R/spde3.R
In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion
## 'spde3' model functions
## External: inla.spde.precision.inla.spde3
## External: inla.spde.result!inla.spde3 inla.spde3.generic
## External: inla.spde3.matern param2.matern.orig
## External: inla.spde3.matern.sd.basis inla.spde3.models
## External: inla.spde3.precision inla.spde3.result
## External: inla.spde3.theta2phi0 inla.spde3.theta2phi1
## External: inla.spde3.theta2phi2 inla.spde3.theta2phi3
##
## Internal: inla.internal.spde3.matern.B.tau
## Internal: inla.internal.test.spde3.sd.basis
inla.spde3.generic <-
function(M0, M1, M2, M3, B0, B1, B2, B3, theta.mu, theta.Q,
transform = c("identity","log","logit","shiftedlog","oldlogit"),
theta.initial = theta.mu,
fixed = rep(FALSE, length(theta.mu)),
theta.fixed = theta.initial[fixed],
BLC = cbind(0.0, diag(nrow=length(theta.mu))),
...)
{
warning("The spde3 interface is still experimental and may be changed without notice.")
transform = match.arg(transform)
if (!inla.is.matrix(M0)) stop("M0 must be of a matrix type.")
if (!inla.is.matrix(M1)) stop("M1 must be of a matrix type.")
if (!inla.is.matrix(M2)) stop("M2 must be of a matrix type.")
if (!inla.is.matrix(M3)) stop("M3 must be of a matrix type.")
M0 = inla.as.dgTMatrix(M0)
M1 = inla.as.dgTMatrix(M1)
M2 = inla.as.dgTMatrix(M2)
M3 = inla.as.dgTMatrix(M3)
n.spde = nrow(M0)
n.theta = length(theta.mu)
spde = (list(model = "generic",
n.spde = n.spde,
n.theta = n.theta,
param.inla = list(),
f = list()
))
class(spde) = c("inla.spde3", "inla.spde", "inla.model.class")
B0 = inla.spde.homogenise_B_matrix(B0, n.spde, n.theta)
B1 = inla.spde.homogenise_B_matrix(B1, n.spde, n.theta)
B2 = inla.spde.homogenise_B_matrix(B2, n.spde, n.theta)
B3 = inla.spde.homogenise_B_matrix(B3, n.spde, n.theta)
BLC = inla.spde.homogenise_B_matrix(BLC, nrow(BLC), n.theta)
param.inla =
list(n = n.spde,
n.theta = n.theta,
M0=M0, M1=M1, M2=M2, M3=M3,
B0=B0, B1=B1, B2=B2, B3=B3, BLC=BLC,
theta.mu=theta.mu, theta.Q=theta.Q,
transform=transform,
theta.initial=theta.initial,
fixed=fixed,
theta.fixed=theta.fixed)
## TODO: Check dimension consistency of inputs
## If any "fixed", move information from B[,1+(1:n.theta)] into
## B[,1], theta.mu, and theta.Q
## NOTE: Should this be in the f function instead?
## No, the information is also needed elsewhere.
if (any(fixed)) {
param.inla$theta.initial = theta.initial[!fixed]
param.inla$B0[,1] =
B0[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$B1[,1] =
B1[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$B2[,1] =
B2[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$B3[,1] =
B3[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$BLC[,1] =
BLC[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$B0 = param.inla$B0[,c(TRUE, !fixed), drop=FALSE]
param.inla$B1 = param.inla$B1[,c(TRUE, !fixed), drop=FALSE]
param.inla$B2 = param.inla$B2[,c(TRUE, !fixed), drop=FALSE]
param.inla$B3 = param.inla$B3[,c(TRUE, !fixed), drop=FALSE]
param.inla$BLC = param.inla$BLC[,c(TRUE, !fixed), drop=FALSE]
param.inla$theta.Q = param.inla$theta.Q[!fixed, !fixed, drop=FALSE]
if (!all(fixed)) {
param.inla$theta.mu =
theta.mu - solve(param.inla$theta.Q,
theta.Q[!fixed, fixed, drop=FALSE]
%*% (theta.fixed[fixed]-theta.mu[fixed]))
} else { ## All fixed.
param.inla$theta.mu = as.vector(matrix(0.0, 0, 1))
}
}
## Remove all BLC rows that would produce point masses, which is
## not supported by inla:
if (n.theta==0) {
param.inla$BLC = matrix(0,0,1)
} else {
param.inla$BLC =
param.inla$BLC[rowSums(abs(param.inla$BLC[,-1,drop=FALSE]))>0,,
drop=FALSE]
}
spde$param.inla = param.inla
## NOTE: Should the prefix be set inside the f function instead?
## Yes, it should.
spde.prefix = inla.fmesher.make.prefix(NULL, NULL)
spde$f = (list(model="spde3",
spde3.prefix=spde.prefix,
n=n.spde,
spde3.transform=transform,
hyper.default =
(list(theta1 =
list(prior="mvnorm",
param=(c(param.inla$theta.mu,
as.matrix(param.inla$theta.Q)))
)
))
))
if (n.theta>0)
for (k in 1:n.theta) {
eval(parse(text=
paste("spde$f$hyper.default$theta", k,
"$initial = param.inla$theta.mu[k]", sep="")))
}
## NOTE: Should this be in the f function instead?
## Yes, it should.
fmesher.write(inla.affirm.double(spde$param.inla$M0), spde.prefix, "M0")
fmesher.write(inla.affirm.double(spde$param.inla$M1), spde.prefix, "M1")
fmesher.write(inla.affirm.double(spde$param.inla$M2), spde.prefix, "M2")
fmesher.write(inla.affirm.double(spde$param.inla$M3), spde.prefix, "M3")
fmesher.write(inla.affirm.double(spde$param.inla$B0), spde.prefix, "B0")
fmesher.write(inla.affirm.double(spde$param.inla$B1), spde.prefix, "B1")
fmesher.write(inla.affirm.double(spde$param.inla$B2), spde.prefix, "B2")
fmesher.write(inla.affirm.double(spde$param.inla$B3), spde.prefix, "B3")
## Only write BLC if it is non-empty
if (nrow(spde$param.inla$BLC)>0)
fmesher.write(inla.affirm.double(spde$param.inla$BLC),
spde.prefix, "BLC")
return(spde)
}
inla.internal.spde3.matern.B.tau =
function(precision.fcn, n.theta, delta = 1e-3, ...)
{
theta.0 = rep(0, n.theta)
Q.0 = precision.fcn(theta.0, ...)
logv.0 = log(diag(inla.qinv(Q.0)))
B.tau = matrix(0, nrow(Q.0), 1L+n.theta)
for (k in seq_len(n.theta)) {
theta = theta.0
theta[k] = theta[k] + delta
Q = precision.fcn(theta, ...)
logv = log(diag(inla.qinv(Q)))
dlogv = (logv-logv.0)/delta
B.tau[, k+1] = dlogv/2
}
B.tau[,1L] = logv.0/2
return(B.tau)
}
inla.spde3.matern.sd.basis =
function(mesh, B.sd, B.range, method=1,
local.offset.compensation=FALSE,
alpha=2, ...)
{
p.sd = ncol(B.sd)-1L
p.kappa = ncol(B.range)-1L
d = inla.ifelse(inherits(mesh, "inla.mesh"), 2, 1)
nu = alpha-d/2
nu.nominal = max(0.5, nu)
alpha.nominal = max(nu.nominal+d/2, alpha)
n.spde = inla.ifelse(d==2, mesh$n, mesh$m)
## log(kappa) = 0.5*log(8*nu) - log(range)
B.kappa = (cBind(log(8*nu.nominal)/2 - B.range[,1],
-B.range[,-1,drop=FALSE] ))
if (method==1) {
spde =
inla.spde3.matern(mesh, B.tau=as.matrix(0), B.kappa=B.kappa, ...)
B.tau =
inla.internal.spde3.matern.B.tau(
precision.fcn=
function(theta, spde) {
return(inla.spde3.precision(spde, theta=theta))
},
n.theta = p.kappa,
spde = spde)
if (!local.offset.compensation) {
B.tau[,1] = rep(mean(B.tau[,1]), spde$n.spde)
}
} else {
spde =
inla.spde3.matern(mesh,
B.tau=as.matrix(0),
B.kappa=B.kappa[,1,drop=FALSE],
alpha=alpha, ...)
