inst/models/values-1.1.1.3-U063_solve_saem-unix.R

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

expected_values[[runno]] <- list(lik = c(-38506.24, 77034.49, 77109.82), param = c(1.3454, 
4.2102, 1.4452, 3.8785, -0.080902, 0.19881), stdev_param = c(0.25946, 
0.31766, 0.29221, 0.40246, 0.32291, NA), sigma = c(prop.err = 0.19881), 
    parFixedDf = structure(list(Estimate = c(lCl = 1.34539443748895, 
    lVc = 4.21020447272894, lQ = 1.44516836813235, lVp = 3.87852233283189, 
    lKA = -0.0809017570424397, prop.err = 0.198814535890824), 
        SE = c(lCl = 0.0291550493058924, lVc = 0.0282612318111431, 
        lQ = 0.0621576473473919, lVp = 0.0354134247891739, lKA = 0.0374535861574148, 
        prop.err = NA), "%RSE" = c(2.16702615184789, 0.671255564764172, 
        4.30106613997653, 0.91306486724074, 46.2951455278868, 
        NA), "Back-transformed" = c(3.83970076387471, 67.3703137937777, 
        4.24256639567045, 48.3527130397054, 0.922284294930803, 
        0.198814535890824), "CI Lower" = c(3.62644050797517, 
        63.7400743993584, 3.75595138498574, 45.1104215213388, 
        0.857006790364861, NA), "CI Upper" = c(4.06550222557821, 
        71.2073091134916, 4.79222646321352, 51.8280428214164, 
        0.992533933498791, NA), "BSV(CV%)" = c(32.5839782724058, 
        26.38846199183, 41.9319708795623, 29.8557709962298, 33.1514147666472, 
        NA), "Shrink(SD)%" = c(0.0171559160359602, 7.95614623752056, 
        33.1640532742759, 17.2019090436035, 12.0121738906342, 
        NA)), class = "data.frame", row.names = c("lCl", "lVc", 
    "lQ", "lVp", "lKA", "prop.err")), omega = structure(c(0.0673175553824095, 
    0, 0, 0, 0, 0, 0.100905012232542, 0, 0, 0, 0, 0, 0.0853853697525082, 
    0, 0, 0, 0, 0, 0.161973446205591, 0, 0, 0, 0, 0, 0.104271389868319
    ), .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 = 98.92, 
        setup = 0.326961000000026, table = 0.587999999999965, 
        cwres = 1.00100000000009, covariance = 0.129000000000019, 
        other = 0.389038999999983), class = "data.frame", row.names = "elapsed"), 
    objDf = structure(list(OBJF = 64220.8641908348, AIC = 77034.4885730439, 
        BIC = 77109.8158553305, "Log-likelihood" = -38506.2442865219, 
        "Condition Number" = 7.15468563373643), row.names = "FOCEi", class = "data.frame"))