tests/testthat/test-saem-cov-parsing.R

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

nlmixrTest(
  {
    context("SAEM covariate parsing")

    run1 <- function() {
      ini({
        tcl <- log(0.008) # typical value of clearance
        tv <- log(0.6) # typical value of volume
        all.cl <- 1 # allometric exponent on CL
        eta.cl ~ 1
        # interindividual variability on clearance and volume
        add.err <- 0.1 # residual variability
      })
      model({
        cl <- exp(tcl + all.cl * log_allo_wt + eta.cl) # individual value of clearance
        v <- exp(tv) # individual value of volume
        ke <- cl / v # elimination rate constant
        d / dt(A1) <- -ke * A1 # model differential equation
        cp <- A1 / v # concentration in plasma
        cp ~ add(add.err) # define error model
      })
    }

    run2 <- function() {
      ini({
        tcl <- log(0.008) # typical value of clearance
        tv <- log(0.6) # typical value of volume
        all.cl <- 1 # allometric exponent on CL
        eta.cl ~ 1
        # interindividual variability on clearance and volume
        add.err <- 0.1 # residual variability
      })
      model({
        cl <- exp(tcl + log_allo_wt * all.cl + eta.cl) # individual value of clearance
        v <- exp(tv) # individual value of volume
        ke <- cl / v # elimination rate constant
        d / dt(A1) <- -ke * A1 # model differential equation
        cp <- A1 / v # concentration in plasma
        cp ~ add(add.err) # define error model
      })
    }


    run3 <- function() {
      ini({
        tcl <- log(0.008) # typical value of clearance
        tv <- log(0.6) # typical value of volume
        all.cl <- 1 # allometric exponent on CL
        eta.cl ~ 1
        # interindividual variability on clearance and volume
        add.err <- 0.1 # residual variability
      })
      model({
        cl <- exp(tcl + eta.cl + log_allo_wt * all.cl) # individual value of clearance
        v <- exp(tv) # individual value of volume
        ke <- cl / v # elimination rate constant
        d / dt(A1) <- -ke * A1 # model differential equation
        cp <- A1 / v # concentration in plasma
        cp ~ add(add.err) # define error model
      })
    }


    run4 <- function() {
      ini({
        tcl <- log(0.008) # typical value of clearance
        tv <- log(0.6) # typical value of volume
        all.cl <- 1 # allometric exponent on CL
        eta.cl ~ 1
        # interindividual variability on clearance and volume
        add.err <- 0.1 # residual variability
      })
      model({
        cl <- exp(tcl + eta.cl + all.cl * log_allo_wt) # individual value of clearance
        v <- exp(tv) # individual value of volume
        ke <- cl / v # elimination rate constant
        d / dt(A1) <- -ke * A1 # model differential equation
        cp <- A1 / v # concentration in plasma
        cp ~ add(add.err) # define error model
      })
    }

    run5 <- function() {
      ini({
        tcl <- log(0.008) # typical value of clearance
        tv <- log(0.6) # typical value of volume
        all.cl <- 1 # allometric exponent on CL
        eta.cl ~ 1
        # interindividual variability on clearance and volume
        add.err <- 0.1 # residual variability
      })
      model({
        cl <- exp(all.cl * log_allo_wt + tcl + eta.cl) # individual value of clearance
        v <- exp(tv) # individual value of volume
        ke <- cl / v # elimination rate constant
        d / dt(A1) <- -ke * A1 # model differential equation
        cp <- A1 / v # concentration in plasma
        cp ~ add(add.err) # define error model
      })
    }

    run6 <- function() {
      ini({
        tcl <- log(0.008) # typical value of clearance
        tv <- log(0.6) # typical value of volume
        all.cl <- 1 # allometric exponent on CL
        eta.cl ~ 1
        # interindividual variability on clearance and volume
        add.err <- 0.1 # residual variability
      })
      model({
        cl <- exp(log_allo_wt * all.cl + tcl + eta.cl) # individual value of clearance
        v <- exp(tv) # individual value of volume
        ke <- cl / v # elimination rate constant
        d / dt(A1) <- -ke * A1 # model differential equation
        cp <- A1 / v # concentration in plasma
        cp ~ add(add.err) # define error model
      })
    }
    p <- list()
    p[[1]] <- nlmixr(run1)
    p[[2]] <- nlmixr(run2)
    p[[3]] <- nlmixr(run3)
    p[[4]] <- nlmixr(run4)
    p[[5]] <- nlmixr(run5)
    p[[6]] <- nlmixr(run6)

    ref <- list(log_allo_wt = c(all.cl = "tcl"))

    for (i in 1:6) {
      test_that(sprintf("Parsing cl/log_allo_wt works correctly #%d", i), {
        expect_equal(p[[1]]$cov.ref, ref)
      })
    }

    run7 <- function() {
      ini({
        tcl <- log(0.008) # typical value of clearance
        tv <- log(0.6) # typical value of volume
        all.cl <- 1 # allometric exponent on CL
        eta.cl ~ 1
        # interindividual variability on clearance and volume
        add.err <- 0.1 # residual variability
      })
      model({
        cl <- exp(tcl + eta.cl) # individual value of clearance
        v <- exp(tv + all.cl * log_allo_wt) # individual value of volume
        ke <- cl / v # elimination rate constant
        d / dt(A1) <- -ke * A1 # model differential equation
        cp <- A1 / v # concentration in plasma
        cp ~ add(add.err) # define error model
      })
    }

    ref <- list(log_allo_wt = c(all.cl = "tv"))
    p7 <- nlmixr(run7)

    ## I'm not sure why, but this doesn't seem to work, though when they parse they seem to be correct.

    ## expect_equal("Parsing on v without eta works.",{
    ##     expect_true(p7$cov.ref$log_allo_wt["all.cl"]=="tv");
    ## })
  },
  test = "saem"
)