# Copyright (C) 2015,2018 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("Fitting of parent only models") calc_dev.percent <- function(fitlist, reference, endpoints = TRUE, round_results = NULL) { dev.percent <- list() for (i in 1:length(fitlist)) { fit <- fitlist[[i]] if (endpoints) { results <- c(fit$bparms.optim, endpoints(fit)$distimes$DT50, endpoints(fit)$distimes$DT90) } else { results <- fit$bparms.optim } if (!missing(round_results)) results <- round(results, round_results) dev.percent[[i]] <- abs(100 * ((reference - results)/reference)) } return(dev.percent) } SFO <- mkinmod(parent = list(type = "SFO")) FOMC <- mkinmod(parent = list(type = "FOMC")) DFOP <- mkinmod(parent = list(type = "DFOP")) HS <- mkinmod(parent = list(type = "HS")) SFORB <- mkinmod(parent = list(type = "SFORB")) test_that("Fits for FOCUS A deviate less than 0.1% from median of values from FOCUS report", { fit.A.SFO <- list(mkinfit("SFO", FOCUS_2006_A, quiet = TRUE)) median.A.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, dataset == "A", c(M0, k, DT50, DT90)), "median")) dev.percent.A.SFO <- calc_dev.percent(fit.A.SFO, median.A.SFO) expect_equivalent(dev.percent.A.SFO[[1]] < 0.1, rep(TRUE, 4)) # Fitting FOCUS A with FOMC is possible, but the correlation between # alpha and beta, when obtained, is 1.0000, and the fit does not # always converge using the Port algorithm (platform dependent), so # we need to suppress a potential warning suppressWarnings(fit.A.FOMC <- try(list(mkinfit("FOMC", FOCUS_2006_A, quiet = TRUE)))) if (!inherits(fit.A.FOMC, "try-error")) { median.A.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, dataset == "A", c(M0, alpha, beta, DT50, DT90)), "median")) dev.percent.A.FOMC <- calc_dev.percent(fit.A.FOMC, median.A.FOMC) # alpha and are beta ill-determined, do not compare those expect_equivalent(dev.percent.A.FOMC[[1]][c(1, 4, 5)] < 0.1, rep(TRUE, 3)) } fit.A.DFOP <- list(mkinfit("DFOP", FOCUS_2006_A, quiet = TRUE)) median.A.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, dataset == "A", c(M0, k1, k2, f, DT50, DT90)), "median")) dev.percent.A.DFOP <- calc_dev.percent(fit.A.DFOP, median.A.DFOP) #expect_equivalent(dev.percent.A.DFOP[[1]] < 0.1, rep(TRUE, 6)) # g/f is ill-determined expect_equivalent(dev.percent.A.DFOP[[1]][c(1, 2, 3, 5, 6)] < 0.1, rep(TRUE, 5)) fit.A.HS <- list(mkinfit("HS", FOCUS_2006_A, quiet = TRUE)) median.A.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, dataset == "A", c(M0, k1, k2, tb, DT50, DT90)), "median")) dev.percent.A.HS <- calc_dev.percent(fit.A.HS, median.A.HS) expect_equivalent(dev.percent.A.HS[[1]] < 0.1, rep(TRUE, 6)) }) test_that("Fits for FOCUS B deviate less than 0.1% from median of values from FOCUS report", { skip_on_cran() fit.B.SFO <- list(mkinfit("SFO", FOCUS_2006_B, quiet = TRUE)) median.B.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, dataset == "B", c(M0, k, DT50, DT90)), "median")) dev.percent.B.SFO <- calc_dev.percent(fit.B.SFO, median.B.SFO) expect_equivalent(dev.percent.B.SFO[[1]] < 0.1, rep(TRUE, 4)) fit.B.FOMC <- list(mkinfit("FOMC", FOCUS_2006_B, quiet = TRUE)) median.B.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, dataset == "B", c(M0, alpha, beta, DT50, DT90)), "median")) dev.percent.B.FOMC <- calc_dev.percent(fit.B.FOMC, median.B.FOMC) expect_equivalent(dev.percent.B.FOMC[[1]] < 0.1, rep(TRUE, 5)) fit.B.DFOP <- list(mkinfit("DFOP", FOCUS_2006_B, quiet = TRUE)) median.B.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, dataset == "B", c(M0, k1, k2, f, DT50, DT90)), "median")) dev.percent.B.DFOP <- calc_dev.percent(fit.B.DFOP, median.B.DFOP) #expect_equivalent(dev.percent.B.DFOP[[1]] < 0.1, rep(TRUE, 6)) # g/f is ill-determined expect_equivalent(dev.percent.B.DFOP[[1]][c(1, 2, 3, 5, 6)] < 0.1, rep(TRUE, 5)) fit.B.HS <- list(mkinfit("HS", FOCUS_2006_B, quiet = TRUE)) median.B.