context("Merge partR2 objects")

data(biomass)
# scale data
# biomass[] <- lapply(biomass, function(x) if (is.double(x)) scale(x) else x)
biomass2 <- biomass %>%
  dplyr::mutate(dplyr::across(
    c(Temperature, Precipitation, SpeciesDiversity, Year),
    function(x) (x - mean(x, na.rm = TRUE)) / sd(x, na.rm = TRUE)
  ))

# Full model
mod_full <- lme4::lmer(Biomass ~ Year + Temperature * Precipitation + SpeciesDiversity + (1 | Population),
  data = biomass2
)

# Semi-partial R2 for interaction and all other predictors of interest
R2_full <- partR2(mod_full,
  partvars = c("Temperature:Precipitation", "SpeciesDiversity", "Year"),
  data = biomass2
)

# model without interaction to get main effect semi-partial R2s
mod_noIA <- lme4::lmer(Biomass ~ Year + Temperature + Precipitation + SpeciesDiversity + (1 | Population),
  data = biomass2
)
R2_noIA <- partR2(mod_noIA,
  partvars = c("Temperature", "Precipitation"),
  data = biomass2
)

# combine both
R2_comb <- mergeR2(R2_full, R2_noIA)

test_that("mergeR2 results in correct number of terms", {
  expect_true(all(R2_comb$R2$term %in% unique(c(R2_full$R2$term, R2_noIA$R2$term))))
  expect_true(all(R2_comb$R2_boot$term %in% unique(c(R2_full$R2_boot$term, R2_noIA$R2_boot$term))))
})