pc.cormat {INLA} | R Documentation |
Functions to evaluate and sample from the PC prior for a correlation matrix.
inla.pc.cormat.dim2p(dim) inla.pc.cormat.p2dim(p) inla.pc.cormat.theta2R(theta) inla.pc.cormat.R2theta(R) inla.pc.cormat.r2R(r) inla.pc.cormat.R2r(R) inla.pc.cormat.r2theta(r) inla.pc.cormat.theta2r(theta) inla.pc.cormat.permute(R) inla.pc.cormat.rtheta(n=1, p, lambda = 1) inla.pc.cormat.dtheta(theta, lambda = 1, log = FALSE)
dim |
The dimension of |
p |
The dimension the correlation matrix |
theta |
A vector of parameters for the correlation matrix |
r |
The off diagonal elements of a correlation matrix |
R |
A correlation matrix |
n |
Number of observations |
lambda |
The rate parameter in the prior |
log |
Logical. Return the density in natural or log-scale. |
The parameterisation of a correlation matrix of dimension p
has dim
parameters: theta
which are in the interval -pi to pi.
The alternative parameterisation is through the off-diagonal elements r
of the
correlation matrix R
. The functions inla.pc.cormat.<A>2<B>
convert between
parameterisations <A>
to parameterisations <B>
, where both
<A>
and <B>
are one of theta
, r
and R
,
and p
and dim
.
inla.pc.cormat.rtheta
generate samples from the prior, returning a matrix
where each row is a sample of theta
.
inla.pc.cormat.dtheta
evaluates the density of theta
.
inla.pc.cormat.permute
randomly permutes a correlation matrix,
which is useful if an exchangable sample of a correlation matrix is required.
Havard Rue hrue@r-inla.org
p = 4 print(paste("theta has length", inla.pc.cormat.p2dim(p))) theta = inla.pc.cormat.rtheta(n=1, p=4, lambda = 1) print("sample theta:") print(theta) print(paste("log.dens", inla.pc.cormat.dtheta(theta, log=TRUE))) print("r:") r = inla.pc.cormat.theta2r(theta) print(r) print("A sample from the non-exchangable prior, R:") R = inla.pc.cormat.r2R(r) print(R) print("A sample from the exchangable prior, R:") R = inla.pc.cormat.permute(R) print(R)