tests/testthat/test-ui-saem.R

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

nlmixrTest(
  {
    context("UI SAEM tests")

    m1 <- function() {
      ini({ # Where initial conditions/variables are specified
        # '<-' or '=' defines population parameters
        lVM <- 7 # log Vmax (mg/hr)
        lKM <- 6 # log KM (mg/L)
        lVc <- 4 # log V (L)
        # Bounds may be specified by c(lower, est, upper), like NONMEM:
        # Residuals errors are assumed to be population parameters
        prop.err <- c(0, 0.2, 1)
        # Between subject variability estimates are specified by '~'
        # Semicolons are optional
        eta.KM ~ 0.3
        eta.Vc ~ 0.1
        eta.VM ~ 0.2
      })
      model({ # Where the model is specified
        # The model uses the ini-defined variable names
        Vc <- exp(lVc + eta.Vc)
        VM <- exp(lVM + eta.VM)
        KM <- exp(lKM + eta.KM)
        # RxODE-style differential equations are supported
        d / dt(centr) <- -(VM * centr / Vc) / (KM + centr / Vc)
        ## Concentration is calculated
        cp <- centr / Vc
        # And is assumed to follow proportional error estimated by prop.err
        cp ~ prop(prop.err)
      })
    }


    test_that("Initial estimate order is correct", {
      tmp <- nlmixr(m1)
      expect_equal(log(tmp$saem.init$theta), c(4, 7, 6))
      expect_equal(tmp$saem.init$omega, c(0.1, 0.2, 0.3))
    })

    m1 <- function() {
      ini({ # Where initial conditions/variables are specified
        # '<-' or '=' defines population parameters
        # Simple numeric expressions are supported
        lCl <- log(200) # log Cl/F (L/hr)
        lVc <- log(60) # log V/F (L)
        lKA <- log(1.5) # log KA (/hr)
        # Bounds may be specified by c(lower, est, upper), like NONMEM:
        # Residuals errors are assumed to be population parameters
        prop.err <- c(0, 0.2, 1)
        # Between subject variability estimates are specified by '~'
        # Semicolons are optional
        eta.Cl ~ 0.1
        eta.Vc ~ 0.1
        eta.KA ~ 0.1
        AllomCL <- 0.75
        AllomV <- 1
      })
      model({ # Where the model is specified
        # The model uses the ini-defined variable names
        Cl <- exp(lCl + eta.Cl + lWT70 * AllomCL)
        Vc <- exp(lVc + eta.Vc + lWT70 * AllomV)
        KA <- exp(lKA + eta.KA)
        # RxODE-style differential equations are supported
        d / dt(depot) <- -KA * depot
        d / dt(centr) <- KA * depot - (Cl / Vc) * centr
        ## Concentration is calculated
        cp <- centr / Vc
        # And is assumed to follow proportional error estimated by prop.err
        cp ~ prop(prop.err)
      })
    }

    test_that("Covariates make sense.", {
      m1 <- nlmixr(m1)
      expect_equal(c(200, 60, 1.5, 0.75, 1, NA), m1$saem.init.theta)
      expect_equal(c("lCl", "AllomCL", "lVc", "AllomV", "lKA"), m1$saem.theta.name)
    })
  },
  test = "saem"
)