context("rbind.mids")

expect_warning(imp1 <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE))
test_that("Constant variables are not imputed by default", {
  expect_equal(sum(is.na(complete(imp1))), 6L)
})

expect_warning(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, remove.constant = FALSE))
test_that("Constant variables are imputed for remove.constant = FALSE", {
  expect_equal(sum(is.na(complete(imp1b))), 0L)
})

imp2 <- mice(nhanes[14:25, ], m = 2, maxit = 1, print = FALSE)
imp3 <- mice(nhanes2, m = 2, maxit = 1, print = FALSE)
imp4 <- mice(nhanes2, m = 1, maxit = 1, print = FALSE)
expect_warning(imp5 <<- mice(nhanes[1:13, ], m = 2, maxit = 2, print = FALSE))
expect_error(imp6 <<- mice(nhanes[1:13, 2:3], m = 2, maxit = 2, print = FALSE), "`mice` detected constant and/or collinear variables. No predictors were left after their removal.")
nh3 <- nhanes
colnames(nh3) <- c("AGE", "bmi", "hyp", "chl")
imp7 <- mice(nh3[14:25, ], m = 2, maxit = 2, print = FALSE)
expect_warning(imp8 <<- mice(nhanes[1:13, ], m = 2, maxit = 2, print = FALSE))
imp9 <- mice(nhanes,
  m = 2, maxit = 1, print = FALSE,
  ignore = c(rep(FALSE, 20), rep(TRUE, 5))
)

mylist <- list(age = NA, bmi = NA, hyp = NA, chl = NA)
nhalf <- nhanes[13:25, ]

test_that("Expands number of rows and imputes", {
  expect_equal(nrow(complete(rbind(imp1, imp2))), 25L)
  expect_equal(nrow(rbind(imp1, imp2)$imp$bmi), 9L)
})

test_that("throws error", {
  expect_error(rbind(imp1, imp3), "datasets have different factor variables")
  expect_error(rbind(imp3, imp4), "number of imputations differ")
  expect_error(rbind(imp1, imp7), "datasets have different variable names")
})
test_that("throws warning", {
  expect_warning(
    rbind(imp1, imp5),
    "iterations differ, so no convergence diagnostics calculated"
  )
  expect_warning(
    rbind(imp5, imp9),
    "iterations differ, so no convergence diagnostics calculated"
  )
})

r1 <- rbind(imp8, imp5)
r2 <- rbind(imp1, mylist)
r3 <- rbind(imp1, nhalf)
r4 <- rbind(imp1, imp2)
r5 <- rbind(imp2, imp9)

test_that("Produces longer imputed data", {
  expect_identical(nrow(complete(r1)), 26L)
  expect_identical(nrow(complete(r2)), 14L)
})

test_that("Constant variables are not imputed", {
  expect_equal(sum(is.na(complete(r3))), 15L)
  expect_equal(sum(is.na(complete(r4))), 6L)
})

test_that("`ignore` is correctly appended", {
  expect_equal(r2$ignore, rep(FALSE, 14))
  expect_equal(r5$ignore, c(rep(FALSE, 32), rep(TRUE, 5)))
})

# r11 <- mice.mids(rbind(imp1, imp5), print = FALSE)
# test_that("plot throws error on convergence diagnostics", {
#   expect_error(plot(r11), "no convergence diagnostics found")
#   })

r21 <- mice.mids(r2, print = FALSE)
r31 <- mice.mids(r3, print = FALSE)

# issue #59
set.seed <- 818
x <- rnorm(10)
D <- data.frame(x = x, y = 2 * x + rnorm(10))
D[c(2:4, 7), 1] <- NA
expect_error(D_mids <<- mice(D[1:5, ], print = FALSE),
             "`mice` detected constant and/or collinear variables. No predictors were left after their removal.")
expect_warning(D_mids <<- mice(D[1:5, ], print = FALSE, remove.collinear = FALSE))

D_rbind <- mice:::rbind.mids(D_mids, D[6:10, ])
cmp <- complete(D_rbind, 1)
test_that("Solves issue #59, rbind", expect_identical(cmp[6:10, ], D[6:10, ]))

test_that("rbind does not throw a warning (#114)", {
  expect_silent(rbind(ordered(c(1, 2))))
})

# calculate chainMean and chainVar
# imp1 <- mice(nhanes[1:13, ], m = 5, maxit = 25, print = FALSE, seed = 123)
# imp2 <- mice(nhanes[14:25, ], m = 5, maxit = 25, print = FALSE, seed = 456)
# z <- rbind(imp1, imp2)
# plot(z)
#
# imp3 <- mice(nhanes, m = 5, maxit = 25, print = FALSE, seed = 123)
# plot(imp3)
#
# An interesting observation is that the SD(hyp, a) < SD(hyp, imp3). This is
# because SD(hyp, imp1) = 0.

# issue 319: https://github.com/amices/mice/issues/319
# impute a subset
# do not touch or impute non-selected rows
# return full data as mids object

# example: impute even rows only
data <- nhanes
odd <- as.logical((1:nrow(data)) %% 2)

# method 1: ignore + where
where <- make.where(data)
where[odd, ] <- FALSE
imp1 <- mice(nhanes, ignore = odd, where = where, seed = 1, m = 2, print = FALSE)
c1 <- complete(imp1, 2)

# method 2: filter + rbind
imp2 <- mice(data[!odd, ], seed = 1, m = 2, print = FALSE)
imp2 <- rbind(imp2, data[odd, ])
idx <- order(as.numeric(rownames(imp2$data)))
imp2$data <- imp2$data[idx, ]
imp2$where <- imp2$where[idx, ]
c2 <- complete(imp2, 2)

test_that("ignore + where is identical to filter + rbind (#319)", {
  expect_identical(c1, c2)
})