inst/models/values-1.1.1.3-U056_saem-unix.R

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

expected_values[[runno]] <- list(lik = c(-43944.72, 87911.45, 87986.78), param = c(6.9179, 
5.5185, 4.2551, 1.3351, 3.9251, 0.19986), stdev_param = c(0.32772, 
0.33682, 0.29856, 0.27284, 0.34632, NA), sigma = c(prop.err = 0.19986), 
    parFixedDf = structure(list(Estimate = c(lVM = 6.91790534202431, 
    lKM = 5.51853890783228, lVc = 4.2550948909022, lQ = 1.33509874731572, 
    lVp = 3.92513806936687, prop.err = 0.199857212346383), SE = c(lVM = 0.0394575689770519, 
    lKM = 0.0536290567585077, lVc = 0.028068872229863, lQ = 0.0476851157982296, 
    lVp = 0.0414679754501222, prop.err = NA), "%RSE" = c(0.570368731953564, 
    0.971798109140694, 0.659653261549512, 3.57165459814137, 1.05647176525464, 
    NA), "Back-transformed" = c(1010.20174965818, 249.270563721573, 
    70.463503216598, 3.80037120434626, 50.6600721800821, 0.199857212346383
    ), "CI Lower" = c(935.021877968555, 224.399487114687, 66.6917269999777, 
    3.46127714409262, 46.7055186921714, NA), "CI Upper" = c(1091.42641371089, 
    276.898199443362, 74.4485936847487, 4.17268559828383, 54.9494574764526, 
    NA), "BSV(CV%)" = c(33.671466973171, 34.6606385685366, 30.5334240516791, 
    27.7998305000252, 35.6966106731906, NA), "Shrink(SD)%" = c(12.0128578355114, 
    34.3766003765904, 1.61889415706469, 48.212955722436, 20.0094359372192, 
    NA)), class = "data.frame", row.names = c("lVM", "lKM", "lVc", 
    "lQ", "lVp", "prop.err")), omega = structure(c(0.107397531462951, 
    0, 0, 0, 0, 0, 0.113450094541847, 0, 0, 0, 0, 0, 0.0891357009109299, 
    0, 0, 0, 0, 0, 0.0744421840543574, 0, 0, 0, 0, 0, 0.119936095141956
    ), .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 = 204.593, 
        setup = 0.234921000000042, table = 0.536000000000058, 
        cwres = 0.807999999999993, covariance = 0.167000000000144, 
        other = 0.378079000000014), class = "data.frame", row.names = "elapsed"), 
    objDf = structure(list(OBJF = 75097.8246272096, AIC = 87911.4490094186, 
        BIC = 87986.7762917053, "Log-likelihood" = -43944.7245047093, 
        "Condition Number" = 4.56848663092183), row.names = "FOCEi", class = "data.frame"))