library(lme4)
data(Socatt, package = "mlmRev")
Socatt$religion <- relevel(Socatt$religion, ref = "none")
Socatt$rv <- as.numeric(as.character(Socatt$numpos))
Socatt$rv <- scale(Socatt$rv) # a plot shows this is clearly non-normal
# ==============================================================================
context("residual bootstrap (lmerMod)")
# ==============================================================================
mySumm <- function(.) {
s <- lme4::getME(., "sigma")
c(beta = lme4::getME(., "beta"), sigma = s, sig01 = unname(s * lme4::getME(., "theta")))
}
nsim <- 10
test_that("two-level additive random intercept model",{
skip_on_cran()
## See p. 31 of Goldstein's book
vcmodA <- lme4::lmer(mathAge11 ~ mathAge8 + gender + class +
(1 | school), data = jsp728)
orig.stats <- mySumm(vcmodA)
set.seed(7142015)
boo <- resid_bootstrap(model = vcmodA, .f = mySumm, B = nsim)
expect_equal(class(boo), "lmeresamp")
expect_equal(boo$observed, orig.stats)
expect_equal(unname(boo$stats$observed), unname(orig.stats))
expect_equal(nrow(boo$replicates), nsim)
expect_equal(ncol(boo$replicates), length(orig.stats))
expect_equal(boo$B, nsim)
expect_equal(boo$type, "residual")
expect_equal(boo$.f, mySumm)
})
# ------------------------------------------------------------------------------
test_that("two-level random intercept model without interaction",{
skip_on_cran()
## See p. 97 of Goldstein's book
rimod <- lmer(normAge11 ~ mathAge8c + gender + class +
(1 | school), data = jsp728)
orig.stats <- mySumm(rimod)
boo <- resid_bootstrap(model = rimod, .f = mySumm, B = nsim)
expect_equal(class(boo), "lmeresamp")
expect_equal(boo$observed, orig.stats)
expect_equal(unname(boo$stats$observed), unname(orig.stats))
expect_equal(nrow(boo$replicates), nsim)
expect_equal(ncol(boo$replicates), length(orig.stats))
expect_equal(boo$B, nsim)
expect_equal(boo$type, "residual")
expect_equal(boo$.f, mySumm)
})
test_that("two-level random intercept model with interaction",{
skip_on_cran()
## See p. 34 of Goldstein's book
vcmodC <- lmer(mathAge11 ~ mathAge8 * schoolMathAge8 + gender + class +
(1 | school), data = jsp728)
orig.stats <- mySumm(vcmodC)
boo <- resid_bootstrap(model = vcmodC, .f = mySumm, B = nsim)
expect_equal(class(boo), "lmeresamp")
expect_equal(boo$observed, orig.stats)
expect_equal(unname(boo$stats$observed), unname(orig.stats))
expect_equal(nrow(boo$replicates), nsim)
expect_equal(ncol(boo$replicates), length(orig.stats))
expect_equal(boo$B, nsim)
expect_equal(boo$type, "residual")
expect_equal(boo$.f, mySumm)
})
# ------------------------------------------------------------------------------
test_that("two-level random coefficient model with interaction",{
skip_on_cran()
## See p. 35 of Goldstein's book
rcmod <- lme4::lmer(mathAge11 ~ mathAge8c * schoolMathAge8 + gender + class +
(mathAge8c | school), data = jsp728)
orig.stats <- mySumm(rcmod)
boo <- resid_bootstrap(model = rcmod, .f = mySumm, B = nsim)
expect_equal(class(boo), "lmeresamp")
expect_equal(boo$observed, orig.stats)
expect_equal(unname(boo$stats$observed), unname(orig.stats))
expect_equal(nrow(boo$replicates), nsim)
expect_equal(ncol(boo$replicates), length(orig.stats))
expect_equal(boo$B, nsim)
expect_equal(boo$type, "residual")
expect_equal(boo$.f, mySumm)
})
# ------------------------------------------------------------------------------
test_that("three-level random intercept model",{
skip_on_cran()
rmA <- lmer(rv ~ religion + year + (1 | respond) + (1 | district), data = Socatt)
orig.stats <- mySumm(rmA)
boo <- resid_bootstrap(model = rmA, .f = mySumm, B = nsim)
expect_equal(class(boo), "lmeresamp")
expect_equal(boo$observed, orig.stats)
expect_equal(unname(boo$stats$observed), unname(orig.stats))
expect_equal(nrow(boo$replicates), nsim)
expect_equal(ncol(boo$replicates), length(orig.stats))
expect_equal(boo$B, nsim)
expect_equal(boo$type, "residual")
expect_equal(boo$.f, mySumm)
})
# ==============================================================================
context("residual bootstrap (glmerMod)")
# ==============================================================================
mySumm <- function(.) {
c(beta = getME(., "beta"), sig01 = unname(getME(., "theta")))
}
test_that("two-level binomial logistic regression",{
skip_on_cran()
gm <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial)
orig.stats <- mySumm(gm)
boo <- resid_bootstrap(model = gm, .f = mySumm, B = nsim)
expect_equal(class(boo), "lmeresamp")
expect_equal(boo$observed, orig.stats)
expect_equal(unname(boo$stats$observed), unname(orig.stats))
expect_equal(nrow(boo$replicates), nsim)
expect_equal(ncol(boo$replicates), length(orig.stats))
expect_equal(boo$B, nsim)
expect_equal(boo$type, "residual")
expect_equal(boo$.f, mySumm)
})
# ------------------------------------------------------------------------------
test_that("two-level poisson regression model",{
skip_on_cran()
gm <- glmer(TICKS ~ YEAR + cHEIGHT + (1|LOCATION),
family="poisson",data=grouseticks)
orig.stats <- mySumm(gm)
boo <- resid_bootstrap(model = gm, .f = mySumm, B = nsim)
expect_equal(class(boo), "lmeresamp")
expect_equal(boo$observed, orig.stats)
expect_equal(unname(boo$stats$observed), unname(orig.stats))
expect_equal(nrow(boo$replicates), nsim)
expect_equal(ncol(boo$replicates), length(orig.stats))
expect_equal(boo$B, nsim)
expect_equal(boo$type, "residual")
expect_equal(boo$.f, mySumm)
})
test_that("three-level poisson regression model",{
skip_on_cran()
gm <- glmer(TICKS ~ YEAR + cHEIGHT + (1|LOCATION/BROOD),
family="poisson", data=grouseticks)
orig.stats <- mySumm(gm)
boo <- resid_bootstrap(model = gm, .f = mySumm, B = nsim)
expect_equal(class(boo), "lmeresamp")
expect_equal(boo$observed, orig.stats)
expect_equal(unname(boo$stats$observed), unname(orig.stats))
expect_equal(nrow(boo$replicates), nsim)
expect_equal(ncol(boo$replicates), length(orig.stats))
expect_equal(boo$B, nsim)
expect_equal(boo$type, "residual")
expect_equal(boo$.f, mySumm)
})
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