tests/testthat/test-cov.R
In nlmixr2/rxode2: Facilities for Simulating from ODE-Based Models
rxTest({
test_that("sync covariates", {
dat <- structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L),
TIME = c(0, 0, 0, 0.5, 0.5, 1, 1,
1.5, 1.5, 2, 2, 2.5, 2.5, 3, 3, 3.5, 3.5, 4, 4, 4.5, 4.5, 5,
5, 5.5, 5.5, 6, 6, 0, 0, 0, 0.5, 0.5, 1, 1, 1.5, 1.5, 2, 2, 2.5,
2.5, 3, 3, 3.5, 3.5, 4, 4, 4.5, 4.5, 5, 5, 5.5, 5.5, 6, 6, 0,
0, 0, 0.5, 0.5, 1, 1, 1.5, 1.5, 2, 2, 2.5, 2.5, 3, 3, 3.5, 3.5,
4, 4, 4.5, 4.5, 5, 5, 5.5, 5.5, 6, 6),
EVID = c(80101L, 60101L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 80101L, 60101L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 80101L, 60101L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L),
AMT = c(10, 10, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 10, 10, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10, 10, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA),
II = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0),
DV = c(NA, NA, 0, 3.8, 0.9, 5.8,
2.5, 3.2, 3.2, 1.8, 2.9, 1, 2.2, 0.5, 1.6, 0.3, 1.1, 0.2, 0.8,
0.1, 0.5, 0, 0.3, 0, 0.2, 0, 0.1, NA, NA, 0, 3.4, 0.7, 7.3, 2.4,
2.8, 2.5, 1.3, 2.7, 1, 1.6, 0.6, 1.1, 0.3, 0.8, 0.2, 0.8, 0.1,
0.5, 0, 0.3, 0, 0.2, 0, 0.2, NA, NA, 0, 2.8, 1.1, 6.7, 2.7, 3.9,
3.6, 1.5, 3.1, 0.7, 2.4, 0.5, 1.5, 0.3, 1.2, 0.1, 0.5, 0.1, 0.6,
0.1, 0.4, 0, 0.2, 0, 0.2),
CMT = c(1L, 1L, 4L, 3L, 4L, 3L, 4L,
3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L,
3L, 4L, 3L, 4L, 1L, 1L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L,
4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 1L,
1L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L,
3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L),
nlmixrRowNums =
c(1,
NA, 14, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22,
10, 23, 11, 24, 12, 25, 13, 26, 27, NA, 40, 28, 41, 29, 42, 30,
43, 31, 44, 32, 45, 33, 46, 34, 47, 35, 48, 36, 49, 37, 50, 38,
51, 39, 52, 53, NA, 66, 54, 67, 55, 68, 56, 69, 57, 70, 58, 71,
59, 72, 60, 73, 61, 74, 62, 75, 63, 76, 64, 77, 65, 78)),
class = "data.frame", row.names = c(NA, -81L))
par <- structure(c(0, 0, 0, 0.693147180559945, 0.693147180559945, 0.693147180559945,
2.30258509299405, 2.30258509299405, 2.30258509299405, 2.30258509299405,
2.30258509299405, 2.30258509299405, 0, 0, 0, 1, 1, 1, 1, 1, 1,
-1.86782087447842, 0, 0, -2.44813462994314, 0, 0, 0.0831313226164289,
0, 0, -2.28843604434224, 0, 0, 1.97816823869187e-05, 0, 0),
dim = c(3L, 12L),
dimnames = list(NULL,
c("THETA[1]", "THETA[2]", "THETA[3]",
"THETA[4]", "THETA[5]", "THETA[6]", "THETA[7]", "ETA[1]", "ETA[2]",
"ETA[3]", "ETA[4]", "ETA[5]")))
rx <- rxode2({
param(THETA[1], THETA[2], THETA[3], THETA[4], THETA[5], THETA[6],
THETA[7], ETA[1], ETA[2], ETA[3], ETA[4], ETA[5])
cmt(center)
cmt(meta)
rx_expr_5 ~ ETA[3] + THETA[1]
rx_expr_6 ~ ETA[1] + THETA[2]
rx_expr_7 ~ ETA[2] + THETA[4]
rx_expr_10 ~ exp(rx_expr_5)
d/dt(center) = -rx_expr_10 * center - exp(rx_expr_6 - (rx_expr_7)) *
center
rx_expr_8 ~ ETA[5] + THETA[5]
rx_expr_11 ~ exp(rx_expr_8)
dur(center) = rx_expr_11
rx_expr_9 ~ ETA[4] + THETA[3]
d/dt(meta) = rx_expr_10 * center - exp(rx_expr_9 - (rx_expr_7)) *
meta
center(0) = 0
meta(0) = 0
rx_expr_0 ~ CMT == 4
rx_expr_1 ~ CMT == 3
rx_expr_2 ~ 1 - (rx_expr_0)
rx_yj_ ~ 2 * (rx_expr_2) * (rx_expr_1) + 2 * (rx_expr_0)
rx_expr_3 ~ (rx_expr_0)
rx_expr_4 ~ (rx_expr_2)
rx_expr_12 ~ rx_expr_4 * (rx_expr_1)
rx_lambda_ ~ rx_expr_12 + rx_expr_3
rx_hi_ ~ rx_expr_12 + rx_expr_3
rx_low_ ~ 0
rx_expr_13 ~ exp(-(rx_expr_7))
rx_expr_14 ~ rx_expr_13 * meta
rx_expr_15 ~ rx_expr_13 * center
rx_expr_17 ~ rx_expr_14 * (rx_expr_0)
rx_expr_18 ~ rx_expr_15 * (rx_expr_2)
rx_expr_19 ~ rx_expr_18 * (rx_expr_1)
rx_pred_ = (rx_expr_0) * (rx_expr_17 + rx_expr_19) + rx_expr_18 *
Rx_pow_di((rx_expr_1), 2)
rx_r_ = (rx_expr_0) * Rx_pow_di(((rx_expr_17 + rx_expr_19) *
THETA[7]), 2) + (rx_expr_2) * Rx_pow_di((rx_expr_15 *
THETA[6] * (rx_expr_1)), 2) * (rx_expr_1)
tkm = THETA[1]
tcl = THETA[2]
tclm = THETA[3]
tv = THETA[4]
tdur0 = THETA[5]
prop.err = THETA[6]
prop.err2 = THETA[7]
eta.cl = ETA[1]
eta.v = ETA[2]
eta.km = ETA[3]
eta.clm = ETA[4]
eta.dur0 = ETA[5]
km = rx_expr_10
cl = exp(rx_expr_6)
clm = exp(rx_expr_9)
v = exp(rx_expr_7)
dur0 = rx_expr_11
cp = rx_expr_15
cm = rx_expr_14