lvar.0 =
log(diag(inla.qinv(inla.spde3.precision(spde, theta=rep(0,0)))))
if (!local.offset.compensation) {
lvar.0 = rep(mean(lvar.0), spde$n.spde)
}
B.tau = -B.kappa*nu
B.tau[,1] = lvar.0/2
}
log.var.scaling = lgamma(nu.nominal)-lgamma(alpha.nominal)-log(4*pi)*d/2
B.tau =
cBind(B.tau[,1]-B.sd[,1],
-B.sd[,-1,drop=FALSE],
B.tau[,-1,drop=FALSE])
B.kappa =
cBind(B.kappa[,1],
matrix(0, n.spde, p.sd),
B.kappa[,-1,drop=FALSE])
return(list(B.tau=B.tau, B.kappa=B.kappa))
}
inla.internal.test.spde3.sd.basis = function (k=1, dth=0.1, r=1000, globe=25, compensate=FALSE) {
alpha=2
mesh = inla.mesh.create(globe=globe)
B.common = inla.mesh.basis(mesh, "b.spline", n=3)
B.common = cBind(B.common, B.common[,1]*(mesh$loc[,3]>0))
B.common[,1] = B.common[,1]*(mesh$loc[,3]<=0)
B.sd = cBind(0, B.common)
B.range = cBind(log(r/6370), B.common)
sd.basis0 = inla.spde3.matern.sd.basis(mesh, B.sd=B.sd, B.range=B.range, method=0, alpha=alpha, local.offset.compensation=compensate)
sd.basis1 = inla.spde3.matern.sd.basis(mesh, B.sd=B.sd, B.range=B.range, method=1, alpha=alpha, local.offset.compensation=compensate)
spde0 =
inla.spde3.matern(mesh, alpha=alpha,
B.tau=sd.basis0$B.tau,
B.kappa=sd.basis0$B.kappa)
spde1 =
inla.spde3.matern(mesh, alpha=alpha,
B.tau=sd.basis1$B.tau,
B.kappa=sd.basis1$B.kappa)
th0 = c(0,0,0,0,0,0,0,0)
th1 = th0
th1[k] = th0[k]+dth
v0.0=diag(inla.qinv(inla.spde3.precision(spde0,theta=th0)))
v0.1=diag(inla.qinv(inla.spde3.precision(spde0,theta=th1)))
v1.0=diag(inla.qinv(inla.spde3.precision(spde1,theta=th0)))
v1.1=diag(inla.qinv(inla.spde3.precision(spde1,theta=th1)))
op=par(mfrow=c(2,2))
on.exit(par(op))
plot(asin(mesh$loc[,3]),rowSums(sd.basis0$B.kappa[,1+k,drop=FALSE]), pch=20,
ylim=(range(c(0, range(rowSums(sd.basis0$B.kappa[,1+k,drop=FALSE])),
range(-rowSums(sd.basis0$B.tau[,1+k,drop=FALSE])),
range(-rowSums(sd.basis1$B.tau[,1+k,drop=FALSE]))))),
main="Basis functions")
points(asin(mesh$loc[,3]),-rowSums(sd.basis0$B.tau[,1+k,drop=FALSE]),col=2,pch=20)
points(asin(mesh$loc[,3]),-rowSums(sd.basis1$B.tau[,1+k,drop=FALSE]),col=4,pch=20)
legend("bottomright",
legend=paste("Method", 0:1),
col=c(2,4), pch=20)
plot(asin(mesh$loc[,3]),v0.1, col=2,pch=20,
ylim=range(c(0,range(v0.0),range(v1.0),range(v0.1),range(v1.1))),
main="Absolute variances")
points(asin(mesh$loc[,3]),v1.1, col=4,pch=20)
points(asin(mesh$loc[,3]),v0.0, col=2,pch=1)
points(asin(mesh$loc[,3]),v1.0, col=4,pch=1)
legend("bottomright",
legend=paste("Method", 0:1),
col=c(2,4), pch=20)
plot(asin(mesh$loc[,3]),v0.1-v0.0, col=2,pch=20,
ylim=range(c(0,range(v0.1-v0.0),range(v1.1-v1.0))),
main="Absolute variance error")
points(asin(mesh$loc[,3]),v1.1-v1.0, col=4,pch=20)
legend("bottomright",
legend=paste("Method", 0:1),
col=c(2,4), pch=20)
plot(asin(mesh$loc[,3]),v0.1/v0.0-1, col=2,pch=20,
ylim=range(c(0,range(v0.1/v0.0-1),range(v1.1/v1.0-1))),
main="Relative variance error")
points(asin(mesh$loc[,3]),v1.1/v1.0-1, col=4,pch=20)
legend("bottomright",
legend=paste("Method", 0:1),
col=c(2,4), pch=20)
return(environment())
}
param2.matern.orig =
function(mesh,
alpha=2,
B.tau = matrix(c(0,1,0),1,3),
B.kappa = matrix(c(0,0,1),1,3),
prior.variance.nominal = 1,
prior.range.nominal = NULL,
prior.tau = NULL,
prior.kappa = NULL,
theta.prior.mean = NULL,
theta.prior.prec = 0.1)
{
## NOTE: For d==1, degree==2, the B.* must be given per basis
## function, not per knot.
inla.require.inherits(mesh, c("inla.mesh", "inla.mesh.1d"), "'mesh'")
if (is.null(B.tau))
stop("B.tau must not be NULL.")
if (is.null(B.kappa))
stop("B.kappa must not be NULL.")
is.stationary = (nrow(B.kappa)==1) && (nrow(B.tau)==1)
d = inla.ifelse(inherits(mesh, "inla.mesh"), 2, 1)
nu = alpha-d/2
nu.nominal = max(0.5, nu)
alpha.nominal = max(nu.nominal+d/2, alpha)
n.spde = inla.ifelse(d==2, mesh$n, mesh$m)
n.theta = ncol(B.kappa)-1L
B.kappa = inla.spde.homogenise_B_matrix(B.kappa, n.spde, n.theta)
B.tau = inla.spde.homogenise_B_matrix(B.tau, n.spde, n.theta)
B.prec =
cbind(+ lgamma(alpha.nominal) - lgamma(nu.nominal)
+ (d/2)*log(4*pi) + 2*nu.nominal*B.kappa[,1]
+ 2*B.tau[,1],
2*nu.nominal*B.kappa[,-1,drop=FALSE]
+ 2*B.tau[,-1,drop=FALSE] )
if (is.stationary) {
B.tau = B.tau[1,,drop=FALSE]
B.kappa = B.kappa[1,,drop=FALSE]
B.prec = B.prec[1,,drop=FALSE]
}
B.variance = -B.prec
B.range =
cbind(0.5*log(8*nu.nominal)-B.kappa[,1],
-B.kappa[,-1,drop=FALSE])
if (n.theta>0) {
B.theta = cbind(0,diag(1, n.theta))
rownames(B.theta) <- rownames(B.theta, do.NULL=FALSE, prefix="theta.")
} else {
B.theta = NULL
}
rownames(B.tau) <- rownames(B.tau, do.NULL=FALSE, prefix="tau.")
rownames(B.kappa) <- rownames(B.kappa, do.NULL=FALSE, prefix="kappa.")
rownames(B.variance) <-
rownames(B.variance, do.NULL=FALSE, prefix="variance.nominal.")
rownames(B.range) <-
rownames(B.range, do.NULL=FALSE, prefix="range.nominal.")
BLC = rbind(B.theta, B.tau, B.kappa, B.variance, B.range)
## Construct priors.
if (is.null(theta.prior.prec)) {
theta.prior.prec = diag(0.1, n.theta, n.theta)
} else {
theta.prior.prec = as.matrix(theta.prior.prec)
if (ncol(theta.prior.prec) == 1) {
theta.prior.prec =
diag(as.vector(theta.prior.prec), n.theta, n.theta)
}
if ((nrow(theta.prior.prec) != n.theta) ||
(ncol(theta.prior.prec) != n.theta)) {
stop(paste("Size of theta.prior.prec is (",
paste(dim(theta.prior.prec), collapse=",", sep=""),
") but should be (",
paste(c(n.theta, n.theta), collapse=",", sep=""),
")."))
}
}
if (is.null(theta.prior.mean)) {
if (is.null(prior.range.nominal)) {
mesh.range =
inla.ifelse(d==2,
(max(c(diff(range(mesh$loc[,1])),
diff(range(mesh$loc[,2])),
diff(range(mesh$loc[,3]))
))),
diff(mesh$interval))
prior.range.nominal = mesh.range*0.2
}
if (is.null(prior.kappa)) {
prior.kappa = sqrt(8*nu.nominal)/prior.range.nominal
}
if (is.null(prior.tau)) {
prior.tau =
sqrt(gamma(nu.nominal)/gamma(alpha.nominal)/
(4*pi*prior.kappa^(2*nu.nominal)*prior.variance.nominal))
}
if (n.theta>0) {
theta.prior.mean =
qr.solve(rbind(B.tau[,-1,drop=FALSE], B.kappa[,-1,drop=FALSE]),
c(log(prior.tau) - B.tau[,1],
log(prior.kappa) - B.kappa[,1]))
} else {
theta.prior.mean = rep(0, n.theta) ## Empty vector
}
}
param =
list(is.stationary=is.stationary,
B.tau=B.tau, B.kappa=B.kappa, BLC=BLC,
theta.prior.mean=theta.prior.mean,
theta.prior.prec=theta.prior.prec)
return(param)
}
inla.spde3.matern =
function(mesh,
alpha=2,
param = NULL,
constr = FALSE,
extraconstr.int = NULL,
extraconstr = NULL,
fractional.method = c("parsimonious", "null"),
B.tau = matrix(c(0,1,0),1,3),
B.kappa = matrix(c(0,0,1),1,3),
prior.variance.nominal = 1,
prior.range.nominal = NULL,
prior.tau = NULL,
prior.kappa = NULL,
theta.prior.mean = NULL,
theta.prior.prec = 0.1)
{
inla.require.inherits(mesh, c("inla.mesh", "inla.mesh.1d"), "'mesh'")
fractional.method = match.arg(fractional.method)
if (is.null(param)) {
param =
param2.matern.orig(
mesh, alpha,
B.tau, B.kappa,
prior.variance.nominal,
prior.range.nominal,
prior.tau,
prior.kappa,
theta.prior.mean,
theta.prior.prec)
} else {
deprecated =
!c(missing(B.tau), missing(B.kappa),
missing(prior.variance.nominal),
missing(prior.range.nominal),
missing(prior.tau), missing(prior.kappa),
missing(theta.prior.mean), missing(theta.prior.prec))
deprecated =
c("B.tau", "B.kappa",
"prior.variance.nominal",
"prior.range.nominal",
"prior.tau", "prior.kappa",
"theta.prior.mean", "theta.prior.prec")[deprecated]
if (length(deprecated) > 0) {
warning(paste("'param' specified; ",
"Ignoring deprecated parameter(s) ",
paste(deprecated, collapse=", "), ".", sep=""))
}
}
d = inla.ifelse(inherits(mesh, "inla.mesh"), 2, 1)
nu = alpha-d/2
nu.nominal = max(0.5, nu)
alpha.nominal = max(nu.nominal+d/2, alpha)
n.spde = inla.ifelse(d==2, mesh$n, mesh$m)
n.theta = ncol(B.kappa)-1L
if (d==2) {
fem =
inla.fmesher.smorg(mesh$loc,
mesh$graph$tv,
fem=2,
output=list("c0", "c1", "g1", "g2"))
} else {
fem = inla.mesh.1d.fem(mesh)
if (mesh$degree==2) {
fem$c0 = fem$c1 ## Use higher order matrix.