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, dataset == "B", c(M0, k1, k2, tb, DT50, DT90)), "median", na.rm = TRUE)) dev.percent.B.HS <- calc_dev.percent(fit.B.HS, median.B.HS) expect_equivalent(dev.percent.B.HS[[1]] < 0.1, rep(TRUE, 6)) fit.B.SFORB <- list(mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE)) dev.percent.B.SFORB <- calc_dev.percent(fit.B.SFORB, median.B.DFOP) expect_equivalent(dev.percent.B.SFORB[[1]][c(1, 5, 6)] < 0.1, rep(TRUE, 3)) }) test_that("Fits for FOCUS C deviate less than 0.1% from median of values from FOCUS report", { fit.C.SFO <- list(mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)) median.C.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, dataset == "C", c(M0, k, DT50, DT90)), "median")) dev.percent.C.SFO <- calc_dev.percent(fit.C.SFO, median.C.SFO) expect_equivalent(dev.percent.C.SFO[[1]] < 0.1, rep(TRUE, 4)) fit.C.FOMC <- list(mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)) median.C.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, dataset == "C", c(M0, alpha, beta, DT50, DT90)), "median")) dev.percent.C.FOMC <- calc_dev.percent(fit.C.FOMC, median.C.FOMC, round_results = 2) # Not enough precision in FOCUS results expect_equivalent(dev.percent.C.FOMC[[1]] < 0.1, rep(TRUE, 5)) fit.C.HS <- list(mkinfit("HS", FOCUS_2006_C, quiet = TRUE)) median.C.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, dataset == "C", c(M0, k1, k2, tb, DT50, DT90)), "median")) dev.percent.C.HS <- calc_dev.percent(fit.C.HS, median.C.HS, round_results = c(2, 4, 6, 2, 2)) # Not enouth precision in k2 available expect_equivalent(dev.percent.C.HS[[1]] < c(0.1, 0.1, 0.3, 0.1, 0.1, 0.1), rep(TRUE, 6)) }) test_that("SFO fits give approximately (0.001%) equal results with different solution methods", { skip_on_cran() fit.A.SFO.default <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE)$bparms.optim fits.A.SFO <- list() fits.A.SFO[[1]] <- mkinfit(SFO, FOCUS_2006_A, quiet = TRUE) fits.A.SFO[[2]] <- mkinfit(SFO, FOCUS_2006_A, quiet = TRUE, solution_type = "eigen") fits.A.SFO[[3]] <- mkinfit(SFO, FOCUS_2006_A, quiet = TRUE, solution_type = "deSolve") dev.percent <- calc_dev.percent(fits.A.SFO, fit.A.SFO.default, endpoints = FALSE) expect_equivalent(dev.percent[[1]] < 0.001, rep(TRUE, 2)) expect_equivalent(dev.percent[[2]] < 0.001, rep(TRUE, 2)) expect_equivalent(dev.percent[[3]] < 0.001, rep(TRUE, 2)) }) test_that("FOMC fits give approximately (0.001%) equal results with different solution methods", { skip_on_cran() fit.C.FOMC.default <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)$bparms.optim fits.C.FOMC <- list() fits.C.FOMC[[1]] <- mkinfit(FOMC, FOCUS_2006_C, quiet = TRUE) fits.C.FOMC[[2]] <- mkinfit(FOMC, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve") dev.percent <- calc_dev.percent(fits.C.FOMC, fit.C.FOMC.default, endpoints = FALSE) expect_equivalent(dev.percent[[1]] < 0.001, rep(TRUE, 3)) expect_equivalent(dev.percent[[2]] < 0.001, rep(TRUE, 3)) }) test_that("DFOP fits give approximately (0.001%) equal results with different solution methods", { skip_on_cran() fit.C.DFOP.default <- mkinfit("DFOP", FOCUS_2006_C, quiet = TRUE)$bparms.optim fits.C.DFOP <- list() fits.C.DFOP[[1]] <- mkinfit(DFOP, FOCUS_2006_C, quiet = TRUE) fits.C.DFOP[[2]] <- mkinfit(DFOP, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve") dev.percent <- calc_dev.percent(fits.C.DFOP, fit.C.DFOP.default, endpoints = FALSE) expect_equivalent(dev.percent[[1]] < 0.001, rep(TRUE, 4)) expect_equivalent(dev.percent[[2]] < 0.001, rep(TRUE, 4)) }) test_that("SFORB fits give approximately (0.002%) equal results with different solution methods", { skip_on_cran() fit.B.SFORB.default <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE)$bparms.optim fits.B.SFORB <- list() fits.B.SFORB[[1]] <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE, solution_type = "eigen") fits.B.SFORB[[2]] <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE, solution_type = "deSolve") dev.percent <- calc_dev.percent(fits.B.SFORB, fit.B.SFORB.default, endpoints = FALSE) expect_equivalent(dev.percent[[1]] < 0.001, rep(TRUE, 4)) expect_equivalent(dev.percent[[2]] < 0.002, rep(TRUE, 4)) })