context("Nonlinear mixed-effects models")
library(nlme)
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
test_that("nlme_function works correctly", {
m_SFO <- mkinmod(parent = mkinsub("SFO"))
d_SFO_1 <- mkinpredict(m_SFO,
c(k_parent = 0.1),
c(parent = 98), sampling_times)
d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
d_SFO_2 <- mkinpredict(m_SFO,
c(k_parent = 0.05),
c(parent = 102), sampling_times)
d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
d_SFO_3 <- mkinpredict(m_SFO,
c(k_parent = 0.02),
c(parent = 103), sampling_times)
d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
d1 <- add_err(d_SFO_1, function(value) 3, n = 1, seed = 123456)
d2 <- add_err(d_SFO_2, function(value) 2, n = 1, seed = 234567)
d3 <- add_err(d_SFO_3, function(value) 4, n = 1, seed = 345678)
ds <- c(d1 = d1, d2 = d2, d3 = d3)
f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
mean_dp <- mean_degparms(f)
grouped_data <- nlme_data(f)
nlme_f <- nlme_function(f)
# The following assignment was introduced for nlme as evaluated by testthat
# to find the function
assign("nlme_f", nlme_f, pos = globalenv())
m_nlme_raw <- nlme(value ~ SSasymp(time, 0, parent_0, log_k_parent_sink),
data = grouped_data,
fixed = parent_0 + log_k_parent_sink ~ 1,
random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
start = mean_dp)
m_nlme_mkin <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
data = grouped_data,
fixed = parent_0 + log_k_parent_sink ~ 1,
random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
start = mean_dp)
expect_equal(m_nlme_raw$coefficients, m_nlme_mkin$coefficients)
m_nlme_mmkin <- nlme(f)
m_nlme_raw_2 <- nlme(value ~ SSasymp(time, 0, parent_0, log_k_parent),
data = grouped_data,
fixed = parent_0 + log_k_parent ~ 1,
random = pdDiag(parent_0 + log_k_parent ~ 1),
start = mean_degparms(f, random = TRUE))
expect_equal(m_nlme_raw_2$coefficients, m_nlme_mmkin$coefficients)
anova_nlme <- anova(m_nlme_raw, m_nlme_mmkin)
# We get a slightly lower AIC with the improved starting values used within
# nlme.mmkin, specifying also random effects
expect_lt(anova_nlme["m_nlme_mmkin", "AIC"],
anova_nlme["m_nlme_raw", "AIC"])
m_nlme_raw_up_1 <- update(m_nlme_raw, random = log_k_parent_sink ~ 1)
# The following three calls give an error although they should
# do the same as the call above
# The error occurs in the evaluation of the modelExpression in the
# call to .C(fit_nlme, ...)
# m_nlme_mkin_up_1 <- update(m_nlme_mkin, random = log_k_parent_sink ~ 1)
# m_nlme_mkin <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
# data = grouped_data,
# fixed = parent_0 + log_k_parent_sink ~ 1,
# random = log_k_parent_sink ~ 1,
# start = mean_dp)
# update(m_nlme_mmkin, random = pdDiag(log_k_parent_sink ~ 1),
# start = c(parent_0 = 100, log_k_parent_sink = 0.1))
m_nlme_raw_up_2 <- update(m_nlme_raw, random = parent_0 ~ 1)
m_nlme_mkin_up_2 <- update(m_nlme_mkin, random = parent_0 ~ 1)
expect_equal(m_nlme_raw_up_2$coefficients, m_nlme_mkin_up_2$coefficients)
expect_silent(tmp <- update(m_nlme_mkin))
expect_silent(tmp <- update(m_nlme_mmkin))
geomean_dt50_mmkin <- exp(mean(log((sapply(f, function(x) endpoints(x)$distimes["parent", "DT50"])))))
expect_equal(round(endpoints(m_nlme_mmkin)$distimes["parent", "DT50"]), round(geomean_dt50_mmkin))
})
test_that("nlme_function works correctly in other cases", {
skip_on_cran()
dt50_in <- c(400, 800, 1200, 1600, 2000)
k_in <- log(2) / dt50_in
SFO <- mkinmod(parent = mkinsub("SFO"))
pred_sfo <- function(k) {
mkinpredict(SFO,
c(k_parent = k),
c(parent = 100),
sampling_times)
}
ds_me_sfo <- mapply(pred_sfo, k_in, SIMPLIFY = FALSE)
add_err_5 <- function(i) {
add_err(ds_me_sfo[[i]], sdfunc = function(value) 5, n = 3, seed = i + 1)
}
ds_me_sfo_5 <- sapply(1:5, add_err_5)
names(ds_me_sfo_5) <- paste("Dataset", 1:15)
dimnames(ds_me_sfo_5) <- list(Subset = 1:3, DT50 = dt50_in)
f_me_sfo_5 <- mmkin("SFO", ds_me_sfo_5, quiet = TRUE)
ds_me_sfo_5_grouped_mkin <- nlme_data(f_me_sfo_5)
ds_me_sfo_5_mean_dp <- mean_degparms(f_me_sfo_5)
me_sfo_function <- nlme_function(f_me_sfo_5)
assign("me_sfo_function", me_sfo_function, pos = globalenv())
f_nlme_sfo_5_all_mkin <- nlme(value ~ me_sfo_function(name, time,
parent_0, log_k_parent_sink),
data = ds_me_sfo_5_grouped_mkin,
fixed = parent_0 + log_k_parent_sink ~ 1,
random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
start = ds_me_sfo_5_mean_dp)
f_nlme_sfo_5 <- nlme(value ~ SSasymp(time, 0, parent_0, log_k_parent_sink),
data = ds_me_sfo_5_grouped_mkin,
fixed = parent_0 + log_k_parent_sink ~ 1,
random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
start = ds_me_sfo_5_mean_dp)
expect_equal(f_nlme_sfo_5_all_mkin$coefficients, f_nlme_sfo_5$coefficients)
# With less ideal starting values we get fits with lower AIC (not shown)
f_nlme_sfo_5_all_mkin_nostart <- nlme(value ~ me_sfo_function(name, time,
parent_0, log_k_parent_sink),
data = ds_me_sfo_5_grouped_mkin,
fixed = parent_0 + log_k_parent_sink ~ 1,
random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
start = c(parent_0 = 100, log_k_parent_sink = log(0.1)))
f_nlme_sfo_5_nostart <- nlme(value ~ SSasymp(time, 0, parent_0, log_k_parent_sink),
data = ds_me_sfo_5_grouped_mkin,
fixed = parent_0 + log_k_parent_sink ~ 1,
random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
start = c(parent_0 = 100, log_k_parent_sink = log(0.1)))
expect_equal(f_nlme_sfo_5_all_mkin_nostart$coefficients, f_nlme_sfo_5_nostart$coefficients)
})