}
}
if (alpha==2) {
B.phi0 = param$B.tau
B.phi1 = 2*param$B.kappa
M0 = fem$c0
M1 = fem$g1
M2 = fem$g2
} else if (alpha==1) {
B.phi0 = param$B.tau
B.phi1 = param$B.kappa
M0 = fem$c0
M1 = fem$g1*0
M2 = fem$g1
} else if (!param$is.stationary) {
stop("Non-stationary Matern with fractional alpha is not implemented.")
} else if ((alpha<2) && (alpha>1)) {
if (fractional.method == "parsimonious") {
lambda = alpha-floor(alpha)
b = matrix(c(1,0,0, 1,1,0, 1,2,1),3,3) %*%
solve(matrix(1/(c(4:2, 3:1, 2:0)+lambda), 3, 3),
1/(c(4:2)+lambda-alpha))
} else if (fractional.method == "null") {
b = c(1,alpha,alpha*(alpha-1)/2)
} else {
stop(paste("Unknown fractional.method '", fractional.method,
"'.", sep=""))
}
B.phi0 = param$B.tau + (alpha-2)*param$B.kappa
B.phi1 = 2*param$B.kappa
M0 = fem$c0*b[1]
M1 = fem$g1*b[2]/2
M2 = fem$g2*b[3]
} else if ((alpha<1) && (alpha>0)) {
if (fractional.method == "parsimonious") {
lambda = alpha-floor(alpha)
b = matrix(c(1,0,1,1),2,2) %*%
solve(matrix(1/(c(2:1, 1:0)+lambda), 2, 2),
1/(c(2:1)+lambda-alpha))
} else if (fractional.method == "null") {
b = c(1,alpha)
} else {
stop(paste("Unknown fractional.method '", fractional.method,
"'.", sep=""))
}
B.phi0 = param$B.tau + (alpha-1)*param$B.kappa
B.phi1 = param$B.kappa
M0 = fem$c0*b[1]
M1 = fem$g1*0
M2 = fem$g1*b[2]
} else {
stop(paste("Unsupported alpha value (", alpha,
"). Supported values are 0 < alpha <= 2", sep=""))
}
spde =
inla.spde3.generic(M0=M0, M1=M1, M2=M2,
B0=B.phi0, B1=B.phi1, B2=1,
theta.mu = param$theta.prior.mean,
theta.Q = param$theta.prior.prec,
transform = "identity",
BLC = param$BLC)
spde$model = "matern"
spde$BLC = param$BLC
if (constr || !is.null(extraconstr.int)) {
if (constr) {
A.constr = matrix(colSums(fem$c1), 1, n.spde)
e.constr = 0
} else {
A.constr = matrix(numeric(0), 0, n.spde)
e.constr = c()
}
if (!is.null(extraconstr.int)) {
A.constr =
rBind(A.constr,
matrix(extraconstr.int$A %*% fem$c1,
nrow(extraconstr.int$A), n.spde))
e.constr = c(e.constr, extraconstr.int$e)
}
if (!is.null(extraconstr)) {
A.constr = rBind(A.constr, extraconstr$A)
e.constr = c(e.constr, extraconstr$e)
}
spde$f$constr = FALSE
spde$f$extraconstr = list(A=A.constr, e=e.constr)
} else if (!is.null(extraconstr)) {
spde$f$constr = FALSE
spde$f$extraconstr = extraconstr
}
return(invisible(spde))
}
param2.iheat <- function(mesh.space,
mesh.time,
gamma.E = NULL,
alpha.E = NULL,
theta.prior.mean = NULL,
theta.prior.prec = NULL)
{
return(list(gamma.E=gamma.E,
alpha.E=alpha.E,
theta.prior.mean=theta.prior.mean,
theta.prior.prec=theta.prior.prec))
}
inla.spde3.iheat =
function(mesh.space,
mesh.time,
order=2,
param = NULL,
constr = FALSE,
extraconstr.int = NULL,
extraconstr = NULL)
{
## order=1:
## gamma.t dot(u) - Laplacian (u) = E
##
## order=2:
## gamma.t dot(u) - Laplacian (u) = v
## gamma.t dot(v) - Laplacian (v) = E
##
## (1 - gamma.E Laplacian)^(alpha.E/2) E = W/sqrt(gamma.s)
## gamma.E and alpha.E known
inla.require.inherits(mesh.space, c("inla.mesh", "inla.mesh.1d"),
"'mesh.space'")
inla.require.inherits(mesh.time, c("inla.mesh.1d"),
"'mesh.time'")
if (is.null(param)) {
param =
param2.iheat(
mesh.space, mesh.time,
theta.prior.mean,
theta.prior.prec)
}
d.space = inla.ifelse(inherits(mesh.space, "inla.mesh.1d"), 1, 2)
d.time = 1
n.space = inla.ifelse(d.space==2, mesh.space$n, mesh.space$m)
n.time = mesh.time$m
n.spde = n.space*n.time
n.theta = 2L ## gamma.s, gamma.t
if (d.space==2) {
fem.space = inla.mesh.fem(mesh.space$loc, 2)
} else {
fem.space = inla.mesh.1d.fem(mesh.space)
if (mesh.space$degree==2) {
fem.space$c0 = fem.space$c1 ## Use higher order matrix.
}
}
fem.time = inla.mesh.1d.fem(mesh.time)
if (mesh.time$degree==2) {
fem.time$c0 = fem.time$c1 ## Use higher order matrix.
}
## TODO: the rest
if (alpha==2) {
B.phi0 = param$B.tau
B.phi1 = 2*param$B.kappa
M0 = fem$c0
M1 = fem$g1
M2 = fem$g2
} else if (alpha==1) {
B.phi0 = param$B.tau
B.phi1 = param$B.kappa
M0 = fem$c0
M1 = fem$g1*0
M2 = fem$g1
} else if (!param$is.stationary) {
stop("Non-stationary Matern with fractional alpha is not implemented.")
