tests/testthat/test-precondition.R

In nlmixr: Nonlinear Mixed Effects Models in Population PK/PD

nlmixrTest(
  {
    context("precondition tests")

    test_that("precondition tests", {
      one.compartment <- function() {
        ini({
          tka <- 0.45 # Log Ka
          tcl <- 1 # Log Cl
          tv <- 3.45 # Log V
          eta.ka ~ 0.6
          eta.cl ~ 0.3
          eta.v ~ 0.1
          add.sd <- 0.7
        })
        model({
          ka <- exp(tka + eta.ka)
          cl <- exp(tcl + eta.cl)
          v <- exp(tv + eta.v)
          d / dt(depot) <- -ka * depot
          d / dt(center) <- ka * depot - cl / v * center
          cp <- center / v
          cp ~ add(add.sd)
        })
      }

      fit2 <- suppressWarnings(nlmixr(one.compartment, theo_sd,
        est = "focei",
        control = list(print = 0)
      ))

      df1 <- fit2$parFixedDf
      cov1 <- fit2$cov

      ## Simply re-evaluate with no estimation (including inner estimation)
      suppressWarnings(preconditionFit(fit2, estType = "none"))

      df2 <- fit2$parFixedDf
      cov2 <- fit2$cov

      ## In this case there isn't a theta/omega estimate so these should be the same
      expect_equal(df1$Estimate, df2$Estimate)
      expect_equal(df1$`Back-transformed`, df2$`Back-transformed`)
      expect_equal(df1$`BSV(CV%)`, df2$`BSV(CV%)`)
      expect_equal(df1$`Shrink(SD)%`, df2$`Shrink(SD)%`)

      expect_false(isTRUE(all.equal(df1$SE, df2$SE)))
      expect_false(isTRUE(all.equal(df1$`%RSE`, df2$`%RSE`)))
      expect_false(isTRUE(all.equal(df1$`CI Lower`, df2$`CI Lower`)))
      expect_false(isTRUE(all.equal(df1$`%RSE`, df2$`%RSE`)))
      expect_false(isTRUE(all.equal(cov1, cov2)))

      setCov(fit2, "r,s")

      df3 <- fit2$parFixedDf
      cov3 <- fit2$cov

      expect_equal(df1$Estimate, df3$Estimate)
      expect_equal(df1$`Back-transformed`, df3$`Back-transformed`)
      expect_equal(df1$`BSV(CV%)`, df3$`BSV(CV%)`)
      expect_equal(df1$`Shrink(SD)%`, df3$`Shrink(SD)%`)

      expect_equal(df1$SE, df3$SE)
      expect_equal(df1$`%RSE`, df3$`%RSE`)
      expect_equal(df1$`CI Lower`, df3$`CI Lower`)
      expect_equal(df1$`%RSE`, df3$`%RSE`)
      expect_equal(cov1, cov3)

      setCov(fit2, "precondition")
      df4 <- fit2$parFixedDf
      cov4 <- fit2$cov


      expect_equal(df2$Estimate, df4$Estimate)
      expect_equal(df2$`Back-transformed`, df4$`Back-transformed`)
      expect_equal(df2$`BSV(CV%)`, df4$`BSV(CV%)`)
      expect_equal(df2$`Shrink(SD)%`, df4$`Shrink(SD)%`)

      expect_equal(df2$SE, df4$SE)
      expect_equal(df2$`%RSE`, df4$`%RSE`)
      expect_equal(df2$`CI Lower`, df4$`CI Lower`)
      expect_equal(df2$`%RSE`, df4$`%RSE`)
      expect_equal(cov2, cov4)
    })
  },
  test = "precondition"
)