tad = tad()
dosenum = dosenum()
cmt(cp)
cmt(cm)
dvid(3, 4)
})
s <- rxSolve(rx, dat, par, returnType="data.frame",
keep=c("nlmixrRowNums", "DV"), subsetNonmem=TRUE, addCov=TRUE)
expect_equal(sort(unique(s[s$time==0.5,"CMT"])), 3:4)
})
skip_if_not_installed("units")
for (meth in c("liblsoda", "lsoda")) { ## Dop is very close but doesn't match precisely.
# context(sprintf("Simple test for time-varying covariates (%s)", meth))
ode <- rxode2({
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeLin <- rxode2({
linear(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeLin <- rxode2({
linear(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeLocf <- rxode2({
locf(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeNocb <- rxode2({
nocb(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeMidpoint <- rxode2({
midpoint(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
et <- eventTable(time.units = "hr") # default time units
et$add.sampling(seq(from = 0, to = 10, by = 0.5))
cov <- data.frame(c = et$get.EventTable()$time + units::set_units(1, h))
et0 <- et
et <- cbind(et, cov)
cov.lin <- approxfun(et$time, et$c, yleft = et$c[1], yright = et$c[length(cov$c)])
t <- tempfile("temp", fileext = ".csv")
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
covsInterpolation = "linear",
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("time varying covariates output covariate in data frame", {
expect_equal(cov$c, out$c)
})
test_that("Linear Approximation matches approxfun.", {
expect_equal(lin.interp$c, lin.interp$c2)
})
t <- tempfile("temp", fileext = ".csv")
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(odeLin,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
covsInterpolation = "nocb",
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("Linear Approximation matches approxfun and overrides covsInterpolation", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## NONMEM interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "nocb", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant", f = 1
)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("NOCB Approximation similar to approxfun.", {
expect_equal(lin.interp$c, lin.interp$c2)
})
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(odeNocb,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "locf", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant", f = 1
)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("NOCB Approximation similar to approxfun and overrides locf when in ode", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## midpoint interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "midpoint", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant", f = 0.5
)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("midpoint Approximation similar to approxfun.", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## midpoint interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(odeMidpoint,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "locf", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant", f = 0.5
)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("midpoint Approximation similar to approxfun and overrides covsInterpolation method", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## covs_interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "locf", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant")
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("Constant Approximation similar to approxfun.", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## covs_interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(odeLocf,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "nocb", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant")
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("Constant Approximation similar to approxfun and overrides covsInterpolation", {
expect_equal(lin.interp$c, lin.interp$c2)
})
out <- as.data.frame(out)
out <- out[, names(out) != "c"]
suppressWarnings(.rxWithSink(t, {
out1 <-
rxSolve(ode,
params = c(a = -8 / 3, b = -10, c = 0),
events = et,
inits = c(X = 1, Y = 1, Z = 1), addCov = TRUE,
method = meth
)
}))
unlink(t)
out1 <- as.data.frame(out1)
test_that("time varying covariates produce different outputs", {
