inst/models/values-1.1.1.3-U054_saem-unix.R

In nlmixrdevelopment/nlmixr.examples: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics

expected_values[[runno]] <- list(lik = c(-12624.03, 25270.06, 25333.11), param = c(7.0523, 
5.734, 4.2559, 1.3272, 3.9132, 0.19929), stdev_param = c(0.26891, 
0.4289, 0.29881, 0.26922, 0.36044, NA), sigma = c(prop.err = 0.19929), 
    parFixedDf = structure(list(Estimate = c(lVM = 7.05229688389662, 
    lKM = 5.73399242795804, lVc = 4.25591099950162, lQ = 1.32717573764477, 
    lVp = 3.91318996810098, prop.err = 0.199288172794764), SE = c(lVM = 0.113133946485573, 
    lKM = 0.178002735338771, lVc = 0.0283131697524897, lQ = 0.0528861720696728, 
    lVp = 0.0611041016004946, prop.err = NA), "%RSE" = c(1.60421417799222, 
    3.10434200210761, 0.665266960606206, 3.98486579957568, 1.56149080669722, 
    NA), "Back-transformed" = c(1155.50976884881, 309.201271199497, 
    70.5210325594216, 3.7703797945461, 50.0583821919359, 0.199288172794764
    ), "CI Lower" = c(925.707604918576, 218.134856110872, 66.7142255303544, 
    3.39913417611361, 44.4083773038934, NA), "CI Upper" = c(1442.35914106213, 
    438.28587423365, 74.5450613225544, 4.18217200574738, 56.4272279197697, 
    NA), "BSV(CV%)" = c(27.3843692506024, 44.9401831946649, 30.5610626092754, 
    27.4177884239828, 37.2465938626225, NA), "Shrink(SD)%" = c(30.4418755250808, 
    35.4406634621203, 2.19566165639061, 53.7862991723018, 29.7980188121028, 
    NA)), class = "data.frame", row.names = c("lVM", "lKM", "lVc", 
    "lQ", "lVp", "prop.err")), omega = structure(c(0.0723117014700279, 
    0, 0, 0, 0, 0, 0.183955227091829, 0, 0, 0, 0, 0, 0.0892901455057427, 
    0, 0, 0, 0, 0, 0.0724820552584338, 0, 0, 0, 0, 0, 0.129914375103443
    ), .Dim = c(5L, 5L), .Dimnames = list(c("eta.VM", "eta.KM", 
    "eta.Vc", "eta.Q", "eta.Vp"), c("eta.VM", "eta.KM", "eta.Vc", 
    "eta.Q", "eta.Vp"))), time = structure(list(saem = 168.149, 
        setup = 0.249918000000037, table = 0.140999999999963, 
        cwres = 0.418000000000006, covariance = 0.0940000000000509, 
        other = 0.298082000000079), class = "data.frame", row.names = "elapsed"), 
    objDf = structure(list(OBJF = 21057.7001088894, AIC = 25270.0598203027, 
        BIC = 25333.1110582441, "Log-likelihood" = -12624.0299101513, 
        "Condition Number" = 57.591535552599), row.names = "FOCEi", class = "data.frame"))