tests/testthat/helper-data.R

In finnlindgren/excursions: Excursion Sets and Contour Credibility Regions for Random Fields

integration.testdata1 <- function()
{
  n = 11
  Q = Matrix(toeplitz(c(1, -0.1, rep(0, n-2))))
  mu = seq(-5, 5, length=n)
  L = chol(Q)
  seed = 1:6
  a = rep(-3,n)
  b = rep(3,n)
  list(n=n,Q=Q,mu=mu,seed=seed,L=L,a=a,b=b)
}


testdata.inla <- function()
{
  if (require("INLA", quietly=TRUE)) {
    local_exc_safe_inla()
  set.seed(1)
  rho = 0.99
  tau = 15
  n = 11
  n.obs  = 10
  x = 1:n
  mu = 10*((x < n/2)*(x - n/2) + (x >= n/2)*(n/2 - x) + n/4)/n
  Q <- tau*sparseMatrix(i=c(1:n, 2:n), j=c(1:n, 1:(n-1)),
                        x=c(1,rep(1+rho^2, n-2),1, rep(-rho, n-1)),
                        dims=c(n, n), symmetric=TRUE)

  X <- mu + INLA::inla.qsample(1, Q, seed=12345L)[,1]

  obs.loc = c(3,4,6,8,2,9,5,11,7,1)
  A = sparseMatrix(i=1:n.obs, j=obs.loc, x=rep(1, n.obs), dims=c(n.obs, n))
  Y <- as.vector(A %*% X +
                   INLA::inla.qsample(1, Diagonal(n.obs, 1.0),
                                      seed=98765L)[,1])

  ef = list(c(list(ar=x),list(cov=mu)))
  s.obs = INLA::inla.stack(data=list(y=Y), A=list(A), effects=ef, tag="obs")
  s.pre = INLA::inla.stack(data=list(y=NA), A=list(1), effects=ef,tag="pred")
  stack = INLA::inla.stack(s.obs,s.pre)
  formula = y ~ -1 + cov + f(ar,model="ar1")
  result = INLA::inla(formula=formula,
              data = INLA::inla.stack.data(stack),
              control.predictor=list(A=INLA::inla.stack.A(stack),compute=TRUE),
              control.compute = list(config = TRUE),
              num.threads = 1)

  seed = 1:6
  return(list(result=result,stack=stack,seed=seed,n=n))
  }
}

testdata.inla.small <- function()
{
  if (require("INLA", quietly=TRUE)) {
    local_exc_safe_inla()
    set.seed(1)
    rho = 0.99
    tau = 15
    n = 7
    n.obs  = 5
    x = 1:n
    mu = 10*((x < n/2)*(x - n/2) + (x >= n/2)*(n/2 - x) + n/4)/n
    Q <- tau*sparseMatrix(i=c(1:n, 2:n), j=c(1:n, 1:(n-1)),
                          x=c(1,rep(1+rho^2, n-2),1, rep(-rho, n-1)),
                          dims=c(n, n), symmetric=TRUE)

    X <- mu + INLA::inla.qsample(1, Q, seed=12345L)[,1]

    obs.loc = c(3,4,6,2,5)
    A = sparseMatrix(i=1:n.obs, j=obs.loc, x=rep(1, n.obs), dims=c(n.obs, n))
    Y <- as.vector(A %*% X +
                     INLA::inla.qsample(1, Diagonal(n.obs, 1.0),
                                        seed=98765L)[,1])

    ef = list(c(list(ar=x),list(cov=mu)))
    s.obs = INLA::inla.stack(data=list(y=Y), A=list(A), effects=ef, tag="obs")
    s.pre = INLA::inla.stack(data=list(y=NA), A=list(1), effects=ef,tag="pred")
    stack = INLA::inla.stack(s.obs,s.pre)
    formula = y ~ -1 + cov + f(ar,model="ar1")
    result = INLA::inla(formula=formula,
                        data = INLA::inla.stack.data(stack),
                        control.predictor=list(A=INLA::inla.stack.A(stack),compute=TRUE),
                        control.compute = list(config = TRUE))

    seed = 1:6
    return(list(result=result,stack=stack,seed=seed,n=n))
  }
}