:orphan: 





.. _builtin_fit_example_fit:



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: :download:`builtin_fit_example.pyml <builtin_fit_example.pyml>`

:Input Data: :download:`builtin_fit_example_data.csv <builtin_fit_example_data.csv>`

:Diagram: 


.. thumbnail:: builtin_fit_example.pyml_output/fit/compartment_diagram.svg
    :width: 200px


Initial fixed effect estimates
==============================

.. code-block:: pyml

    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
=====================

.. code-block:: pyml

    -890.877751328


which required N. iterations and took 708.69 seconds

Final fitted fixed effects
==========================

.. code-block:: pyml

    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: :download:`fx_params.csv (fit) <builtin_fit_example.pyml_output/fit/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <builtin_fit_example.pyml_output/fit/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <builtin_fit_example.pyml_output/fit/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <builtin_fit_example.pyml_output/fit/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <builtin_fit_example.pyml_output/fit/solN/px_params.csv>`



Plots
*****



Dense sim plots
===============



.. thumbnail:: images/fit_dense/000000.svg
    :width: 200px


.. thumbnail:: images/fit_dense/000001.svg
    :width: 200px


.. thumbnail:: images/fit_dense/000002.svg
    :width: 200px


Alternatively see :ref:`builtin_fit_example_dense_sim_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
================  ==============  ================  =============  ============