tests/testthat/test-focei-foce-stride.R

In nlmixr2est: Nonlinear Mixed Effects Models in Population PK/PD, Estimation Routines

test_that("FOCE reads all ETA columns with the allocated observation stride", {
  skip_on_cran()
  model <- function() {
    ini({ level <- 0.2; slope <- -0.1; error <- fix(0.2)
          etaLevel ~ 0.2; etaSlope ~ 0.05 })
    model({ prediction <- exp(level+etaLevel+(slope+etaSlope)*TIME)
            prediction ~ prop(error) })
  }
  data <- data.frame(
    ID = rep(1:2, each = 3),
    TIME = rep(1:3, 2),
    DV = c(1.5, 1.4, 1.3, 0.8, 0.85, 0.9),
    AMT = 0,
    EVID = 0
  )
  fit <- .nlmixr(
    model,
    data,
    "foce",
    control = foceiControl(
      interaction = FALSE,
      innerOpt = "n1qn1",
      epsilon = 1e-10,
      maxOuterIterations = 0L,
      maxInnerIterations = 1000L,
      print = 0,
      covMethod = "",
      calcTables = FALSE,
      compress = FALSE
    )
  )
  omega <- as.matrix(fit$omega)
  oi <- solve(omega)
  value <- 0
  for (id in 1:2) {
    eta <- as.numeric(fit$eta[id, -1])
    time <- 1:3
    f <- exp(0.2 + eta[1] + (-0.1 + eta[2]) * time)
    variance <- (0.2 * exp(0.2 - 0.1 * time))^2
    a <- cbind(f, time * f)
    hessian <- oi + crossprod(a, a / variance)
    value <- value +
      sum(log(variance) + (data$DV[data$ID == id] - f)^2 / variance) +
      drop(crossprod(eta, oi %*% eta)) +
      as.numeric(determinant(omega)$modulus) +
      as.numeric(determinant(hessian)$modulus)
  }
  expect_equal(fit$objf, value, tolerance = 1e-8)
})