} else if ((alpha<2) && (alpha>1)) {
if (fractional.method == "parsimonious") {
lambda = alpha-floor(alpha)
b = matrix(c(1,0,0, 1,1,0, 1,2,1),3,3) %*%
solve(matrix(1/(c(4:2, 3:1, 2:0)+lambda), 3, 3),
1/(c(4:2)+lambda-alpha))
} else if (fractional.method == "null") {
b = c(1,alpha,alpha*(alpha-1)/2)
} else {
stop(paste("Unknown fractional.method '", fractional.method,
"'.", sep=""))
}
B.phi0 = param$B.tau + (alpha-2)*param$B.kappa
B.phi1 = 2*param$B.kappa
M0 = fem$c0*b[1]
M1 = fem$g1*b[2]/2
M2 = fem$g2*b[3]
} else if ((alpha<1) && (alpha>0)) {
if (fractional.method == "parsimonious") {
lambda = alpha-floor(alpha)
b = matrix(c(1,0,1,1),2,2) %*%
solve(matrix(1/(c(2:1, 1:0)+lambda), 2, 2),
1/(c(2:1)+lambda-alpha))
} else if (fractional.method == "null") {
b = c(1,alpha)
} else {
stop(paste("Unknown fractional.method '", fractional.method,
"'.", sep=""))
}
B.phi0 = param$B.tau + (alpha-1)*param$B.kappa
B.phi1 = param$B.kappa
M0 = fem$c0*b[1]
M1 = fem$g1*0
M2 = fem$g1*b[2]
} else {
stop(paste("Unsupported alpha value (", alpha,
"). Supported values are 0 < alpha <= 2", sep=""))
}
spde =
inla.spde3.generic(M0=M0, M1=M1, M2=M2,
B0=B.phi0, B1=B.phi1, B2=1,
theta.mu = param$theta.prior.mean,
theta.Q = param$theta.prior.prec,
transform = "identity",
BLC = param$BLC)
spde$model = "matern"
spde$BLC = param$BLC
if (constr || !is.null(extraconstr.int)) {
if (constr) {
A.constr = matrix(colSums(fem$c1), 1, n.spde)
e.constr = 0
} else {
A.constr = matrix(numeric(0), 0, n.spde)
e.constr = c()
}
if (!is.null(extraconstr.int)) {
A.constr =
rBind(A.constr,
matrix(extraconstr.int$A %*% fem$c1,
nrow(extraconstr.int$A), n.spde))
e.constr = c(e.constr, extraconstr.int$e)
}
if (!is.null(extraconstr)) {
A.constr = rBind(A.constr, extraconstr$A)
e.constr = c(e.constr, extraconstr$e)
}
spde$f$constr = FALSE
spde$f$extraconstr = list(A=A.constr, e=e.constr)
} else if (!is.null(extraconstr)) {
spde$f$constr = FALSE
spde$f$extraconstr = extraconstr
}
return(invisible(spde))
}
inla.spde3.theta2phi0 = function(spde, theta)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$n.theta>0)
return(exp(spde$param.inla$B0[,1, drop=TRUE] +
spde$param.inla$B0[,-1, drop=FALSE] %*% theta))
else
return(exp(spde$param.inla$B0[,1, drop=TRUE]))
}
inla.spde3.theta2phi1 = function(spde, theta)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$n.theta>0)
return(exp(spde$param.inla$B1[,1, drop=TRUE] +
spde$param.inla$B1[,-1, drop=FALSE] %*% theta))
else
return(exp(spde$param.inla$B1[,1, drop=TRUE]))
}
inla.spde3.theta2phi2 = function(spde, theta)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$n.theta>0) {
phi = ((spde$param.inla$B2[,1, drop=TRUE] +
spde$param.inla$B2[,-1, drop=FALSE] %*% theta))
} else {
phi = ((spde$param.inla$B2[,1, drop=TRUE]))
}
if (spde$param.inla$transform == "identity") {
return(phi)
} else if (spde$param.inla$transform == "log") {
return(exp(phi))
} else if (spde$param.inla$transform == "logit") {
return((1-exp(phi))/(1+exp(phi)))
} else if (spde$param.inla$transform == "shiftedlog") {
return(2*exp(phi)-1)
} else if (spde$param.inla$transform == "oldlogit") {
return(cos(pi/(1+exp(-phi))))
} else {
warning(paste("Unknown link function '",
spde$param.inla$transform,
"' phi2. Using identity link instead.", sep=""))
return(phi)
}
}
inla.spde3.theta2phi3 = function(spde, theta)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$n.theta>0)
return(exp(spde$param.inla$B3[,1, drop=TRUE] +
spde$param.inla$B3[,-1, drop=FALSE] %*% theta))
else
return(exp(spde$param.inla$B3[,1, drop=TRUE]))
}
inla.spde3.precision =
function(spde, theta=NULL,
phi0=inla.spde3.theta2phi0(spde, theta),
phi1=inla.spde3.theta2phi1(spde, theta),
phi2=inla.spde3.theta2phi2(spde, theta),
phi3=inla.spde3.theta2phi3(spde, theta),
...)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$f$model!="spde3") {
stop("'inla.spde3.precision' only supports internal inla models 'spde3'")
}
D0 <- Diagonal(spde$n.spde, phi0)
D1 <- Diagonal(spde$n.spde, phi1)
D12 <- Diagonal(spde$n.spde, phi1*phi2)
D3 <- Diagonal(nrow(spde$param.inla$M3), phi3)
Q <- (D0 %*% (D1 %*% spde$param.inla$M0 %*% D1 +
D12 %*% spde$param.inla$M1 +
t(spde$param.inla$M1) %*% D12 +
spde$param.inla$M2 +
t(spde$param.inla$M3) %*% D3 %*% spde$param.inla$M3)
%*% D0)
return(Q)
}
inla.spde3.result = function(inla, name, spde, do.transform=TRUE, ...)
{
inla.require.inherits(inla, "inla", "'inla'")
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (!spde$f$model=="spde3") {
stop("'inla.spde3.result' only supports internal inla models 'spde3'")
}
result = list()
## Setup rownames
BLC.names = rownames(spde$param.inla$BLC)
## Values
result$summary.values = inla$summary.random[[name]]
## Marginals for values
if (!is.null(inla$marginals.random[[name]])) {
result$marginals.values = inla$marginals.random[[name]]
}
## Theta
result$summary.hyperpar =
inla.extract.el(inla$summary.hyperpar,
paste("Theta[^ ]+ for ", name, "$", sep=""))
## Marginals for theta
if (!is.null(inla$marginals$hyperpar)) {
result$marginals.hyperpar =
inla.extract.el(inla$marginals.hyperpar,
paste("Theta[^ ]+ for ", name, "$", sep=""))
}
## log-tau/kappa/variance/range
if (!is.null(inla$summary.spde3.blc[[name]])) {
rownames(inla$summary.spde3.blc[[name]]) <- BLC.names
result$summary.theta =
inla.extract.el(inla$summary.spde3.blc[[name]],
"theta\\.[^ ]+$")
result$summary.log.tau =
inla.extract.el(inla$summary.spde3.blc[[name]],
"tau\\.[^ ]+$")
result$summary.log.kappa =
inla.extract.el(inla$summary.spde3.blc[[name]],
"kappa\\.[^ ]+$")
result$summary.log.variance.nominal =
inla.extract.el(inla$summary.spde3.blc[[name]],
"variance.nominal\\.[^ ]+$")
result$summary.log.range.nominal =
inla.extract.el(inla$summary.spde3.blc[[name]],
"range.nominal\\.[^ ]+$")
}
## Marginals for log-tau/kappa/variance/range
if (!is.null(inla$marginals.spde3.blc[[name]])) {
names(inla$marginals.spde3.blc[[name]]) <- BLC.names
result$marginals.theta =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"theta\\.[^ ]+$")
result$marginals.log.tau =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"tau\\.[^ ]+$")
result$marginals.log.kappa =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"kappa\\.[^ ]+$")
result$marginals.log.variance.nominal =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"variance.nominal\\.[^ ]+$")
result$marginals.log.range.nominal =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"range.nominal\\.[^ ]+$")
if (do.transform) {
result$marginals.tau =
lapply(result$marginals.log.tau,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.kappa =
lapply(result$marginals.log.kappa,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.variance.nominal =
lapply(result$marginals.log.variance.nominal,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.range.nominal =
lapply(result$marginals.log.range.nominal,
function(x) inla.tmarginal(function(y) exp(y), x))
}
}
return(result)
}
inla.spde3.result = function(inla, name, spde, do.transform=TRUE, ...)
{
inla.require.inherits(inla, "inla", "'inla'")
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (!spde$f$model=="spde3") {
stop("'inla.spde3.result' only supports internal inla models 'spde3'")
}
result = list()
## Setup rownames
BLC.names = rownames(spde$param.inla$BLC)
## Values
result$summary.values = inla$summary.random[[name]]
## Marginals for values
if (!is.null(inla$marginals.random[[name]])) {
result$marginals.values = inla$marginals.random[[name]]
}
## Theta
result$summary.hyperpar =
inla.extract.el(inla$summary.hyperpar,
paste("Theta[^ ]+ for ", name, "$", sep=""))
## Marginals for theta
if (!is.null(inla$marginals$hyperpar)) {
result$marginals.hyperpar =
inla.extract.el(inla$marginals.hyperpar,
paste("Theta[^ ]+ for ", name, "$", sep=""))
}
## log-tau/kappa/variance/range
if (!is.null(inla$summary.spde3.blc[[name]])) {
rownames(inla$summary.spde3.blc[[name]]) <- BLC.names
result$summary.theta =
inla.extract.el(inla$summary.spde3.blc[[name]],
"theta\\.[^ ]+$")
result$summary.log.tau =
inla.extract.el(inla$summary.spde3.blc[[name]],
"tau\\.[^ ]+$")
result$summary.log.kappa =
inla.extract.el(inla$summary.spde3.blc[[name]],
"kappa\\.[^ ]+$")
result$summary.log.variance.nominal =
inla.extract.el(inla$summary.spde3.blc[[name]],
"variance.nominal\\.[^ ]+$")
result$summary.log.range.nominal =
inla.extract.el(inla$summary.spde3.blc[[name]],
"range.nominal\\.[^ ]+$")
}
## Marginals for log-tau/kappa/variance/range
if (!is.null(inla$marginals.spde3.blc[[name]])) {
names(inla$marginals.spde3.blc[[name]]) <- BLC.names
result$marginals.theta =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"theta\\.[^ ]+$")
result$marginals.log.tau =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"tau\\.[^ ]+$")
result$marginals.log.kappa =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"kappa\\.[^ ]+$")
result$marginals.log.variance.nominal =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"variance.nominal\\.[^ ]+$")
result$marginals.log.range.nominal =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"range.nominal\\.[^ ]+$")
if (do.transform) {
result$marginals.tau =
lapply(result$marginals.log.tau,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.kappa =
lapply(result$marginals.log.kappa,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.variance.nominal =
lapply(result$marginals.log.variance.nominal,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.range.nominal =
lapply(result$marginals.log.range.nominal,
function(x) inla.tmarginal(function(y) exp(y), x))
}
}
return(result)
}
inla.spde3.models = function()
{
return(c("generic", "matern"))
}
## spde.common-connections:
inla.spde.precision.inla.spde3 = inla.spde3.precision
inla.spde.result.inla.spde3 = inla.spde3.result
andrewzm/INLA documentation built on May 10, 2019, 11:12 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
## 'spde3' model functions
## External: inla.spde.precision.inla.spde3
## External: inla.spde.result!inla.spde3 inla.spde3.generic
## External: inla.spde3.matern param2.matern.orig
## External: inla.spde3.matern.sd.basis inla.spde3.models
## External: inla.spde3.precision inla.spde3.result
## External: inla.spde3.theta2phi0 inla.spde3.theta2phi1
## External: inla.spde3.theta2phi2 inla.spde3.theta2phi3
##
## Internal: inla.internal.spde3.matern.B.tau
## Internal: inla.internal.test.spde3.sd.basis
inla.spde3.generic <-
function(M0, M1, M2, M3, B0, B1, B2, B3, theta.mu, theta.Q,
transform = c("identity","log","logit","shiftedlog","oldlogit"),
theta.initial = theta.mu,
fixed = rep(FALSE, length(theta.mu)),
theta.fixed = theta.initial[fixed],
BLC = cbind(0.0, diag(nrow=length(theta.mu))),
...)