expect_false(isTRUE(all.equal(out, out1)))
})
cov <- data.frame(
c = et0$get.EventTable()$time + units::set_units(1, hr),
a = -et0$get.EventTable()$time / units::set_units(100, hr)
)
et <- cbind(et0, cov)
suppressWarnings(.rxWithSink(t, {
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
method = meth
)
out3 <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
method = meth
)
}))
unlink(t)
test_that("time varying covariates output covariate in data frame", {
expect_equal(cov$c[et0$get.obs.rec()], out$c)
expect_equal(cov$a[et0$get.obs.rec()], out$a)
})
cov <- data.frame(c = et0$get.EventTable()$time + units::set_units(1, hr))
et <- cbind(et0, cov)
suppressWarnings(.rxWithSink(t, {
out2 <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
method = meth
)
}))
unlink(t)
test_that("Before assinging the time varying to -8/3, out and out2 should be different", {
expect_false(isTRUE(all.equal(out, out2)))
})
# context(sprintf("Test First Assignment (%s)", meth))
## Assign a time-varying to a simple parameter
suppressWarnings(.rxWithSink(t, {
out$a <- -8 / 3
}))
unlink(t)
test_that("The out$a=-8/3 works.", {
expect_equal(as.data.frame(out), as.data.frame(out2))
})
# context(sprintf("Test Second Assignment (%s)", meth))
suppressWarnings(.rxWithSink(t, {
out$a <- out3$a
}))
unlink(t)
test_that("the out$a = time varying covariate works.", {
expect_equal(as.data.frame(out), as.data.frame(out3))
})
# context(sprintf("Covariate solve with data frame event table (%s)", meth))
## Covariate solve for data frame
d3 <- data.frame(
TIME = c(0, 0, 2.99270072992701, 192, 336, 456),
AMT = c(137L, 0L, -137L, 0L, 0L, 0L),
V2I = c(909L, 909L, 909L, 909L, 909L, 909L),
V1I = c(545L, 545L, 545L, 545L, 545L, 545L),
CLI = c(471L, 471L, 471L, 471L, 471L, 471L),
EVID = c(10101L, 0L, 10101L, 0L, 0L, 0L)
)
mod1 <- rxode2({
d/dt(A_centr) <- -A_centr * (CLI / V1I + 204 / V1I) + 204 * A_periph / V2I
d/dt(A_periph) <- 204 * A_centr / V1I - 204 * A_periph / V2I
d/dt(A_circ) <- -4 * A_circ * exp(-ETA[2] - THETA[2]) + 4 * A_tr3 * exp(-ETA[2] - THETA[2])
A_circ(0) <- exp(ETA[1] + THETA[1])
d/dt(A_prol) <- 4 * A_prol * Rx_pow(exp(ETA[1] + THETA[1]) / A_circ, exp(THETA[4])) * (-A_centr * exp(ETA[3] + THETA[3]) / V1I + 1) * exp(-ETA[2] - THETA[2]) - 4 * A_prol * exp(-ETA[2] - THETA[2])
A_prol(0) <- exp(ETA[1] + THETA[1])
d/dt(A_tr1) <- 4 * A_prol * exp(-ETA[2] - THETA[2]) - 4 * A_tr1 * exp(-ETA[2] - THETA[2])
A_tr1(0) <- exp(ETA[1] + THETA[1])
d/dt(A_tr2) <- 4 * A_tr1 * exp(-ETA[2] - THETA[2]) - 4 * A_tr2 * exp(-ETA[2] - THETA[2])
A_tr2(0) <- exp(ETA[1] + THETA[1])
d/dt(A_tr3) <- 4 * A_tr2 * exp(-ETA[2] - THETA[2]) - 4 * A_tr3 * exp(-ETA[2] - THETA[2])
A_tr3(0) <- exp(ETA[1] + THETA[1])
})
tmp <-
rxSolve(
mod1, d3,
setNames(
c(2.02103, 4.839305, 3.518676, -1.391113, 0.108127023, -0.064170725, 0.087765769),
c(sprintf("THETA[%d]", 1:4), sprintf("ETA[%d]", 1:3))
),
addCov = TRUE,
method = meth
)
test_that("Data Frame single subject solve", {
expect_equal(
tmp %>%
dplyr::select(CLI, V1I, V2I) %>% as.data.frame(),
d3 %>% dplyr::filter(EVID == 0) %>%
dplyr::select(CLI, V1I, V2I) %>% as.data.frame()
)
expect_equal(names(tmp$params), mod1$params[-(1:3)])
})
d3 <- data.frame(
ID = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
TIME = c(0, 0, 2.99270072992701, 192, 336, 456, 0, 0, 3.07272727272727, 432),
AMT = c(137L, 0L, -137L, 0L, 0L, 0L, 110L, 0L, -110L, 0L),
V2I = c(909L, 909L, 909L, 909L, 909L, 909L, 942L, 942L, 942L, 942L),
V1I = c(545L, 545L, 545L, 545L, 545L, 545L, 306L, 306L, 306L, 306L),
CLI = c(471L, 471L, 471L, 471L, 471L, 471L, 405L, 405L, 405L, 405L),
EVID = c(10101L, 0L, 10101L, 0L, 0L, 0L, 10101L, 0L, 10101L, 0L)
)
par2 <-
matrix(
c(
2.02103, 4.839305, 3.518676, -1.391113, 0.108127023, -0.064170725, 0.087765769,
2.02103, 4.839305, 3.518676, -1.391113, -0.064170725, 0.087765769, 0.108127023
),
nrow = 2, byrow = T,
dimnames = list(NULL, c(sprintf("THETA[%d]", 1:4), sprintf("ETA[%d]", 1:3)))
)
tmp <- rxSolve(mod1, d3, par2, addCov = TRUE, cores = 2, method = meth)
test_that("Data Frame multi subject solve", {
expect_equal(
tmp %>%
dplyr::select(CLI, V1I, V2I) %>% as.data.frame(),
d3 %>%
dplyr::filter(EVID == 0) %>%
dplyr::select(CLI, V1I, V2I) %>% as.data.frame()
)
expect_equal(names(tmp$params)[-1], mod1$params[-(1:3)])
})
