tests/testthat/test-copy.R

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

test_that("bru: inla copy feature", {
  skip_on_cran()
  local_bru_safe_inla()

  # Seed influences data as well as predict()!
  set.seed(123L)

  df1 <- data.frame(x = cos(1:100))
  df2 <- data.frame(x = sin(1:100))
  df1 <- within(df1, y <- 1 + exp(2 * x) + rnorm(length(x), mean = 0, sd = 0.1))
  df2 <- within(df2, y <- 1 + (6 * x) + rnorm(length(x), mean = 0, sd = 0.1))

  cmp <- ~
    +1 +
      myLin1(x,
        model = "rw1",
        mapper = bru_mapper(fm_mesh_1d(seq(-1, 1, length.out = 100)),
          indexed = FALSE
        )
      ) +
      myLin2(x, copy = "myLin1", fixed = FALSE)
  cmps <- component_list(cmp)

  fit <- bru(
    cmp,
    like(y ~ Intercept + exp(myLin1), family = "gaussian", data = df1, exclude = "myLin2"),
    like(y ~ Intercept + (myLin2), family = "gaussian", data = df2, exclude = "myLin1"),
    options = list(control.inla = list(int.strategy = "eb"))
  )


  expect_equal(
    fit$summary.fixed["Intercept", "mean"],
    1,
    tolerance = midtol
  )
  expect_equal(
    fit$summary.hyperpar["Beta for myLin2", "mean"],
    3,
    tolerance = midtol
  )

  skip_if_not_installed("sn")
  pr <- predict(
    fit,
    data.frame(x = c(0.5, 1)),
    ~myLin2,
    n.samples = 500,
    seed = 1L
  )

  expect_equal(pr[, "mean"], c(3, 6), tolerance = midtol)
})

test_that("Component copy feature", {
  skip_on_cran()
  local_bru_safe_inla()

  # Seed influences data as well as predict()!
  set.seed(123L)

  mydata <- data.frame(
    x1 = rep(1:4, times = 2),
    x2 = rep(c(1, 2), each = 4)
  )
  mydata <- within(mydata, {
    y <- rpois(8, exp(x1^0.5 + x2^0.5 * 2 - 1))
  })

  inlaform <- y ~ -1 +
    f(x1, model = "rw2", values = 1:4, scale.model = TRUE) +
    f(x2, copy = "x1", fixed = FALSE)
  fit <- INLA::inla(
    formula = inlaform,
    data = mydata, family = "poisson",
    inla.mode = bru_options_get("inla.mode"),
    control.compute = list(config = TRUE),
    control.inla = list(int.strategy = "eb")
  )

  cmp <- y ~ -1 +
    x1(x1, model = "rw2", scale.model = TRUE) +
    x2(x2, copy = "x1", fixed = FALSE)
  fit_bru <- bru(
    cmp,
    family = "poisson",
    data = mydata,
    options = list(control.inla = list(int.strategy = "eb"))
  )

  expect_equal(
    fit_bru$summary.hyperpar,
    fit$summary.hyperpar,
    tolerance = midtol
  )
})

test_that("Component copy feature with group", {
  skip_on_cran()
  local_bru_safe_inla()

  # Seed influences data as well as predict()!
  set.seed(123L)

  n <- c(16, 8)
  mydata <- data.frame(
    x1 = rep(seq_len(n[1]), times = n[2]),
    x2 = rep(seq_len(n[2]), each = n[1])
  )
  mydata <- rbind(mydata, mydata)
  mydata <- within(mydata, {
    Intercept <- 1
    z <- rep(c(1, 2), times = length(x1) / 2)
    z2 <- rep(c(1, 2), each = length(x1) / 2)
  })
  mydata <- within(mydata, {
    y <- rnorm(prod(n), x1^1.0 / n[1] * 4 + x2^1.0 / n[1] * 4 * 10, 1)
  })

  inlaform <- y ~ -1 + Intercept +
    f(x1, model = "rw1", values = seq_len(n[1]), scale.model = TRUE, group = z) +
    f(x2, copy = "x1", fixed = FALSE, group = z2)
  fit <- INLA::inla(
    formula = inlaform, data = mydata, family = "normal",
    control.inla = list(int.strategy = "eb"),
    control.compute = list(config = TRUE),
    inla.mode = bru_options_get("inla.mode")
  )

  cmp <- ~ -1 + Intercept +
    x1(x1, model = "rw1", scale.model = TRUE, group = z) +
    x2(x2, copy = "x1", fixed = FALSE, group = z2)
  fit_bru <- bru(
    cmp,
    formula = y ~ Intercept + x1 + x2,
    family = "normal", data = mydata,
    options = list(
      bru_max_iter = 1,
      control.inla = list(int.strategy = "eb")
    )
  )

  cmp2 <- ~ -1 + Intercept +
    x1(x1, model = "rw1", scale.model = TRUE, group = z) +
    x1copy(x2, copy = "x1", fixed = TRUE, group = z2) +
    beta(1, model = "linear")
  fit_bru2 <- bru(
    cmp2,
    formula = y ~ Intercept + x1 + beta * x1copy,
    family = "normal",
    data = mydata,
    options = list(
      bru_max_iter = 5,
      bru_initial = list(beta = 1),
      control.inla = list(int.strategy = "eb")
    )
  )

  expect_equal(
    fit_bru$summary.hyperpar$mean,
    fit$summary.hyperpar$mean,
    tolerance = hitol
  )
  expect_equal(
    fit_bru$summary.hyperpar$sd,
    fit$summary.hyperpar$sd,
    tolerance = hitol
  )
})