{
warning("The spde3 interface is still experimental and may be changed without notice.")
transform = match.arg(transform)
if (!inla.is.matrix(M0)) stop("M0 must be of a matrix type.")
if (!inla.is.matrix(M1)) stop("M1 must be of a matrix type.")
if (!inla.is.matrix(M2)) stop("M2 must be of a matrix type.")
if (!inla.is.matrix(M3)) stop("M3 must be of a matrix type.")
M0 = inla.as.dgTMatrix(M0)
M1 = inla.as.dgTMatrix(M1)
M2 = inla.as.dgTMatrix(M2)
M3 = inla.as.dgTMatrix(M3)
n.spde = nrow(M0)
n.theta = length(theta.mu)
spde = (list(model = "generic",
n.spde = n.spde,
n.theta = n.theta,
param.inla = list(),
f = list()
))
class(spde) = c("inla.spde3", "inla.spde", "inla.model.class")
B0 = inla.spde.homogenise_B_matrix(B0, n.spde, n.theta)
B1 = inla.spde.homogenise_B_matrix(B1, n.spde, n.theta)
B2 = inla.spde.homogenise_B_matrix(B2, n.spde, n.theta)
B3 = inla.spde.homogenise_B_matrix(B3, n.spde, n.theta)
BLC = inla.spde.homogenise_B_matrix(BLC, nrow(BLC), n.theta)
param.inla =
list(n = n.spde,
n.theta = n.theta,
M0=M0, M1=M1, M2=M2, M3=M3,
B0=B0, B1=B1, B2=B2, B3=B3, BLC=BLC,
theta.mu=theta.mu, theta.Q=theta.Q,
transform=transform,
theta.initial=theta.initial,
fixed=fixed,
theta.fixed=theta.fixed)
## TODO: Check dimension consistency of inputs
## If any "fixed", move information from B[,1+(1:n.theta)] into
## B[,1], theta.mu, and theta.Q
## NOTE: Should this be in the f function instead?
## No, the information is also needed elsewhere.
if (any(fixed)) {
param.inla$theta.initial = theta.initial[!fixed]
param.inla$B0[,1] =
B0[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$B1[,1] =
B1[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$B2[,1] =
B2[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$B3[,1] =
B3[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$BLC[,1] =
BLC[,c(TRUE, fixed), drop=FALSE] %*% c(1.0, theta.fixed)
param.inla$B0 = param.inla$B0[,c(TRUE, !fixed), drop=FALSE]
param.inla$B1 = param.inla$B1[,c(TRUE, !fixed), drop=FALSE]
param.inla$B2 = param.inla$B2[,c(TRUE, !fixed), drop=FALSE]
param.inla$B3 = param.inla$B3[,c(TRUE, !fixed), drop=FALSE]
param.inla$BLC = param.inla$BLC[,c(TRUE, !fixed), drop=FALSE]
param.inla$theta.Q = param.inla$theta.Q[!fixed, !fixed, drop=FALSE]
if (!all(fixed)) {
param.inla$theta.mu =
theta.mu - solve(param.inla$theta.Q,
theta.Q[!fixed, fixed, drop=FALSE]
%*% (theta.fixed[fixed]-theta.mu[fixed]))
} else { ## All fixed.
param.inla$theta.mu = as.vector(matrix(0.0, 0, 1))
}
}
## Remove all BLC rows that would produce point masses, which is
## not supported by inla:
if (n.theta==0) {
param.inla$BLC = matrix(0,0,1)
} else {
param.inla$BLC =
param.inla$BLC[rowSums(abs(param.inla$BLC[,-1,drop=FALSE]))>0,,
drop=FALSE]
}
spde$param.inla = param.inla
## NOTE: Should the prefix be set inside the f function instead?
## Yes, it should.
spde.prefix = inla.fmesher.make.prefix(NULL, NULL)
spde$f = (list(model="spde3",
spde3.prefix=spde.prefix,
n=n.spde,
spde3.transform=transform,
hyper.default =
(list(theta1 =
list(prior="mvnorm",
param=(c(param.inla$theta.mu,
as.matrix(param.inla$theta.Q)))
)
))
))
if (n.theta>0)
for (k in 1:n.theta) {
eval(parse(text=
paste("spde$f$hyper.default$theta", k,
"$initial = param.inla$theta.mu[k]", sep="")))
}
## NOTE: Should this be in the f function instead?
## Yes, it should.
fmesher.write(inla.affirm.double(spde$param.inla$M0), spde.prefix, "M0")
fmesher.write(inla.affirm.double(spde$param.inla$M1), spde.prefix, "M1")
fmesher.write(inla.affirm.double(spde$param.inla$M2), spde.prefix, "M2")
fmesher.write(inla.affirm.double(spde$param.inla$M3), spde.prefix, "M3")
fmesher.write(inla.affirm.double(spde$param.inla$B0), spde.prefix, "B0")
fmesher.write(inla.affirm.double(spde$param.inla$B1), spde.prefix, "B1")
fmesher.write(inla.affirm.double(spde$param.inla$B2), spde.prefix, "B2")
fmesher.write(inla.affirm.double(spde$param.inla$B3), spde.prefix, "B3")
## Only write BLC if it is non-empty
if (nrow(spde$param.inla$BLC)>0)
fmesher.write(inla.affirm.double(spde$param.inla$BLC),
spde.prefix, "BLC")
return(spde)
}
inla.internal.spde3.matern.B.tau =
function(precision.fcn, n.theta, delta = 1e-3, ...)
{
theta.0 = rep(0, n.theta)
Q.0 = precision.fcn(theta.0, ...)
logv.0 = log(diag(inla.qinv(Q.0)))
B.tau = matrix(0, nrow(Q.0), 1L+n.theta)
for (k in seq_len(n.theta)) {
theta = theta.0
theta[k] = theta[k] + delta
Q = precision.fcn(theta, ...)
logv = log(diag(inla.qinv(Q)))
dlogv = (logv-logv.0)/delta
B.tau[, k+1] = dlogv/2
}
B.tau[,1L] = logv.0/2
return(B.tau)
}
inla.spde3.matern.sd.basis =
function(mesh, B.sd, B.range, method=1,
local.offset.compensation=FALSE,
alpha=2, ...)
{
p.sd = ncol(B.sd)-1L
p.kappa = ncol(B.range)-1L
d = inla.ifelse(inherits(mesh, "inla.mesh"), 2, 1)
nu = alpha-d/2
nu.nominal = max(0.5, nu)
alpha.nominal = max(nu.nominal+d/2, alpha)
n.spde = inla.ifelse(d==2, mesh$n, mesh$m)
## log(kappa) = 0.5*log(8*nu) - log(range)
B.kappa = (cBind(log(8*nu.nominal)/2 - B.range[,1],
-B.range[,-1,drop=FALSE] ))
if (method==1) {
spde =
inla.spde3.matern(mesh, B.tau=as.matrix(0), B.kappa=B.kappa, ...)
B.tau =
inla.internal.spde3.matern.B.tau(
precision.fcn=
function(theta, spde) {
return(inla.spde3.precision(spde, theta=theta))
},
n.theta = p.kappa,
spde = spde)
if (!local.offset.compensation) {
B.tau[,1] = rep(mean(B.tau[,1]), spde$n.spde)
}
} else {
spde =
inla.spde3.matern(mesh,
B.tau=as.matrix(0),
B.kappa=B.kappa[,1,drop=FALSE],
alpha=alpha, ...)
