nmTest({ test_that("npde", { .nlmixr <- function(...) suppressWarnings(suppressMessages(nlmixr(...))) one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models 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) linCmt() ~ add(add.sd) }) } fit <- .nlmixr(one.cmt, theo_sd, est="saem") expect_false(all(c("EPRED","ERES","NPDE","NPD", "PDE", "PD") %in% names(fit))) suppressMessages(expect_error(addNpde(fit), NA)) expect_true(all(c("EPRED","ERES","NPDE","NPD", "PDE", "PD") %in% names(fit))) .range1 <- range(fit$PDE) .range2 <- range(fit$PD) expect_true(.range1[1] > 0) expect_true(.range2[1] > 0) expect_true(.range1[2] < 1) expect_true(.range2[2] < 1) .range1 <- range(fit$NPDE) .range2 <- range(fit$NPD) .range3 <- range(fit$ERES) expect_true(.range1[1] < -1.7) expect_true(.range2[1] < -1.7) expect_true(.range3[1] < -1.7) expect_true(.range1[2] > 1.7) expect_true(.range2[2] > 1.7) expect_true(.range3[2] > 1.7) .range4 <- range(fit$EPRED) expect_true(.range4[1] > -0.1) expect_true(.range4[2] > 7) fit <- .nlmixr(one.cmt, theo_sd, est="saem") expect_false(all(c("EPRED","ERES","NPDE","NPD","PDE","PD") %in% names(fit))) fit2 <- suppressMessages(addNpde(fit, updateObject=FALSE)) expect_false(all(c("EPRED","ERES","NPDE","NPD","PDE","PD") %in% names(fit))) expect_true(all(c("EPRED","ERES","NPDE","NPD","PDE","PD") %in% names(fit2))) fit <- .nlmixr(one.cmt, theo_sd, est="saem", table=tableControl(npde=TRUE)) expect_true(all(c("EPRED","ERES","NPDE","NPD", "PDE","PD") %in% names(fit))) }) test_that("pheno", { pheno <- function() { ini({ tcl <- log(0.008) # typical value of clearance tv <- log(0.6) # typical value of volume ## var(eta.cl) eta.cl + eta.v ~ c(1, 0.01, 1) ## cov(eta.cl, eta.v), var(eta.v) # 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 + eta.v) # 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 }) } fit <- nlmixr(pheno, pheno_sd, "saem", control=list(print=0), table=list(npde=TRUE)) # Since there is a correlation here the npde and npd expect_false(isTRUE(all.equal(fit$NPDE, fit$NPD))) expect_false(isTRUE(all.equal(fit$PDE, fit$PD))) }) })