## Now check missing covariate values.
d3na <-
data.frame(
ID = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
TIME = c(0, 0, 2.99270072992701, 192, 336, 456, 0, 0, 3.07272727272727, 432),
AMT = c(137L, 0L, -137L, 0L, 0L, 0L, 110L, 0L, -110L, 0L),
V2I = c(909L, NA_integer_, 909L, 909L, 909L, 909L, 942L, 942L, 942L, 942L),
V1I = c(545L, 545L, 545L, 545L, 545L, 545L, 306L, 306L, 306L, NA_integer_),
CLI = c(471L, 471L, 471L, 471L, NA_integer_, 471L, 405L, 405L, 405L, 405L),
EVID = c(10101L, 0L, 10101L, 0L, 0L, 0L, 10101L, 0L, 10101L, 0L)
)
expect_warning(
tmp <- rxSolve(mod1, d3na, par2, addCov = TRUE, cores = 2, method = meth),
NA)
tmp2 <- rxSolve(mod1, d3, par2, addCov = TRUE, cores = 2, method = meth)
# context(sprintf("Test NA extrapolation for %s solving", meth))
test_that("NA solve is the same", {
for (i in c("id", "time", "A_centr", "A_periph", "A_circ", "A_prol", "A_tr1", "A_tr2", "A_tr3")) {
expect_equal(tmp[[i]], tmp2[[i]])
}
})
d3na <- data.frame(
ID = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
TIME = c(0, 0, 2.99270072992701, 192, 336, 456, 0, 0, 3.07272727272727, 432),
AMT = c(137L, 0L, -137L, 0L, 0L, 0L, 110L, 0L, -110L, 0L),
V2I = c(909L, NA_integer_, 909L, 909L, 909L, 909L, 942L, 942L, 942L, 942L),
V1I = c(545L, 545L, 545L, 545L, 545L, 545L, NA_integer_, NA_integer_, NA_integer_, NA_integer_),
CLI = c(471L, 471L, 471L, 471L, NA_integer_, 471L, 405L, 405L, 405L, 405L),
EVID = c(10101L, 0L, 10101L, 0L, 0L, 0L, 10101L, 0L, 10101L, 0L)
)
test_that("All covariates are NA give a warning", {
expect_warning(expect_warning(
rxSolve(mod1, d3na, par2, addCov = TRUE, cores = 2, method = meth),
"column 'V1I' has only 'NA' values for id '2'"))
})
}
# time-varying covariates work with ODEs
test_that("time varying covariates lhs", {
dfadvan <- data.frame(
ID = c(
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
),
TIME = c(
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L
), AMT = c(
100L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 100L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 100L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 100L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L
), MDV = c(
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), CLCR = c(
120L, 120L,
120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L,
120L, 120L, 120L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 120L, 120L, 120L, 120L, 120L, 120L,
120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L,
120L, 120L, 120L, 120L
)
)
mod <- rxode2({
CLpop <- 2 # clearance
Vpop <- 10 # central volume of distribution
CL <- CLpop * (CLCR / 100)
V <- Vpop
})
mod2 <- rxode2({
CLpop <- 2 # clearance
Vpop <- 10 # central volume of distribution
CL <- CLpop * (CLCR / 100)
V <- Vpop
d / dt(matt) <- 0
})
x1 <- rxSolve(mod, dfadvan, keep = "CL")
x2 <- rxSolve(mod2, dfadvan, keep = "CL")
expect_equal(x1$CL, x2$CL)
})
})
nlmixr2/rxode2 documentation built on Jan. 11, 2025, 8:48 a.m.
