context("piecewise")
# Simulate data from a piecewise logistic trend
ts <- mvgam:::piecewise_logistic(t = 1:100,
                                 cap = 8.5,
                                 deltas = extraDistr::rlaplace(10, 0, 0.025),
                                 k = 0.075,
                                 m = 0,
                                 changepoint_ts = sample(1:100, 10))
y <- rnorm(100, ts, 0.75)

# Don't put 'cap' variable in dataframe
df <- data.frame(y = y,
                 time = 1:100,
                 series = as.factor('series1'),
                 #cap = 8.75,
                 fake = rnorm(100))

test_that("logistic should error if cap is missing", {
  expect_error(mvgam(formula = y ~ 0,
                     data = df,
                     trend_model = PW(growth = 'logistic',
                                      n_changepoints = 10),
                     # priors = prior(normal(2, 5), class = k_trend),
                     family = gaussian(),
                     run_model = TRUE,
                     return_model_data = TRUE),
               'Capacities must be supplied as a variable named "cap" for logistic growth')
})

# Now include some missing values in 'cap'
df <- data.frame(y = y,
                 time = 1:100,
                 series = as.factor('series1'),
                 cap = sample(c(8.75, NA), 100, TRUE),
                 fake = rnorm(100))

test_that("logistic should error if cap has NAs", {
  expect_error(mvgam(formula = y ~ 0,
                     data = df,
                     trend_model = PW(growth = 'logistic',
                                      n_changepoints = 10),
                     priors = prior(normal(2, 5), class = k_trend),
                     family = gaussian(),
                     run_model = TRUE,
                     return_model_data = TRUE),
               'Missing values found for some "cap" terms')
})

# Missing values can also happen when transforming to the link scale
y <- rpois(100, ts + 5)
df <- data.frame(y = y,
                 time = 1:100,
                 series = as.factor('series1'),
                 cap = -1,
                 fake = rnorm(100))

test_that("logistic should error if cap has NAs after link transformation", {
  expect_error(mvgam(formula = y ~ 0,
                     data = df,
                     trend_model = PW(growth = 'logistic',
                                      n_changepoints = 10),
                     family = poisson(),
                     run_model = TRUE,
                     return_model_data = TRUE),
               paste0('Missing or infinite values found for some "cap" terms\n',
               'after transforming to the log link scale'))
})

# Make sure cap is in the right order
y <- rpois(100, ts + 5)
df <- rbind(data.frame(y = y,
                 time = 1:100,
                 series = as.factor('series1'),
                 cap = y + 20,
                 fake = rnorm(100)),
            data.frame(y = y + 2,
                       time = 1:100,
                       series = as.factor('series2'),
                       cap = y + 22,
                       fake = rnorm(100)))

test_that("logistic caps should be included in the correct order", {
  skip_on_cran()
  mod <- mvgam(formula = y ~ 0,
                     data = df,
                     trend_model = PW(growth = 'logistic',
                                      n_changepoints = 10),
                     family = poisson(),
                     run_model = FALSE,
                     return_model_data = TRUE)

  # caps should now be logged and in a matrix [1:n_timepoints, 1:n_series]
  expect_true(all(mod$model_data$cap ==
        log(cbind(df %>%
                    dplyr::filter(series == 'series1') %>%
                    dplyr::arrange(time) %>%
                    dplyr::pull(cap),
                  df %>%
                    dplyr::filter(series == 'series2') %>%
                    dplyr::arrange(time) %>%
                    dplyr::pull(cap)))))
})