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test_that("Reweighting method 'tc' produces reasonable variance estimates", {
# Check if we can approximately obtain the parameters and the error model
# components that were used in the data generation
# Parent only
DFOP <- mkinmod(parent = mkinsub("DFOP"))
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
parms_DFOP <- c(k1 = 0.2, k2 = 0.02, g = 0.5)
parms_DFOP_optim <- c(parent_0 = 100, parms_DFOP)
d_DFOP <- mkinpredict(DFOP,
parms_DFOP, c(parent = 100),
sampling_times)
d_2_10 <- add_err(d_DFOP,
sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
n = 10, reps = 2, digits = 5, LOD = -Inf, seed = 123456)
d_100_1 <- add_err(d_DFOP,
sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
n = 1, reps = 100, digits = 5, LOD = -Inf, seed = 123456)
# Per default (on my box where I set NOT_CRAN) use all cores minus one
if (identical(Sys.getenv("NOT_CRAN"), "true")) {
n_cores <- parallel::detectCores() - 1
} else {
n_cores <- 1
}
# We are only allowed one core on travis, but they also set NOT_CRAN=true
if (Sys.getenv("TRAVIS") != "") n_cores = 1
# On Windows we would need to make a cluster first
if (Sys.info()["sysname"] == "Windows") n_cores = 1
# Unweighted fits
f_2_10 <- mmkin("DFOP", d_2_10, error_model = "const", quiet = TRUE,
cores = n_cores)
parms_2_10 <- apply(sapply(f_2_10, function(x) x$bparms.optim), 1, mean)
parm_errors_2_10 <- (parms_2_10 - parms_DFOP_optim) / parms_DFOP_optim
expect_true(all(abs(parm_errors_2_10) < 0.12))
f_2_10_tc <- mmkin("DFOP", d_2_10, error_model = "tc", quiet = TRUE,
cores = n_cores)
parms_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$bparms.optim), 1, mean)
parm_errors_2_10_tc <- (parms_2_10_tc - parms_DFOP_optim) / parms_DFOP_optim
expect_true(all(abs(parm_errors_2_10_tc) < 0.05))
tcf_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$errparms), 1, mean, na.rm = TRUE)
tcf_2_10_error_model_errors <- (tcf_2_10_tc - c(0.5, 0.07)) / c(0.5, 0.07)
expect_true(all(abs(tcf_2_10_error_model_errors) < 0.2))
# When we have 100 replicates in the synthetic data, we can roundtrip
# the parameters with < 2% precision
f_tc_100_1 <- mkinfit(DFOP, d_100_1[[1]], error_model = "tc", quiet = TRUE)
parm_errors_100_1 <- (f_tc_100_1$bparms.optim - parms_DFOP_optim) / parms_DFOP_optim
expect_true(all(abs(parm_errors_100_1) < 0.02))
tcf_100_1_error_model_errors <- (f_tc_100_1$errparms - c(0.5, 0.07)) /
c(0.5, 0.07)
# We also get a precision of < 2% for the error model components
expect_true(all(abs(tcf_100_1_error_model_errors) < 0.02))
# Parent and two metabolites
m_synth_DFOP_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"),
M1 = list(type = "SFO", to = "M2"),
M2 = list(type = "SFO"), use_of_ff = "max",
quiet = TRUE)
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
parms_DFOP_lin <- c(k1 = 0.2, k2 = 0.02, g = 0.5,
f_parent_to_M1 = 0.5, k_M1 = 0.3,
f_M1_to_M2 = 0.7, k_M2 = 0.02)
d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,
parms_DFOP_lin,
c(parent = 100, M1 = 0, M2 = 0),
sampling_times)
parms_DFOP_lin_optim = c(parent_0 = 100, parms_DFOP_lin)
d_met_2_15 <- add_err(d_synth_DFOP_lin,
sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
n = 15, reps = 100, digits = 5, LOD = 0.01, seed = 123456)
# For a single fit, we get a relative error of less than 5% in the error
# model components
f_met_2_tc_e4 <- mkinfit(m_synth_DFOP_lin, d_met_2_15[[1]], quiet = TRUE,
error_model = "tc", error_model_algorithm = "direct")
parm_errors_met_2_tc_e4 <- (f_met_2_tc_e4$errparms - c(0.5, 0.07)) / c(0.5, 0.07)
expect_true(all(abs(parm_errors_met_2_tc_e4) < 0.05))
# Doing more takes a lot of computing power
skip_on_travis()
skip_on_cran()
f_met_2_15_tc_e4 <- mmkin(list(m_synth_DFOP_lin), d_met_2_15, quiet = TRUE,
error_model = "tc", cores = n_cores)
parms_met_2_15_tc_e4 <- apply(sapply(f_met_2_15_tc_e4, function(x) x$bparms.optim), 1, mean)
parm_errors_met_2_15_tc_e4 <- (parms_met_2_15_tc_e4[names(parms_DFOP_lin_optim)] -
parms_DFOP_lin_optim) / parms_DFOP_lin_optim
expect_true(all(abs(parm_errors_met_2_15_tc_e4) < 0.015))
tcf_met_2_15_tc <- apply(sapply(f_met_2_15_tc_e4, function(x) x$errparms), 1, mean, na.rm = TRUE)
tcf_met_2_15_tc_error_model_errors <- (tcf_met_2_15_tc - c(0.5, 0.07)) /
c(0.5, 0.07)
# Here we get a precision < 10% for retrieving the original error model components
# from 15 datasets
expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.10))
})
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)
})
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