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rxTest({
test_that("sync covariates", {
dat <- structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L),
TIME = c(0, 0, 0, 0.5, 0.5, 1, 1,
1.5, 1.5, 2, 2, 2.5, 2.5, 3, 3, 3.5, 3.5, 4, 4, 4.5, 4.5, 5,
5, 5.5, 5.5, 6, 6, 0, 0, 0, 0.5, 0.5, 1, 1, 1.5, 1.5, 2, 2, 2.5,
2.5, 3, 3, 3.5, 3.5, 4, 4, 4.5, 4.5, 5, 5, 5.5, 5.5, 6, 6, 0,
0, 0, 0.5, 0.5, 1, 1, 1.5, 1.5, 2, 2, 2.5, 2.5, 3, 3, 3.5, 3.5,
4, 4, 4.5, 4.5, 5, 5, 5.5, 5.5, 6, 6),
EVID = c(80101L, 60101L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 80101L, 60101L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 80101L, 60101L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L),
AMT = c(10, 10, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 10, 10, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10, 10, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA),
II = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0),
DV = c(NA, NA, 0, 3.8, 0.9, 5.8,
2.5, 3.2, 3.2, 1.8, 2.9, 1, 2.2, 0.5, 1.6, 0.3, 1.1, 0.2, 0.8,
0.1, 0.5, 0, 0.3, 0, 0.2, 0, 0.1, NA, NA, 0, 3.4, 0.7, 7.3, 2.4,
2.8, 2.5, 1.3, 2.7, 1, 1.6, 0.6, 1.1, 0.3, 0.8, 0.2, 0.8, 0.1,
0.5, 0, 0.3, 0, 0.2, 0, 0.2, NA, NA, 0, 2.8, 1.1, 6.7, 2.7, 3.9,
3.6, 1.5, 3.1, 0.7, 2.4, 0.5, 1.5, 0.3, 1.2, 0.1, 0.5, 0.1, 0.6,
0.1, 0.4, 0, 0.2, 0, 0.2),
CMT = c(1L, 1L, 4L, 3L, 4L, 3L, 4L,
3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L,
3L, 4L, 3L, 4L, 1L, 1L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L,
4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 1L,
1L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L,
3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L),
nlmixrRowNums =
c(1,
NA, 14, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22,
10, 23, 11, 24, 12, 25, 13, 26, 27, NA, 40, 28, 41, 29, 42, 30,
43, 31, 44, 32, 45, 33, 46, 34, 47, 35, 48, 36, 49, 37, 50, 38,
51, 39, 52, 53, NA, 66, 54, 67, 55, 68, 56, 69, 57, 70, 58, 71,
59, 72, 60, 73, 61, 74, 62, 75, 63, 76, 64, 77, 65, 78)),
class = "data.frame", row.names = c(NA, -81L))
par <- structure(c(0, 0, 0, 0.693147180559945, 0.693147180559945, 0.693147180559945,
2.30258509299405, 2.30258509299405, 2.30258509299405, 2.30258509299405,
2.30258509299405, 2.30258509299405, 0, 0, 0, 1, 1, 1, 1, 1, 1,
-1.86782087447842, 0, 0, -2.44813462994314, 0, 0, 0.0831313226164289,
0, 0, -2.28843604434224, 0, 0, 1.97816823869187e-05, 0, 0),
dim = c(3L, 12L),
dimnames = list(NULL,
c("THETA[1]", "THETA[2]", "THETA[3]",
"THETA[4]", "THETA[5]", "THETA[6]", "THETA[7]", "ETA[1]", "ETA[2]",
"ETA[3]", "ETA[4]", "ETA[5]")))
rx <- rxode2({
param(THETA[1], THETA[2], THETA[3], THETA[4], THETA[5], THETA[6],
THETA[7], ETA[1], ETA[2], ETA[3], ETA[4], ETA[5])
cmt(center)
cmt(meta)
rx_expr_5 ~ ETA[3] + THETA[1]
rx_expr_6 ~ ETA[1] + THETA[2]
rx_expr_7 ~ ETA[2] + THETA[4]
rx_expr_10 ~ exp(rx_expr_5)
d/dt(center) = -rx_expr_10 * center - exp(rx_expr_6 - (rx_expr_7)) *
center
rx_expr_8 ~ ETA[5] + THETA[5]
rx_expr_11 ~ exp(rx_expr_8)
dur(center) = rx_expr_11
rx_expr_9 ~ ETA[4] + THETA[3]
d/dt(meta) = rx_expr_10 * center - exp(rx_expr_9 - (rx_expr_7)) *
meta
center(0) = 0
meta(0) = 0
rx_expr_0 ~ CMT == 4
rx_expr_1 ~ CMT == 3
rx_expr_2 ~ 1 - (rx_expr_0)
rx_yj_ ~ 2 * (rx_expr_2) * (rx_expr_1) + 2 * (rx_expr_0)
rx_expr_3 ~ (rx_expr_0)
rx_expr_4 ~ (rx_expr_2)
rx_expr_12 ~ rx_expr_4 * (rx_expr_1)
rx_lambda_ ~ rx_expr_12 + rx_expr_3
rx_hi_ ~ rx_expr_12 + rx_expr_3
rx_low_ ~ 0
rx_expr_13 ~ exp(-(rx_expr_7))
rx_expr_14 ~ rx_expr_13 * meta
rx_expr_15 ~ rx_expr_13 * center
rx_expr_17 ~ rx_expr_14 * (rx_expr_0)
rx_expr_18 ~ rx_expr_15 * (rx_expr_2)
rx_expr_19 ~ rx_expr_18 * (rx_expr_1)
rx_pred_ = (rx_expr_0) * (rx_expr_17 + rx_expr_19) + rx_expr_18 *
Rx_pow_di((rx_expr_1), 2)
rx_r_ = (rx_expr_0) * Rx_pow_di(((rx_expr_17 + rx_expr_19) *
THETA[7]), 2) + (rx_expr_2) * Rx_pow_di((rx_expr_15 *
THETA[6] * (rx_expr_1)), 2) * (rx_expr_1)
tkm = THETA[1]
tcl = THETA[2]
tclm = THETA[3]
tv = THETA[4]
tdur0 = THETA[5]
prop.err = THETA[6]
