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First order absorption model with peripheral compartment

[Generated automatically as a Fitting summary]

Inputs

Description

Name:builtin_fit_example
Title:First order absorption model with peripheral compartment
Author:J.R. Hartley
Abstract:
A two compartment PK model with bolus dose and
first order absorption, similar to a Nonmem advan4trans4 model.
Keywords:fitting; pk; advan4; dep_two_cmp; first order
Input Script:builtin_fit_example.pyml
Input Data:builtin_fit_example_data.csv
Diagram:

Initial fixed effect estimates

f[KA] = 1
f[CL] = 1
f[V1] = 20
f[Q] = 0.5
f[V2] = 100
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.05, 0.01, 0.01, 0.01, 0.01 ],
    [ 0.01, 0.05, 0.01, 0.01, 0.01 ],
    [ 0.01, 0.01, 0.05, 0.01, 0.01 ],
    [ 0.01, 0.01, 0.01, 0.05, 0.01 ],
    [ 0.01, 0.01, 0.01, 0.01, 0.05 ]
]
f[PNOISE] = 0.1

Outputs

Final objective value

-890.877751328

which required N. iterations and took 708.69 seconds

Final fitted fixed effects

f[KA] = 0.23326
f[CL] = 2.2423
f[V1] = 58.107
f[Q] = 0.69961
f[V2] = 114.93
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.16704, 0.02277, -0.041913, 0.085272, 0.0053418 ],
    [ 0.02277, 0.02969, 0.0082606, 0.010176, 0.0073914 ],
    [ -0.041913, 0.0082606, 0.031914, -0.022249, 0.0097421 ],
    [ 0.085272, 0.010176, -0.022249, 0.12799, -0.001646 ],
    [ 0.0053418, 0.0073914, 0.0097421, -0.001646, 0.052089 ]
]
f[PNOISE] = 0.14935

Fitted parameter .csv files

Fixed Effects:fx_params.csv (fit)
Random Effects:rx_params.csv (fit)
Model params:mx_params.csv (fit)
State values:sx_params.csv (fit)
Predictions:px_params.csv (fit)

Plots

Dense sim plots

Alternatively see All dense_sim graph plots

Comparison

Compare Main f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA] 0.23326 1 0.76674 0.76674
f[CL] 2.24226 1 1.24226 1.24226
f[V1] 58.1068 20 1.90534 38.1068
f[Q] 0.699607 0.5 0.399213 0.199607
f[V2] 114.934 100 0.14934 14.934

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE] 0.149348 0.1 0.493476 0.0493476

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA_isv] 0.167038 0.05 2.34077 0.117038
f[KA_isv;CL_isv] 0.0227701 0.01 1.27701 0.0127701
f[KA_isv;V1_isv] -0.0419131 0.01 5.19131 0.0519131
f[KA_isv;Q_isv] 0.0852715 0.01 7.52715 0.0752715
f[KA_isv;V2_isv] 0.00534182 0.01 0.465818 0.00465818
f[CL_isv;KA_isv] 0.0227701 0.01 1.27701 0.0127701
f[CL_isv] 0.0296897 0.05 0.406206 0.0203103
f[CL_isv;V1_isv] 0.00826064 0.01 0.173936 0.00173936
f[CL_isv;Q_isv] 0.0101758 0.01 0.0175835 0.000175835
f[CL_isv;V2_isv] 0.00739137 0.01 0.260863 0.00260863
f[V1_isv;KA_isv] -0.0419131 0.01 5.19131 0.0519131
f[V1_isv;CL_isv] 0.00826064 0.01 0.173936 0.00173936
f[V1_isv] 0.0319135 0.05 0.361729 0.0180865
f[V1_isv;Q_isv] -0.0222493 0.01 3.22493 0.0322493
f[V1_isv;V2_isv] 0.00974212 0.01 0.0257884 0.000257884
f[Q_isv;KA_isv] 0.0852715 0.01 7.52715 0.0752715
f[Q_isv;CL_isv] 0.0101758 0.01 0.0175835 0.000175835
f[Q_isv;V1_isv] -0.0222493 0.01 3.22493 0.0322493
f[Q_isv] 0.127988 0.05 1.55977 0.0779883
f[Q_isv;V2_isv] -0.001646 0.01 1.1646 0.011646
f[V2_isv;KA_isv] 0.00534182 0.01 0.465818 0.00465818
f[V2_isv;CL_isv] 0.00739137 0.01 0.260863 0.00260863
f[V2_isv;V1_isv] 0.00974212 0.01 0.0257884 0.000257884
f[V2_isv;Q_isv] -0.001646 0.01 1.1646 0.011646
f[V2_isv] 0.0520889 0.05 0.041777 0.00208885
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