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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))
})
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