prop.err2 = THETA[7]
eta.cl = ETA[1]
eta.v = ETA[2]
eta.km = ETA[3]
eta.clm = ETA[4]
eta.dur0 = ETA[5]
km = rx_expr_10
cl = exp(rx_expr_6)
clm = exp(rx_expr_9)
v = exp(rx_expr_7)
dur0 = rx_expr_11
cp = rx_expr_15
cm = rx_expr_14
tad = tad()
dosenum = dosenum()
cmt(cp)
cmt(cm)
dvid(3, 4)
})
s <- rxSolve(rx, dat, par, returnType="data.frame",
keep=c("nlmixrRowNums", "DV"), subsetNonmem=TRUE, addCov=TRUE)
expect_equal(sort(unique(s[s$time==0.5,"CMT"])), 3:4)
})
skip_if_not_installed("units")
for (meth in c("liblsoda", "lsoda")) { ## Dop is very close but doesn't match precisely.
# context(sprintf("Simple test for time-varying covariates (%s)", meth))
ode <- rxode2({
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeLin <- rxode2({
linear(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeLin <- rxode2({
linear(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeLocf <- rxode2({
locf(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeNocb <- rxode2({
nocb(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
odeMidpoint <- rxode2({
midpoint(c)
b <- -1
d/dt(X) <- a * X + Y * Z
d/dt(Y) <- b * (Y - Z)
d/dt(Z) <- -X * Y + c * Y - Z
printf("%.10f,%.10f\n", t, c)
})
et <- eventTable(time.units = "hr") # default time units
et$add.sampling(seq(from = 0, to = 10, by = 0.5))
cov <- data.frame(c = et$get.EventTable()$time + units::set_units(1, h))
et0 <- et
et <- cbind(et, cov)
cov.lin <- approxfun(et$time, et$c, yleft = et$c[1], yright = et$c[length(cov$c)])
t <- tempfile("temp", fileext = ".csv")
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
covsInterpolation = "linear",
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("time varying covariates output covariate in data frame", {
expect_equal(cov$c, out$c)
})
test_that("Linear Approximation matches approxfun.", {
expect_equal(lin.interp$c, lin.interp$c2)
})
t <- tempfile("temp", fileext = ".csv")
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(odeLin,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
covsInterpolation = "nocb",
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("Linear Approximation matches approxfun and overrides covsInterpolation", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## NONMEM interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "nocb", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant", f = 1
)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("NOCB Approximation similar to approxfun.", {
expect_equal(lin.interp$c, lin.interp$c2)
})
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(odeNocb,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "locf", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant", f = 1
)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("NOCB Approximation similar to approxfun and overrides locf when in ode", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## midpoint interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "midpoint", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant", f = 0.5
)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("midpoint Approximation similar to approxfun.", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## midpoint interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(odeMidpoint,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "locf", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant", f = 0.5
)
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("midpoint Approximation similar to approxfun and overrides covsInterpolation method", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## covs_interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "locf", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant")
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("Constant Approximation similar to approxfun.", {
expect_equal(lin.interp$c, lin.interp$c2)
})
## covs_interpolation
suppressWarnings(.rxWithSink(t, {
cat("t,c\n")
out <- rxSolve(odeLocf,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