lvar.0 =
log(diag(inla.qinv(inla.spde3.precision(spde, theta=rep(0,0)))))
if (!local.offset.compensation) {
lvar.0 = rep(mean(lvar.0), spde$n.spde)
}
B.tau = -B.kappa*nu
B.tau[,1] = lvar.0/2
}
log.var.scaling = lgamma(nu.nominal)-lgamma(alpha.nominal)-log(4*pi)*d/2
B.tau =
cBind(B.tau[,1]-B.sd[,1],
-B.sd[,-1,drop=FALSE],
B.tau[,-1,drop=FALSE])
B.kappa =
cBind(B.kappa[,1],
matrix(0, n.spde, p.sd),
B.kappa[,-1,drop=FALSE])
return(list(B.tau=B.tau, B.kappa=B.kappa))
}
inla.internal.test.spde3.sd.basis = function (k=1, dth=0.1, r=1000, globe=25, compensate=FALSE) {
alpha=2
mesh = inla.mesh.create(globe=globe)
B.common = inla.mesh.basis(mesh, "b.spline", n=3)
B.common = cBind(B.common, B.common[,1]*(mesh$loc[,3]>0))
B.common[,1] = B.common[,1]*(mesh$loc[,3]<=0)
B.sd = cBind(0, B.common)
B.range = cBind(log(r/6370), B.common)
sd.basis0 = inla.spde3.matern.sd.basis(mesh, B.sd=B.sd, B.range=B.range, method=0, alpha=alpha, local.offset.compensation=compensate)
sd.basis1 = inla.spde3.matern.sd.basis(mesh, B.sd=B.sd, B.range=B.range, method=1, alpha=alpha, local.offset.compensation=compensate)
spde0 =
inla.spde3.matern(mesh, alpha=alpha,
B.tau=sd.basis0$B.tau,
B.kappa=sd.basis0$B.kappa)
spde1 =
inla.spde3.matern(mesh, alpha=alpha,
B.tau=sd.basis1$B.tau,
B.kappa=sd.basis1$B.kappa)
th0 = c(0,0,0,0,0,0,0,0)
th1 = th0
th1[k] = th0[k]+dth
v0.0=diag(inla.qinv(inla.spde3.precision(spde0,theta=th0)))
v0.1=diag(inla.qinv(inla.spde3.precision(spde0,theta=th1)))
v1.0=diag(inla.qinv(inla.spde3.precision(spde1,theta=th0)))
v1.1=diag(inla.qinv(inla.spde3.precision(spde1,theta=th1)))
op=par(mfrow=c(2,2))
on.exit(par(op))
plot(asin(mesh$loc[,3]),rowSums(sd.basis0$B.kappa[,1+k,drop=FALSE]), pch=20,
ylim=(range(c(0, range(rowSums(sd.basis0$B.kappa[,1+k,drop=FALSE])),
range(-rowSums(sd.basis0$B.tau[,1+k,drop=FALSE])),
range(-rowSums(sd.basis1$B.tau[,1+k,drop=FALSE]))))),
main="Basis functions")
points(asin(mesh$loc[,3]),-rowSums(sd.basis0$B.tau[,1+k,drop=FALSE]),col=2,pch=20)
points(asin(mesh$loc[,3]),-rowSums(sd.basis1$B.tau[,1+k,drop=FALSE]),col=4,pch=20)
legend("bottomright",
legend=paste("Method", 0:1),
col=c(2,4), pch=20)
plot(asin(mesh$loc[,3]),v0.1, col=2,pch=20,
ylim=range(c(0,range(v0.0),range(v1.0),range(v0.1),range(v1.1))),
main="Absolute variances")
points(asin(mesh$loc[,3]),v1.1, col=4,pch=20)
points(asin(mesh$loc[,3]),v0.0, col=2,pch=1)
points(asin(mesh$loc[,3]),v1.0, col=4,pch=1)
legend("bottomright",
legend=paste("Method", 0:1),
col=c(2,4), pch=20)
plot(asin(mesh$loc[,3]),v0.1-v0.0, col=2,pch=20,
ylim=range(c(0,range(v0.1-v0.0),range(v1.1-v1.0))),
main="Absolute variance error")
points(asin(mesh$loc[,3]),v1.1-v1.0, col=4,pch=20)
legend("bottomright",
legend=paste("Method", 0:1),
col=c(2,4), pch=20)
plot(asin(mesh$loc[,3]),v0.1/v0.0-1, col=2,pch=20,
ylim=range(c(0,range(v0.1/v0.0-1),range(v1.1/v1.0-1))),
main="Relative variance error")
points(asin(mesh$loc[,3]),v1.1/v1.0-1, col=4,pch=20)
legend("bottomright",
legend=paste("Method", 0:1),
col=c(2,4), pch=20)
return(environment())
}
param2.matern.orig =
function(mesh,
alpha=2,
B.tau = matrix(c(0,1,0),1,3),
B.kappa = matrix(c(0,0,1),1,3),
prior.variance.nominal = 1,
prior.range.nominal = NULL,
prior.tau = NULL,
prior.kappa = NULL,
theta.prior.mean = NULL,
theta.prior.prec = 0.1)
{
## NOTE: For d==1, degree==2, the B.* must be given per basis
## function, not per knot.
inla.require.inherits(mesh, c("inla.mesh", "inla.mesh.1d"), "'mesh'")
if (is.null(B.tau))
stop("B.tau must not be NULL.")
if (is.null(B.kappa))
stop("B.kappa must not be NULL.")
is.stationary = (nrow(B.kappa)==1) && (nrow(B.tau)==1)
d = inla.ifelse(inherits(mesh, "inla.mesh"), 2, 1)
nu = alpha-d/2
nu.nominal = max(0.5, nu)
alpha.nominal = max(nu.nominal+d/2, alpha)
n.spde = inla.ifelse(d==2, mesh$n, mesh$m)
n.theta = ncol(B.kappa)-1L
B.kappa = inla.spde.homogenise_B_matrix(B.kappa, n.spde, n.theta)
B.tau = inla.spde.homogenise_B_matrix(B.tau, n.spde, n.theta)
B.prec =
cbind(+ lgamma(alpha.nominal) - lgamma(nu.nominal)
+ (d/2)*log(4*pi) + 2*nu.nominal*B.kappa[,1]
+ 2*B.tau[,1],
2*nu.nominal*B.kappa[,-1,drop=FALSE]
+ 2*B.tau[,-1,drop=FALSE] )
if (is.stationary) {
B.tau = B.tau[1,,drop=FALSE]
B.kappa = B.kappa[1,,drop=FALSE]
B.prec = B.prec[1,,drop=FALSE]
}
B.variance = -B.prec
B.range =
cbind(0.5*log(8*nu.nominal)-B.kappa[,1],
-B.kappa[,-1,drop=FALSE])
if (n.theta>0) {
B.theta = cbind(0,diag(1, n.theta))
rownames(B.theta) <- rownames(B.theta, do.NULL=FALSE, prefix="theta.")
} else {
B.theta = NULL
}
rownames(B.tau) <- rownames(B.tau, do.NULL=FALSE, prefix="tau.")
rownames(B.kappa) <- rownames(B.kappa, do.NULL=FALSE, prefix="kappa.")
rownames(B.variance) <-
rownames(B.variance, do.NULL=FALSE, prefix="variance.nominal.")
rownames(B.range) <-
rownames(B.range, do.NULL=FALSE, prefix="range.nominal.")
BLC = rbind(B.theta, B.tau, B.kappa, B.variance, B.range)
## Construct priors.
if (is.null(theta.prior.prec)) {
theta.prior.prec = diag(0.1, n.theta, n.theta)
} else {
theta.prior.prec = as.matrix(theta.prior.prec)
if (ncol(theta.prior.prec) == 1) {
theta.prior.prec =
diag(as.vector(theta.prior.prec), n.theta, n.theta)
}
if ((nrow(theta.prior.prec) != n.theta) ||
(ncol(theta.prior.prec) != n.theta)) {
stop(paste("Size of theta.prior.prec is (",
paste(dim(theta.prior.prec), collapse=",", sep=""),
") but should be (",
paste(c(n.theta, n.theta), collapse=",", sep=""),
")."))
}
}
if (is.null(theta.prior.mean)) {
if (is.null(prior.range.nominal)) {
mesh.range =
inla.ifelse(d==2,
(max(c(diff(range(mesh$loc[,1])),
diff(range(mesh$loc[,2])),
diff(range(mesh$loc[,3]))
))),
diff(mesh$interval))
prior.range.nominal = mesh.range*0.2
}
if (is.null(prior.kappa)) {
prior.kappa = sqrt(8*nu.nominal)/prior.range.nominal
}
if (is.null(prior.tau)) {
prior.tau =
sqrt(gamma(nu.nominal)/gamma(alpha.nominal)/
(4*pi*prior.kappa^(2*nu.nominal)*prior.variance.nominal))
}
if (n.theta>0) {
theta.prior.mean =
qr.solve(rbind(B.tau[,-1,drop=FALSE], B.kappa[,-1,drop=FALSE]),
c(log(prior.tau) - B.tau[,1],
log(prior.kappa) - B.kappa[,1]))
} else {
theta.prior.mean = rep(0, n.theta) ## Empty vector
}
}
param =
list(is.stationary=is.stationary,
B.tau=B.tau, B.kappa=B.kappa, BLC=BLC,
theta.prior.mean=theta.prior.mean,
theta.prior.prec=theta.prior.prec)
return(param)
}
inla.spde3.matern =
function(mesh,
alpha=2,
param = NULL,
constr = FALSE,
extraconstr.int = NULL,
extraconstr = NULL,
fractional.method = c("parsimonious", "null"),
B.tau = matrix(c(0,1,0),1,3),
B.kappa = matrix(c(0,0,1),1,3),
prior.variance.nominal = 1,
prior.range.nominal = NULL,
prior.tau = NULL,
prior.kappa = NULL,
theta.prior.mean = NULL,
theta.prior.prec = 0.1)
{
inla.require.inherits(mesh, c("inla.mesh", "inla.mesh.1d"), "'mesh'")
fractional.method = match.arg(fractional.method)
if (is.null(param)) {
param =
param2.matern.orig(
mesh, alpha,
B.tau, B.kappa,
prior.variance.nominal,
prior.range.nominal,
prior.tau,
prior.kappa,
theta.prior.mean,
theta.prior.prec)
} else {
deprecated =
!c(missing(B.tau), missing(B.kappa),
missing(prior.variance.nominal),
missing(prior.range.nominal),
missing(prior.tau), missing(prior.kappa),
missing(theta.prior.mean), missing(theta.prior.prec))
deprecated =
c("B.tau", "B.kappa",
"prior.variance.nominal",
"prior.range.nominal",
"prior.tau", "prior.kappa",
"theta.prior.mean", "theta.prior.prec")[deprecated]
if (length(deprecated) > 0) {
warning(paste("'param' specified; ",
"Ignoring deprecated parameter(s) ",
paste(deprecated, collapse=", "), ".", sep=""))
}
}
d = inla.ifelse(inherits(mesh, "inla.mesh"), 2, 1)
nu = alpha-d/2
nu.nominal = max(0.5, nu)
alpha.nominal = max(nu.nominal+d/2, alpha)
n.spde = inla.ifelse(d==2, mesh$n, mesh$m)
n.theta = ncol(B.kappa)-1L
if (d==2) {
fem =
inla.fmesher.smorg(mesh$loc,
mesh$graph$tv,
fem=2,
output=list("c0", "c1", "g1", "g2"))
} else {
fem = inla.mesh.1d.fem(mesh)
if (mesh$degree==2) {
fem$c0 = fem$c1 ## Use higher order matrix.
