)
 print(x$logdens - xx$logdens)

 ## the use of a constraint
 constr = list(A = matrix(rep(1, n), 1, n), e = 0)
 x = inla.qsample(n, Q=Q, constr=constr)
 print(constr$A %*% x)

 ## control the RNG (require serial mode)
 x = inla.qsample(n, Q=Q, seed = 123, num.threads="1:1")
 ## restart from same seed,  only sample 1
 xx = inla.qsample(n=1, Q=Q, seed = 123, num.threads="1:1")
 ## continue from the save state, sample the remaining 2
 xxx = inla.qsample(n=n-1, Q=Q, seed = -1, num.threads="1:1")
 ## should be 0
 print(x - cbind(xx, xxx))
[Package INLA version 25.10.19 Index]