covsInterpolation = "nocb", addCov = TRUE,
method = meth
)
}))
lin.interp <- read.csv(t)
unlink(t)
cov.lin <- approxfun(out$time, out$c,
yleft = cov$c[1], yright = cov$c[length(cov$c)],
method = "constant")
lin.interp$c2 <- cov.lin(lin.interp$t)
test_that("Constant Approximation similar to approxfun and overrides covsInterpolation", {
expect_equal(lin.interp$c, lin.interp$c2)
})
out <- as.data.frame(out)
out <- out[, names(out) != "c"]
suppressWarnings(.rxWithSink(t, {
out1 <-
rxSolve(ode,
params = c(a = -8 / 3, b = -10, c = 0),
events = et,
inits = c(X = 1, Y = 1, Z = 1), addCov = TRUE,
method = meth
)
}))
unlink(t)
out1 <- as.data.frame(out1)
test_that("time varying covariates produce different outputs", {
expect_false(isTRUE(all.equal(out, out1)))
})
cov <- data.frame(
c = et0$get.EventTable()$time + units::set_units(1, hr),
a = -et0$get.EventTable()$time / units::set_units(100, hr)
)
et <- cbind(et0, cov)
suppressWarnings(.rxWithSink(t, {
out <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
method = meth
)
out3 <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
method = meth
)
}))
unlink(t)
test_that("time varying covariates output covariate in data frame", {
expect_equal(cov$c[et0$get.obs.rec()], out$c)
expect_equal(cov$a[et0$get.obs.rec()], out$a)
})
cov <- data.frame(c = et0$get.EventTable()$time + units::set_units(1, hr))
et <- cbind(et0, cov)
suppressWarnings(.rxWithSink(t, {
out2 <- rxSolve(ode,
params = c(a = -8 / 3, b = -10),
events = et,
inits = c(X = 1, Y = 1, Z = 1),
addCov = TRUE,
method = meth
)
}))
unlink(t)
test_that("Before assinging the time varying to -8/3, out and out2 should be different", {
expect_false(isTRUE(all.equal(out, out2)))
})
# context(sprintf("Test First Assignment (%s)", meth))
## Assign a time-varying to a simple parameter
suppressWarnings(.rxWithSink(t, {
out$a <- -8 / 3
}))
unlink(t)
test_that("The out$a=-8/3 works.", {
expect_equal(as.data.frame(out), as.data.frame(out2))
})
# context(sprintf("Test Second Assignment (%s)", meth))
suppressWarnings(.rxWithSink(t, {
out$a <- out3$a
}))
unlink(t)
test_that("the out$a = time varying covariate works.", {
expect_equal(as.data.frame(out), as.data.frame(out3))
})
# context(sprintf("Covariate solve with data frame event table (%s)", meth))
## Covariate solve for data frame
d3 <- data.frame(
TIME = c(0, 0, 2.99270072992701, 192, 336, 456),
AMT = c(137L, 0L, -137L, 0L, 0L, 0L),
V2I = c(909L, 909L, 909L, 909L, 909L, 909L),
V1I = c(545L, 545L, 545L, 545L, 545L, 545L),
CLI = c(471L, 471L, 471L, 471L, 471L, 471L),
EVID = c(10101L, 0L, 10101L, 0L, 0L, 0L)
)
mod1 <- rxode2({
d/dt(A_centr) <- -A_centr * (CLI / V1I + 204 / V1I) + 204 * A_periph / V2I
d/dt(A_periph) <- 204 * A_centr / V1I - 204 * A_periph / V2I
d/dt(A_circ) <- -4 * A_circ * exp(-ETA[2] - THETA[2]) + 4 * A_tr3 * exp(-ETA[2] - THETA[2])
A_circ(0) <- exp(ETA[1] + THETA[1])
d/dt(A_prol) <- 4 * A_prol * Rx_pow(exp(ETA[1] + THETA[1]) / A_circ, exp(THETA[4])) * (-A_centr * exp(ETA[3] + THETA[3]) / V1I + 1) * exp(-ETA[2] - THETA[2]) - 4 * A_prol * exp(-ETA[2] - THETA[2])
A_prol(0) <- exp(ETA[1] + THETA[1])
d/dt(A_tr1) <- 4 * A_prol * exp(-ETA[2] - THETA[2]) - 4 * A_tr1 * exp(-ETA[2] - THETA[2])
A_tr1(0) <- exp(ETA[1] + THETA[1])
d/dt(A_tr2) <- 4 * A_tr1 * exp(-ETA[2] - THETA[2]) - 4 * A_tr2 * exp(-ETA[2] - THETA[2])
A_tr2(0) <- exp(ETA[1] + THETA[1])
d/dt(A_tr3) <- 4 * A_tr2 * exp(-ETA[2] - THETA[2]) - 4 * A_tr3 * exp(-ETA[2] - THETA[2])
A_tr3(0) <- exp(ETA[1] + THETA[1])
})
tmp <-
rxSolve(
mod1, d3,
setNames(
c(2.02103, 4.839305, 3.518676, -1.391113, 0.108127023, -0.064170725, 0.087765769),
c(sprintf("THETA[%d]", 1:4), sprintf("ETA[%d]", 1:3))
),
addCov = TRUE,
method = meth
)
test_that("Data Frame single subject solve", {
expect_equal(
tmp %>%
dplyr::select(CLI, V1I, V2I) %>% as.data.frame(),
d3 %>% dplyr::filter(EVID == 0) %>%
dplyr::select(CLI, V1I, V2I) %>% as.data.frame()
)
expect_equal(names(tmp$params), mod1$params[-(1:3)])
})
d3 <- data.frame(
ID = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
