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-rw-r--r--tests/testthat/setup_script.R33
-rw-r--r--tests/testthat/test_confidence.R149
2 files changed, 129 insertions, 53 deletions
diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R
index 51fea4f6..c3fb4f06 100644
--- a/tests/testthat/setup_script.R
+++ b/tests/testthat/setup_script.R
@@ -1,21 +1,3 @@
-# Copyright (C) 2016-2019 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/>
-
require(mkin)
require(testthat)
@@ -76,5 +58,16 @@ f_SFO_lin_mkin_OLS <- mkinfit(m_synth_SFO_lin, SFO_lin_a, quiet = TRUE)
f_SFO_lin_mkin_ML <- mkinfit(m_synth_SFO_lin, SFO_lin_a, quiet = TRUE,
error_model = "const", error_model_algorithm = "direct")
-fit_obs_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs", quiet = TRUE)
-fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE)
+# We know direct optimization is OK and direct needs 4 sec versus 5.5 for threestep and 6 for IRLS
+fit_obs_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs", quiet = TRUE,
+ error_model_algorithm = "direct")
+# We know threestep is OK, and threestep (and IRLS) need 4.8 se versus 5.6 for direct
+fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE,
+ error_model_algorithm = "threestep")
+
+# We know direct optimization is OK and direct needs 8 sec versus 11 sec for threestep
+f_tc_2 <- mkinfit(m_synth_DFOP_par, DFOP_par_c, error_model = "tc",
+ error_model_algorithm = "direct", quiet = TRUE)
+
+f_tc_2_ntf <- mkinfit(m_synth_DFOP_par, DFOP_par_c, error_model = "tc",
+ transform_fractions = FALSE, error_model_algorithm = "direct", quiet = TRUE)
diff --git a/tests/testthat/test_confidence.R b/tests/testthat/test_confidence.R
index 0423302b..68ff4a98 100644
--- a/tests/testthat/test_confidence.R
+++ b/tests/testthat/test_confidence.R
@@ -1,40 +1,123 @@
-# Copyright (C) 2019 Johannes Ranke
-# Contact: jranke@uni-bremen.de
+context("Confidence intervals and p-values")
-# This file is part of the R package mkin
+test_that("The confint method 'quadratic' is consistent with the summary", {
+ expect_equivalent(
+ confint(f_SFO_lin_mkin_ML, method = "quadratic"),
+ summary(f_SFO_lin_mkin_ML)$bpar[, c("Lower", "Upper")])
-# mkin is free software: you can redistribute it and/or modify it under the
-# terms of the GNU General Public License as published by the Free Software
-# Foundation, either version 3 of the License, or (at your option) any later
-# version.
+ expect_equivalent(
+ confint(f_SFO_lin_mkin_ML, method = "quadratic", backtransform = FALSE),
+ summary(f_SFO_lin_mkin_ML)$par[, c("Lower", "Upper")])
-# 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.
+ f_notrans <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE, transform_rates = FALSE)
+ expect_equivalent(
+ confint(f_notrans, method = "quadratic", transformed = FALSE),
+ summary(f_notrans)$par[, c("Lower", "Upper")])
-# You should have received a copy of the GNU General Public License along with
-# this program. If not, see <http://www.gnu.org/licenses/>
+ expect_equivalent(
+ confint(f_notrans, method = "quadratic", transformed = FALSE),
+ summary(f_notrans)$bpar[, c("Lower", "Upper")])
-context("Confidence intervals and p-values")
+})
+
+test_that("Quadratic confidence intervals for rate constants are comparable to confint.nls", {
+
+ SFO_trans <- function(t, parent_0, log_k_parent_sink) {
+ parent_0 * exp(- exp(log_k_parent_sink) * t)
+ }
+ SFO_notrans <- function(t, parent_0, k_parent_sink) {
+ parent_0 * exp(- k_parent_sink * t)
+ }
+ f_1_nls_trans <- nls(value ~ SFO_trans(time, parent_0, log_k_parent_sink),
+ data = FOCUS_2006_A,
+ start = list(parent_0 = 100, log_k_parent_sink = log(0.1)))
+ f_1_nls_notrans <- nls(value ~ SFO_notrans(time, parent_0, k_parent_sink),
+ data = FOCUS_2006_A,
+ start = list(parent_0 = 100, k_parent_sink = 0.1))
+
+ f_1_mkin_OLS <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE)
+ f_1_mkin_OLS_notrans <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE,
+ transform_rates = FALSE)
+
+ # Check fitted parameter values
+ expect_equivalent(
+ (f_1_mkin_OLS$bparms.optim -coef(f_1_nls_notrans))/f_1_mkin_OLS$bparms.optim,
+ rep(0, 2), tolerance = 1e-6)
+ expect_equivalent(
+ (f_1_mkin_OLS$par[1:2] - coef(f_1_nls_trans))/f_1_mkin_OLS$par[1:2],
+ rep(0, 2), tolerance = 1e-6)
+
+ # Check the standard error for the transformed parameters
+ se_nls <- summary(f_1_nls_trans)$coefficients[, "Std. Error"]
+ # This is of similar magnitude as the standard error obtained with the mkin
+ se_mkin <- summary(f_1_mkin_OLS)$par[1:2, "Std. Error"]
+
+ se_nls_notrans <- summary(f_1_nls_notrans)$coefficients[, "Std. Error"]
+ # This is also of similar magnitude as the standard error obtained with the mkin
+ se_mkin_notrans <- summary(f_1_mkin_OLS_notrans)$par[1:2, "Std. Error"]
+
+ # The difference can partly be explained by the ratio between
+ # the maximum likelihood estimate of the standard error sqrt(rss/n)
+ # and the estimate used in nls sqrt(rss/rdf), i.e. by the factor
+ # sqrt(n/rdf).
