inst/models/values-1.1.1.3-U062_solve_saem-unix.R

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

expected_values[[runno]] <- list(lik = c(-13224.64, 26471.28, 26534.33), param = c(1.3603, 
4.2322, 1.3738, 3.8828, -0.082017, 0.19416), stdev_param = c(0.27956, 
0.30694, 0.31952, 0.32791, 0.35965, NA), sigma = c(prop.err = 0.19416), 
    parFixedDf = structure(list(Estimate = c(lCl = 1.36033126394913, 
    lVc = 4.23215119131238, lQ = 1.37381586993406, lVp = 3.8827597757202, 
    lKA = -0.082017108389879, prop.err = 0.19415985496711), SE = c(lCl = 0.0285675283820851, 
    lVc = 0.041588730887297, lQ = 0.123258794248241, lVp = 0.0573002474574704, 
    lKA = 0.063924288407078, prop.err = NA), "%RSE" = c(2.10004203675741, 
    0.982685376946574, 8.97200250381127, 1.47576081878107, 77.9401879217756, 
    NA), "Back-transformed" = c(3.89748418397449, 68.8652151995417, 
    3.95039616939885, 48.5580396223039, 0.921256197351321, 0.19415985496711
    ), "CI Lower" = c(3.68525579044501, 63.4745333982472, 3.10257704947502, 
    43.3997358376751, 0.812770569656718, NA), "CI Upper" = c(4.12193449467372, 
    74.7137097444208, 5.0298927782766, 54.329436952802, 1.04422208781093, 
    NA), "BSV(CV%)" = c(31.4310041157679, 28.5112031118897, 33.692553031394, 
    32.7851395712572, 37.1600635128661, NA), "Shrink(SD)%" = c(0.703069992782979, 
    15.6656178045198, 62.3450408484174, 33.4881728866632, 29.0679457558813, 
    NA)), class = "data.frame", row.names = c("lCl", "lVc", "lQ", 
    "lVp", "lKA", "prop.err")), omega = structure(c(0.0781537280177815, 
    0, 0, 0, 0, 0, 0.0942103042288283, 0, 0, 0, 0, 0, 0.10209306726118, 
    0, 0, 0, 0, 0, 0.107525102927145, 0, 0, 0, 0, 0, 0.129348810836102
    ), .Dim = c(5L, 5L), .Dimnames = list(c("eta.Vc", "eta.Cl", 
    "eta.Vp", "eta.Q", "eta.KA"), c("eta.Vc", "eta.Cl", "eta.Vp", 
    "eta.Q", "eta.KA"))), time = structure(list(saem = 28.074, 
        setup = 0.316125999999988, table = 0.615999999999985, 
        cwres = 1.16999999999996, covariance = 0.0450000000000728, 
        other = 0.454874000000082), class = "data.frame", row.names = "elapsed"), 
    objDf = structure(list(OBJF = 22258.9179787061, AIC = 26471.2776901194, 
        BIC = 26534.3289280608, "Log-likelihood" = -13224.6388450597, 
        "Condition Number" = 24.1902633908432), row.names = "FOCEi", class = "data.frame"))