## Regression test: when the model uses a time variable other than the first
## one detected in the input (here the rtimes column 'time' next to 'year'),
## frametoLme() must build $fluc on the time variable the model used.
## Bundled data only: Prings05, Pradii03.
data(Prings05, package = "BIOdry", envir = environment())
data(Pradii03, package = "BIOdry", envir = environment())

## ringApply help example: tree-level rtimes, then sample-level scacum
## around the radii measured in 2003; the result has both 'year' and 'time'.
rings_rtimes_scacum <- function() {
    dfm1 <- ringApply(Prings05, lv = 2, fn = "rtimes")
    ringApply(dfm1, lv = "sample", sc.c = Pradii03, rf.t = 2003, fn = "scacum")
}

## the model's time term is 'time', $fluc is built on it, and each $fluc row
## carries the time value of the input row with the same name
expect_fluc_on_time <- function(fit, input) {
    expect_equal(deparse(formula(fit$model)), "log(x) ~ log(csx) + time")
    expect_false(is.null(fit$fluc))
    expect_equal(deparse(nlme::getCovariateFormula(fit$fluc)), "~time")
    md <- as.data.frame(fit$model$data)
    fl <- as.data.frame(fit$fluc)
    expect_equal(nrow(fl), nrow(md))
    expect_false("year" %in% names(fl))
    expect_equal(fl$time, input[rownames(fl), "time"])
    i <- match(rownames(fl), rownames(md))
    expect_false(anyNA(i))
    pr <- residuals(fit$model, type = "pearson")
    expect_equal(fl$tree.res, unname(pr[i]), tolerance = 1e-10)
}

## The fits use the single-level random part of helper-fits.R; the
## three-level default route is checked in test-default-route-3-levels.R.
test_that("$fluc uses 'time' when it is declared with defineDataRoles", {
    rd <- defineDataRoles(rings_rtimes_scacum(), response = "x", time = "time",
                          groups = "plot/tree/sample")
    expect_equal(deparse(tdForm(rd)),
                 "log(x) ~ log(csx) + time | plot/tree/sample")
    expect_fluc_on_time(fit_tree(rd, "time"), rd)
})

test_that("$fluc uses 'time' when the formula string uses it", {
    rd <- rings_rtimes_scacum()
    expect_fluc_on_time(fit_tree(rd, "time"), rd)
})