+ # In the case of mkin, the residual degrees of freedom are only taken into
+ # account in the subsequent step of generating the confidence intervals for
+ # the parameters (including sigma)
+
+ # Strangely, this only works for the rate constant to less than 1%, but
+ # not for the initial estimate
+ expect_equivalent(se_nls[2] / se_mkin[2], sqrt(8/5), tolerance = 0.01)
+ expect_equivalent(se_nls_notrans[2] / se_mkin_notrans[2], sqrt(8/5), tolerance = 0.01)
+
+ # Another case:
+ f_2_mkin <- mkinfit("DFOP", FOCUS_2006_C, quiet = TRUE)
+ f_2_nls <- nls(value ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = FOCUS_2006_C)
+ se_mkin_2 <- summary(f_2_mkin)$par[1:4, "Std. Error"]
+ se_nls_2 <- summary(f_2_nls)$coefficients[, "Std. Error"]
+ # Here we the ratio of standard errors can be explained by the same
+ # principle up to about 3%
+ expect_equivalent(
+ se_nls_2[c("lrc1", "lrc2")] / se_mkin_2[c("log_k1", "log_k2")],
+ rep(sqrt(nrow(FOCUS_2006_C) / (nrow(FOCUS_2006_C) - 4)), 2),
+ tolerance = 0.03)
+}
+
+test_that("Likelihood profile based confidence intervals work", {
-test_that("Confidence intervals are stable", {
- bpar_1 <- summary(f_SFO_lin_mkin_ML)$bpar[, c("Estimate", "Lower", "Upper")]
- # The reference used here is mkin 0.9.48.1
- bpar_1_mkin_0.9 <- read.table(text =
-"parent_0 102.0000 98.6000 106.0000
-k_parent 0.7390 0.6770 0.8070
-k_M1 0.2990 0.2560 0.3490
-k_M2 0.0202 0.0176 0.0233
-f_parent_to_M1 0.7690 0.6640 0.8480
-f_M1_to_M2 0.7230 0.6030 0.8180",
-col.names = c("parameter", "estimate", "lower", "upper"))
-
- expect_equivalent(signif(bpar_1[1:6, "Estimate"], 3), bpar_1_mkin_0.9$estimate)
-
- # Relative difference of lower bound of the confidence interval is < 0.02
- expect_equivalent(bpar_1[1:6, "Lower"], bpar_1_mkin_0.9$lower,
- scale = bpar_1_mkin_0.9$lower, tolerance = 0.02)
- expect_equivalent(f_SFO_lin_mkin_OLS$bpar, f_SFO_lin_mkin_ML$bpar)
- })
+# f <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
+# f <- fits[["SFO", "FOCUS_C"]]
+# f_notrans <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE, transform_rates = FALSE)
+# pf <- parms(f)
+# f_nll <- function(parent_0, k_parent_sink, sigma) {
+# - f$ll(c(parent_0 = as.numeric(parent_0),
+# k_parent_sink = as.numeric(k_parent_sink),
+# sigma = as.numeric(sigma)))
+# }
+# f_nll(parent_0 = 100, k_parent_sink = 0.3, sigma = 4.7)
+# f_nll(pf[1], pf[2], pf[3])
+# f_mle <- stats4::mle(f_nll, start = as.list(parms(f)), nobs = nrow(FOCUS_2006_C))
+# f_mle <- stats4::mle(f_nll, start = as.list(parms(f)), nobs = 17L)
+# logLik(f_mle)
+# stats4::confint(f_mle, nobs = nrow(FOCUS_2006_C))
+#
+# ci_mkin_1 <- confint(f,
+# method = "quadratic", backtransform = FALSE,
+# transformed = TRUE)
+# ci_maxLik_1 <- maxLik::confint.maxLik(f_maxLik)
+#
+# f_tc_2_maxLik <- maxLik::maxLik(f_tc_2$ll,
+# start = f_tc_2$par)
+# ci_tc_2 <- confint(f_tc_2, method = "quadratic",
+# backtransform = FALSE, transformed = TRUE,
+# distribution = "normal")
+# ci_tc_2_maxLik <- confint(f_tc_2_maxLik)
+# rel_diff_ci_tc_2 <- (ci_tc_2 - ci_tc_2_maxLik)/ci_tc_2_maxLik
+# # The ilr transformed variable appear to misbehave in the numeric differentiation
+# expect_equivalent(
+# rel_diff_ci_tc_2[c(2, 3, 4, 6, 7, 9, 10), ], rep(0, 14),
+# tolerance = 0.05)
+#
+# ci_tc_2[, 1]
+# ci_tc_2_maxLik[, 1]
+})

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