}
}
if (alpha==2) {
B.phi0 = param$B.tau
B.phi1 = 2*param$B.kappa
M0 = fem$c0
M1 = fem$g1
M2 = fem$g2
} else if (alpha==1) {
B.phi0 = param$B.tau
B.phi1 = param$B.kappa
M0 = fem$c0
M1 = fem$g1*0
M2 = fem$g1
} else if (!param$is.stationary) {
stop("Non-stationary Matern with fractional alpha is not implemented.")
} else if ((alpha<2) && (alpha>1)) {
if (fractional.method == "parsimonious") {
lambda = alpha-floor(alpha)
b = matrix(c(1,0,0, 1,1,0, 1,2,1),3,3) %*%
solve(matrix(1/(c(4:2, 3:1, 2:0)+lambda), 3, 3),
1/(c(4:2)+lambda-alpha))
} else if (fractional.method == "null") {
b = c(1,alpha,alpha*(alpha-1)/2)
} else {
stop(paste("Unknown fractional.method '", fractional.method,
"'.", sep=""))
}
B.phi0 = param$B.tau + (alpha-2)*param$B.kappa
B.phi1 = 2*param$B.kappa
M0 = fem$c0*b[1]
M1 = fem$g1*b[2]/2
M2 = fem$g2*b[3]
} else if ((alpha<1) && (alpha>0)) {
if (fractional.method == "parsimonious") {
lambda = alpha-floor(alpha)
b = matrix(c(1,0,1,1),2,2) %*%
solve(matrix(1/(c(2:1, 1:0)+lambda), 2, 2),
1/(c(2:1)+lambda-alpha))
} else if (fractional.method == "null") {
b = c(1,alpha)
} else {
stop(paste("Unknown fractional.method '", fractional.method,
"'.", sep=""))
}
B.phi0 = param$B.tau + (alpha-1)*param$B.kappa
B.phi1 = param$B.kappa
M0 = fem$c0*b[1]
M1 = fem$g1*0
M2 = fem$g1*b[2]
} else {
stop(paste("Unsupported alpha value (", alpha,
"). Supported values are 0 < alpha <= 2", sep=""))
}
spde =
inla.spde3.generic(M0=M0, M1=M1, M2=M2,
B0=B.phi0, B1=B.phi1, B2=1,
theta.mu = param$theta.prior.mean,
theta.Q = param$theta.prior.prec,
transform = "identity",
BLC = param$BLC)
spde$model = "matern"
spde$BLC = param$BLC
if (constr || !is.null(extraconstr.int)) {
if (constr) {
A.constr = matrix(colSums(fem$c1), 1, n.spde)
e.constr = 0
} else {
A.constr = matrix(numeric(0), 0, n.spde)
e.constr = c()
}
if (!is.null(extraconstr.int)) {
A.constr =
rBind(A.constr,
matrix(extraconstr.int$A %*% fem$c1,
nrow(extraconstr.int$A), n.spde))
e.constr = c(e.constr, extraconstr.int$e)
}
if (!is.null(extraconstr)) {
A.constr = rBind(A.constr, extraconstr$A)
e.constr = c(e.constr, extraconstr$e)
}
spde$f$constr = FALSE
spde$f$extraconstr = list(A=A.constr, e=e.constr)
} else if (!is.null(extraconstr)) {
spde$f$constr = FALSE
spde$f$extraconstr = extraconstr
}
return(invisible(spde))
}
param2.iheat <- function(mesh.space,
mesh.time,
gamma.E = NULL,
alpha.E = NULL,
theta.prior.mean = NULL,
theta.prior.prec = NULL)
{
return(list(gamma.E=gamma.E,
alpha.E=alpha.E,
theta.prior.mean=theta.prior.mean,
theta.prior.prec=theta.prior.prec))
}
inla.spde3.iheat =
function(mesh.space,
mesh.time,
order=2,
param = NULL,
constr = FALSE,
extraconstr.int = NULL,
extraconstr = NULL)
{
## order=1:
## gamma.t dot(u) - Laplacian (u) = E
##
## order=2:
## gamma.t dot(u) - Laplacian (u) = v
## gamma.t dot(v) - Laplacian (v) = E
##
## (1 - gamma.E Laplacian)^(alpha.E/2) E = W/sqrt(gamma.s)
## gamma.E and alpha.E known
inla.require.inherits(mesh.space, c("inla.mesh", "inla.mesh.1d"),
"'mesh.space'")
inla.require.inherits(mesh.time, c("inla.mesh.1d"),
"'mesh.time'")
if (is.null(param)) {
param =
param2.iheat(
mesh.space, mesh.time,
theta.prior.mean,
theta.prior.prec)
}
d.space = inla.ifelse(inherits(mesh.space, "inla.mesh.1d"), 1, 2)
d.time = 1
n.space = inla.ifelse(d.space==2, mesh.space$n, mesh.space$m)
n.time = mesh.time$m
n.spde = n.space*n.time
n.theta = 2L ## gamma.s, gamma.t
if (d.space==2) {
fem.space = inla.mesh.fem(mesh.space$loc, 2)
} else {
fem.space = inla.mesh.1d.fem(mesh.space)
if (mesh.space$degree==2) {
fem.space$c0 = fem.space$c1 ## Use higher order matrix.
}
}
fem.time = inla.mesh.1d.fem(mesh.time)
if (mesh.time$degree==2) {
fem.time$c0 = fem.time$c1 ## Use higher order matrix.
}
## TODO: the rest
if (alpha==2) {
B.phi0 = param$B.tau
B.phi1 = 2*param$B.kappa
M0 = fem$c0
M1 = fem$g1
M2 = fem$g2
} else if (alpha==1) {
B.phi0 = param$B.tau
B.phi1 = param$B.kappa
M0 = fem$c0
M1 = fem$g1*0
M2 = fem$g1
} else if (!param$is.stationary) {
stop("Non-stationary Matern with fractional alpha is not implemented.")
