tests/testthat/test-variance.R

context("variance")
test_that("matW", {

  expectEqual <- function(x, y) {
    testthat::expect_equal(x, y, tolerance = 1e-06)
  }

  library(saeSim)

  set.seed(1)
  dat <- base_id(10, 1) %>%
    sim_gen_e() %>%
    sim_gen_x() %>%
    sim_gen_v() %>%
    sim_resp_eq(y = 100 + 2 * x + v + e) %>%
    as.data.frame

  dat$samplingVar <- 1

  modelFit <- rfh(y ~ x, dat, "samplingVar")
  out <- predict(modelFit)

  # This should be the same as Xb + u which is used in predict:
  W <-  weights(modelFit)$W

  prediction <- W %*% modelFit$y

  expectEqual(
    as.numeric(out$reblup),
    as.numeric(prediction)
  )

  expectEqual(
    as.numeric(rowSums(W)),
    rep(1, 10)
  )

  expectEqual(
    modelFit$x %*% weights(modelFit)$A + # XA
      weights(modelFit)$B %*% ( # B
        Diagonal(length(modelFit$y)) - # I -
          modelFit$x %*% weights(modelFit)$A), # XA
    weights(modelFit)$W
  )

})

test_that("matWbc", {

  expectEqual <- function(x, y) {
    testthat::expect_equal(x, y, tolerance = 1e-06, check.attributes = FALSE)
  }

  library(saeSim)

  set.seed(1)
  dat <- base_id(10, 1) %>%
    sim_gen_e() %>%
    sim_gen_x() %>%
    sim_gen_v() %>%
    sim_resp_eq(y = 100 + 2 * x + v + e) %>%
    as.data.frame

  dat$samplingVar <- 1

  modelFit <- rfh(y ~ x, dat, "samplingVar")
  modelFit$re[1] <- 1000
  modelFit$re[2] <- -1000
  modelFit$reblup[1] <- 1000
  modelFit$reblup[2] <- -1000

  out <- predict(modelFit, typ = c("reblupbc", "reblup"))

  # This should be the same as Xb + u which is used in predict:
  Wbc <-  weights(modelFit)$Wbc
  W <-  weights(modelFit)$W

  expectEqual(
    Wbc[1, ],
    c(1 + 1 / modelFit$y[1], rep(0, 9))
  )

  expectEqual(
    Wbc[2, ],
    c(0, 1 - 1 / modelFit$y[2], rep(0, 8))
  )

  expectEqual(
    Wbc[3, ],
    W[3, ]
  )

  expectEqual(
    out$reblupbc[1],
    modelFit$y[1] + 1
  )

  expectEqual(
    out$reblupbc[2],
    modelFit$y[2] - 1
  )

})

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saeRobust documentation built on May 2, 2019, 7:29 a.m.