TIME = c(0, 0, 2.99270072992701, 192, 336, 456, 0, 0, 3.07272727272727, 432),
AMT = c(137L, 0L, -137L, 0L, 0L, 0L, 110L, 0L, -110L, 0L),
V2I = c(909L, 909L, 909L, 909L, 909L, 909L, 942L, 942L, 942L, 942L),
V1I = c(545L, 545L, 545L, 545L, 545L, 545L, 306L, 306L, 306L, 306L),
CLI = c(471L, 471L, 471L, 471L, 471L, 471L, 405L, 405L, 405L, 405L),
EVID = c(10101L, 0L, 10101L, 0L, 0L, 0L, 10101L, 0L, 10101L, 0L)
)
par2 <-
matrix(
c(
2.02103, 4.839305, 3.518676, -1.391113, 0.108127023, -0.064170725, 0.087765769,
2.02103, 4.839305, 3.518676, -1.391113, -0.064170725, 0.087765769, 0.108127023
),
nrow = 2, byrow = T,
dimnames = list(NULL, c(sprintf("THETA[%d]", 1:4), sprintf("ETA[%d]", 1:3)))
)
tmp <- rxSolve(mod1, d3, par2, addCov = TRUE, cores = 2, method = meth)
test_that("Data Frame multi subject solve", {
expect_equal(
tmp %>%
dplyr::select(CLI, V1I, V2I) %>% as.data.frame(),
d3 %>%
dplyr::filter(EVID == 0) %>%
dplyr::select(CLI, V1I, V2I) %>% as.data.frame()
)
expect_equal(names(tmp$params)[-1], mod1$params[-(1:3)])
})
## Now check missing covariate values.
d3na <-
data.frame(
ID = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
TIME = c(0, 0, 2.99270072992701, 192, 336, 456, 0, 0, 3.07272727272727, 432),
AMT = c(137L, 0L, -137L, 0L, 0L, 0L, 110L, 0L, -110L, 0L),
V2I = c(909L, NA_integer_, 909L, 909L, 909L, 909L, 942L, 942L, 942L, 942L),
V1I = c(545L, 545L, 545L, 545L, 545L, 545L, 306L, 306L, 306L, NA_integer_),
CLI = c(471L, 471L, 471L, 471L, NA_integer_, 471L, 405L, 405L, 405L, 405L),
EVID = c(10101L, 0L, 10101L, 0L, 0L, 0L, 10101L, 0L, 10101L, 0L)
)
expect_warning(
tmp <- rxSolve(mod1, d3na, par2, addCov = TRUE, cores = 2, method = meth),
NA)
tmp2 <- rxSolve(mod1, d3, par2, addCov = TRUE, cores = 2, method = meth)
# context(sprintf("Test NA extrapolation for %s solving", meth))
test_that("NA solve is the same", {
for (i in c("id", "time", "A_centr", "A_periph", "A_circ", "A_prol", "A_tr1", "A_tr2", "A_tr3")) {
expect_equal(tmp[[i]], tmp2[[i]])
}
})
d3na <- data.frame(
ID = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
TIME = c(0, 0, 2.99270072992701, 192, 336, 456, 0, 0, 3.07272727272727, 432),
AMT = c(137L, 0L, -137L, 0L, 0L, 0L, 110L, 0L, -110L, 0L),
V2I = c(909L, NA_integer_, 909L, 909L, 909L, 909L, 942L, 942L, 942L, 942L),
V1I = c(545L, 545L, 545L, 545L, 545L, 545L, NA_integer_, NA_integer_, NA_integer_, NA_integer_),
CLI = c(471L, 471L, 471L, 471L, NA_integer_, 471L, 405L, 405L, 405L, 405L),
EVID = c(10101L, 0L, 10101L, 0L, 0L, 0L, 10101L, 0L, 10101L, 0L)
)
test_that("All covariates are NA give a warning", {
expect_warning(expect_warning(
rxSolve(mod1, d3na, par2, addCov = TRUE, cores = 2, method = meth),
"column 'V1I' has only 'NA' values for id '2'"))
})
}
# time-varying covariates work with ODEs
test_that("time varying covariates lhs", {
dfadvan <- data.frame(
ID = c(
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
),
TIME = c(
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L
), AMT = c(
100L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 100L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 100L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 100L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L
), MDV = c(
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), CLCR = c(
120L, 120L,
120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L,
120L, 120L, 120L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 120L, 120L, 120L, 120L, 120L, 120L,
120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L, 120L,
120L, 120L, 120L, 120L
)
)
mod <- rxode2({
CLpop <- 2 # clearance
Vpop <- 10 # central volume of distribution
CL <- CLpop * (CLCR / 100)
V <- Vpop
})
mod2 <- rxode2({
CLpop <- 2 # clearance
Vpop <- 10 # central volume of distribution
CL <- CLpop * (CLCR / 100)
V <- Vpop
d / dt(matt) <- 0
})
x1 <- rxSolve(mod, dfadvan, keep = "CL")
x2 <- rxSolve(mod2, dfadvan, keep = "CL")
expect_equal(x1$CL, x2$CL)
})
})
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