From 7624a2b8398b4ad665a3b0b622488e1893a5ee7c Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 21 Oct 2019 12:11:34 +0200 Subject: Refactor mkinfit, infrastructure work mkinfit objects now include an ll() function to calculate the log-likelihood. Part of the code was refactored, hopefully making it easier to read and maintain. IRLS is currently the default algorithm for the error model "obs", for no particular reason. This may be subject to change when I get around to investigate. Slow tests are now in a separate subdirectory and will probably only be run by my own Makefile target. Formatting of test logs is improved. Roundtripping error model parameters works with a precision of 10% when we use lots of replicates in the synthetic data (see slow tests). This is not new in this commit, but as I think it is reasonable this closes #7. --- tests/testthat/slow/test_parent_only.R | 218 +++++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 tests/testthat/slow/test_parent_only.R (limited to 'tests/testthat/slow/test_parent_only.R') diff --git a/tests/testthat/slow/test_parent_only.R b/tests/testthat/slow/test_parent_only.R new file mode 100644 index 00000000..7521e145 --- /dev/null +++ b/tests/testthat/slow/test_parent_only.R @@ -0,0 +1,218 @@ +# 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 + +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)) +}) -- cgit v1.2.1