} else if ((alpha<2) && (alpha>1)) {
if (fractional.method == "parsimonious") {
lambda = alpha-floor(alpha)
b = matrix(c(1,0,0, 1,1,0, 1,2,1),3,3) %*%
solve(matrix(1/(c(4:2, 3:1, 2:0)+lambda), 3, 3),
1/(c(4:2)+lambda-alpha))
} else if (fractional.method == "null") {
b = c(1,alpha,alpha*(alpha-1)/2)
} else {
stop(paste("Unknown fractional.method '", fractional.method,
"'.", sep=""))
}
B.phi0 = param$B.tau + (alpha-2)*param$B.kappa
B.phi1 = 2*param$B.kappa
M0 = fem$c0*b[1]
M1 = fem$g1*b[2]/2
M2 = fem$g2*b[3]
} else if ((alpha<1) && (alpha>0)) {
if (fractional.method == "parsimonious") {
lambda = alpha-floor(alpha)
b = matrix(c(1,0,1,1),2,2) %*%
solve(matrix(1/(c(2:1, 1:0)+lambda), 2, 2),
1/(c(2:1)+lambda-alpha))
} else if (fractional.method == "null") {
b = c(1,alpha)
} else {
stop(paste("Unknown fractional.method '", fractional.method,
"'.", sep=""))
}
B.phi0 = param$B.tau + (alpha-1)*param$B.kappa
B.phi1 = param$B.kappa
M0 = fem$c0*b[1]
M1 = fem$g1*0
M2 = fem$g1*b[2]
} else {
stop(paste("Unsupported alpha value (", alpha,
"). Supported values are 0 < alpha <= 2", sep=""))
}
spde =
inla.spde3.generic(M0=M0, M1=M1, M2=M2,
B0=B.phi0, B1=B.phi1, B2=1,
theta.mu = param$theta.prior.mean,
theta.Q = param$theta.prior.prec,
transform = "identity",
BLC = param$BLC)
spde$model = "matern"
spde$BLC = param$BLC
if (constr || !is.null(extraconstr.int)) {
if (constr) {
A.constr = matrix(colSums(fem$c1), 1, n.spde)
e.constr = 0
} else {
A.constr = matrix(numeric(0), 0, n.spde)
e.constr = c()
}
if (!is.null(extraconstr.int)) {
A.constr =
rBind(A.constr,
matrix(extraconstr.int$A %*% fem$c1,
nrow(extraconstr.int$A), n.spde))
e.constr = c(e.constr, extraconstr.int$e)
}
if (!is.null(extraconstr)) {
A.constr = rBind(A.constr, extraconstr$A)
e.constr = c(e.constr, extraconstr$e)
}
spde$f$constr = FALSE
spde$f$extraconstr = list(A=A.constr, e=e.constr)
} else if (!is.null(extraconstr)) {
spde$f$constr = FALSE
spde$f$extraconstr = extraconstr
}
return(invisible(spde))
}
inla.spde3.theta2phi0 = function(spde, theta)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$n.theta>0)
return(exp(spde$param.inla$B0[,1, drop=TRUE] +
spde$param.inla$B0[,-1, drop=FALSE] %*% theta))
else
return(exp(spde$param.inla$B0[,1, drop=TRUE]))
}
inla.spde3.theta2phi1 = function(spde, theta)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$n.theta>0)
return(exp(spde$param.inla$B1[,1, drop=TRUE] +
spde$param.inla$B1[,-1, drop=FALSE] %*% theta))
else
return(exp(spde$param.inla$B1[,1, drop=TRUE]))
}
inla.spde3.theta2phi2 = function(spde, theta)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$n.theta>0) {
phi = ((spde$param.inla$B2[,1, drop=TRUE] +
spde$param.inla$B2[,-1, drop=FALSE] %*% theta))
} else {
phi = ((spde$param.inla$B2[,1, drop=TRUE]))
}
if (spde$param.inla$transform == "identity") {
return(phi)
} else if (spde$param.inla$transform == "log") {
return(exp(phi))
} else if (spde$param.inla$transform == "logit") {
return((1-exp(phi))/(1+exp(phi)))
} else if (spde$param.inla$transform == "shiftedlog") {
return(2*exp(phi)-1)
} else if (spde$param.inla$transform == "oldlogit") {
return(cos(pi/(1+exp(-phi))))
} else {
warning(paste("Unknown link function '",
spde$param.inla$transform,
"' phi2. Using identity link instead.", sep=""))
return(phi)
}
}
inla.spde3.theta2phi3 = function(spde, theta)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$n.theta>0)
return(exp(spde$param.inla$B3[,1, drop=TRUE] +
spde$param.inla$B3[,-1, drop=FALSE] %*% theta))
else
return(exp(spde$param.inla$B3[,1, drop=TRUE]))
}
inla.spde3.precision =
function(spde, theta=NULL,
phi0=inla.spde3.theta2phi0(spde, theta),
phi1=inla.spde3.theta2phi1(spde, theta),
phi2=inla.spde3.theta2phi2(spde, theta),
phi3=inla.spde3.theta2phi3(spde, theta),
...)
{
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (spde$f$model!="spde3") {
stop("'inla.spde3.precision' only supports internal inla models 'spde3'")
}
D0 <- Diagonal(spde$n.spde, phi0)
D1 <- Diagonal(spde$n.spde, phi1)
D12 <- Diagonal(spde$n.spde, phi1*phi2)
D3 <- Diagonal(nrow(spde$param.inla$M3), phi3)
Q <- (D0 %*% (D1 %*% spde$param.inla$M0 %*% D1 +
D12 %*% spde$param.inla$M1 +
t(spde$param.inla$M1) %*% D12 +
spde$param.inla$M2 +
t(spde$param.inla$M3) %*% D3 %*% spde$param.inla$M3)
%*% D0)
return(Q)
}
inla.spde3.result = function(inla, name, spde, do.transform=TRUE, ...)
{
inla.require.inherits(inla, "inla", "'inla'")
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (!spde$f$model=="spde3") {
stop("'inla.spde3.result' only supports internal inla models 'spde3'")
}
result = list()
## Setup rownames
BLC.names = rownames(spde$param.inla$BLC)
## Values
result$summary.values = inla$summary.random[[name]]
## Marginals for values
if (!is.null(inla$marginals.random[[name]])) {
result$marginals.values = inla$marginals.random[[name]]
}
## Theta
result$summary.hyperpar =
inla.extract.el(inla$summary.hyperpar,
paste("Theta[^ ]+ for ", name, "$", sep=""))
## Marginals for theta
if (!is.null(inla$marginals$hyperpar)) {
result$marginals.hyperpar =
inla.extract.el(inla$marginals.hyperpar,
paste("Theta[^ ]+ for ", name, "$", sep=""))
}
## log-tau/kappa/variance/range
if (!is.null(inla$summary.spde3.blc[[name]])) {
rownames(inla$summary.spde3.blc[[name]]) <- BLC.names
result$summary.theta =
inla.extract.el(inla$summary.spde3.blc[[name]],
"theta\\.[^ ]+$")
result$summary.log.tau =
inla.extract.el(inla$summary.spde3.blc[[name]],
"tau\\.[^ ]+$")
result$summary.log.kappa =
inla.extract.el(inla$summary.spde3.blc[[name]],
"kappa\\.[^ ]+$")
result$summary.log.variance.nominal =
inla.extract.el(inla$summary.spde3.blc[[name]],
"variance.nominal\\.[^ ]+$")
result$summary.log.range.nominal =
inla.extract.el(inla$summary.spde3.blc[[name]],
"range.nominal\\.[^ ]+$")
}
## Marginals for log-tau/kappa/variance/range
if (!is.null(inla$marginals.spde3.blc[[name]])) {
names(inla$marginals.spde3.blc[[name]]) <- BLC.names
result$marginals.theta =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"theta\\.[^ ]+$")
result$marginals.log.tau =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"tau\\.[^ ]+$")
result$marginals.log.kappa =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"kappa\\.[^ ]+$")
result$marginals.log.variance.nominal =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"variance.nominal\\.[^ ]+$")
result$marginals.log.range.nominal =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"range.nominal\\.[^ ]+$")
if (do.transform) {
result$marginals.tau =
lapply(result$marginals.log.tau,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.kappa =
lapply(result$marginals.log.kappa,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.variance.nominal =
lapply(result$marginals.log.variance.nominal,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.range.nominal =
lapply(result$marginals.log.range.nominal,
function(x) inla.tmarginal(function(y) exp(y), x))
}
}
return(result)
}
inla.spde3.result = function(inla, name, spde, do.transform=TRUE, ...)
{
inla.require.inherits(inla, "inla", "'inla'")
inla.require.inherits(spde, "inla.spde3", "'spde'")
if (!spde$f$model=="spde3") {
stop("'inla.spde3.result' only supports internal inla models 'spde3'")
}
result = list()
## Setup rownames
BLC.names = rownames(spde$param.inla$BLC)
## Values
result$summary.values = inla$summary.random[[name]]
## Marginals for values
if (!is.null(inla$marginals.random[[name]])) {
result$marginals.values = inla$marginals.random[[name]]
}
## Theta
result$summary.hyperpar =
inla.extract.el(inla$summary.hyperpar,
paste("Theta[^ ]+ for ", name, "$", sep=""))
## Marginals for theta
if (!is.null(inla$marginals$hyperpar)) {
result$marginals.hyperpar =
inla.extract.el(inla$marginals.hyperpar,
paste("Theta[^ ]+ for ", name, "$", sep=""))
}
## log-tau/kappa/variance/range
if (!is.null(inla$summary.spde3.blc[[name]])) {
rownames(inla$summary.spde3.blc[[name]]) <- BLC.names
result$summary.theta =
inla.extract.el(inla$summary.spde3.blc[[name]],
"theta\\.[^ ]+$")
result$summary.log.tau =
inla.extract.el(inla$summary.spde3.blc[[name]],
"tau\\.[^ ]+$")
result$summary.log.kappa =
inla.extract.el(inla$summary.spde3.blc[[name]],
"kappa\\.[^ ]+$")
result$summary.log.variance.nominal =
inla.extract.el(inla$summary.spde3.blc[[name]],
"variance.nominal\\.[^ ]+$")
result$summary.log.range.nominal =
inla.extract.el(inla$summary.spde3.blc[[name]],
"range.nominal\\.[^ ]+$")
}
## Marginals for log-tau/kappa/variance/range
if (!is.null(inla$marginals.spde3.blc[[name]])) {
names(inla$marginals.spde3.blc[[name]]) <- BLC.names
result$marginals.theta =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"theta\\.[^ ]+$")
result$marginals.log.tau =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"tau\\.[^ ]+$")
result$marginals.log.kappa =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"kappa\\.[^ ]+$")
result$marginals.log.variance.nominal =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"variance.nominal\\.[^ ]+$")
result$marginals.log.range.nominal =
inla.extract.el(inla$marginals.spde3.blc[[name]],
"range.nominal\\.[^ ]+$")
if (do.transform) {
result$marginals.tau =
lapply(result$marginals.log.tau,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.kappa =
lapply(result$marginals.log.kappa,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.variance.nominal =
lapply(result$marginals.log.variance.nominal,
function(x) inla.tmarginal(function(y) exp(y), x))
result$marginals.range.nominal =
lapply(result$marginals.log.range.nominal,
function(x) inla.tmarginal(function(y) exp(y), x))
}
}
return(result)
}
inla.spde3.models = function()
{
return(c("generic", "matern"))
}
## spde.common-connections:
inla.spde.precision.inla.spde3 = inla.spde3.precision
inla.spde.result.inla.spde3 = inla.spde3.result
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.