# Copyright (C) 2018,2019 Johannes Ranke
# Contact: jranke@uni-bremen.de

# This file is part of the R package mkin

# mkin is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.

# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
# details.

# You should have received a copy of the GNU General Public License along with
# this program. If not, see <http://www.gnu.org/licenses/>

context("Error model fitting")

m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
                           M1 = mkinsub("SFO", "M2"),
                           M2 = mkinsub("SFO"),
                           use_of_ff = "max", quiet = TRUE)

m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
                           M1 = mkinsub("SFO"),
                           M2 = mkinsub("SFO"),
                           use_of_ff = "max", quiet = TRUE)

SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data

DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data

test_that("Error model 'const' works", {
  skip_on_cran()
  fit_const_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "const", quiet = TRUE)
  bpar_1 <- fit_const_1$bparms.optim
  # The reference used here is mkin 0.9.48.1
  bpar_1_mkin_0.9 <-   read.table(text =
"parent_0       102.0000
k_parent         0.7390
k_M1             0.2990
k_M2             0.0202
f_parent_to_M1   0.7690
f_M1_to_M2       0.7230",
col.names = c("parameter", "estimate"))

  expect_equivalent(signif(bpar_1, 3), bpar_1_mkin_0.9$estimate)
})

test_that("Error model 'obs' works", {
  skip_on_cran()
  fit_obs_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs", quiet = TRUE)
  parms_2 <- round(fit_obs_1$bparms.optim, c(1, 4, 4, 4, 4, 4))
  expect_equivalent(parms_2, c(102.1, 0.7389, 0.2982, 0.0203, 0.7677, 0.7246))
})

test_that("Error model 'tc' works", {
  skip_on_cran()
  fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE)
  parms_3 <- round(fit_tc_1$bparms.optim, c(1, 4, 4, 4, 4, 4))
  expect_equivalent(parms_3, c(102.1, 0.7393, 0.2992, 0.0202, 0.7687, 0.7229))
})

test_that("The different error model fitting methods work for parent fits", {
  skip_on_cran()

  f_9_OLS <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
                     quiet = TRUE)
  expect_equivalent(round(AIC(f_9_OLS), 2), 137.43)

  f_9_direct <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
    error_model = "tc", error_model_algorithm = "direct", quiet = TRUE)
  expect_equivalent(round(AIC(f_9_direct), 2), 134.94)

  f_9_twostep <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
    error_model = "tc", error_model_algorithm = "twostep", quiet = TRUE)
  expect_equivalent(round(AIC(f_9_twostep), 2), 134.94)

  f_9_threestep <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
    error_model = "tc", error_model_algorithm = "threestep", quiet = TRUE)
  expect_equivalent(round(AIC(f_9_threestep), 2), 139.43)

  f_9_fourstep <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
    error_model = "tc", error_model_algorithm = "fourstep", quiet = TRUE)
  expect_equivalent(round(AIC(f_9_fourstep), 2), 139.43)

  f_9_IRLS <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
    error_model = "tc", error_model_algorithm = "IRLS", quiet = TRUE)
  expect_equivalent(round(AIC(f_9_IRLS), 2), 139.43)

  f_9_d_3 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
    error_model = "tc", error_model_algorithm = "d_3", quiet = TRUE)
  expect_equivalent(round(AIC(f_9_d_3), 2), 134.94)
})

test_that("The default error model algorithm finds the best known AIC values for parent fits", {
  skip_on_cran()
  f_tc_exp_d_3 <- mmkin(c("SFO", "DFOP", "HS"),
    lapply(experimental_data_for_UBA_2019, function(x) x$data),
    error_model = "tc",
    error_model_algorithm = "d_3",
    quiet = TRUE)

  AIC_exp_d_3 <- lapply(f_tc_exp_d_3, AIC)
  AIC_exp_d_3 <- lapply(AIC_exp_d_3, round, 1)
  dim(AIC_exp_d_3) <- dim(f_tc_exp_d_3)
  dimnames(AIC_exp_d_3) <- dimnames(f_tc_exp_d_3)

  expect_known_output(AIC_exp_d_3, "AIC_exp_d_3.out")
})