: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] = 1.0000
    f[CL] = 1.0000
    f[V1] = 20.0000
    f[Q] = 0.5000
    f[V2] = 100.0000
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.0500, 0.0100, 0.0100, 0.0100, 0.0100 ],
        [ 0.0100, 0.0500, 0.0100, 0.0100, 0.0100 ],
        [ 0.0100, 0.0100, 0.0500, 0.0100, 0.0100 ],
        [ 0.0100, 0.0100, 0.0100, 0.0500, 0.0100 ],
        [ 0.0100, 0.0100, 0.0100, 0.0100, 0.0500 ],
    ]
    f[PNOISE] = 0.1000



Outputs
*******



Final objective value
=====================

.. code-block:: pyml

    -913.9529


which required N. iterations and took 1266.15 seconds

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

.. code-block:: pyml

    f[KA] = 0.1846
    f[CL] = 1.6740
    f[V1] = 47.5042
    f[Q] = 1.7764
    f[V2] = 109.6770
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.0965, 0.0418, -0.0532, -0.0200, -0.0172 ],
        [ 0.0418, 0.1443, 0.0079, -0.1026, -0.1679 ],
        [ -0.0532, 0.0079, 0.0369, -0.0123, -0.0301 ],
        [ -0.0200, -0.1026, -0.0123, 0.1982, 0.2090 ],
        [ -0.0172, -0.1679, -0.0301, 0.2090, 0.2667 ],
    ]
    f[PNOISE] = 0.1337



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/000001.svg
    :width: 200px


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


.. thumbnail:: images/fit_dense/000003.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]                    0.1846            1.0000         0.8154        0.8154
f[CL]                    1.6740            1.0000         0.6740        0.6740
f[V1]                   47.5042           20.0000         1.3752       27.5042
f[Q]                     1.7764            0.5000         2.5527        1.2764
f[V2]                  109.6770          100.0000         0.0968        9.6770
===============  ==============  ================  =============  ============

Compare Noise f[X]
==================


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE]                0.1337            0.1000         0.3372        0.0337
===============  ==============  ================  =============  ============

Compare Variance f[X]
=====================


================  ==============  ================  =============  ============
Variable Name       Fitted Value    Starting Value    Prop Change    Abs Change
================  ==============  ================  =============  ============
f[KA_isv]                 0.0965            0.0500         0.9301        0.0465
f[KA_isv;CL_isv]          0.0418            0.0100         3.1804        0.0318
f[KA_isv;V1_isv]         -0.0532            0.0100         6.3232        0.0632
f[KA_isv;Q_isv]          -0.0200            0.0100         3.0007        0.0300
f[KA_isv;V2_isv]         -0.0172            0.0100         2.7245        0.0272
f[CL_isv;KA_isv]          0.0418            0.0100         3.1804        0.0318
f[CL_isv]                 0.1443            0.0500         1.8854        0.0943
f[CL_isv;V1_isv]          0.0079            0.0100         0.2129        0.0021
f[CL_isv;Q_isv]          -0.1026            0.0100        11.2632        0.1126
f[CL_isv;V2_isv]         -0.1679            0.0100        17.7906        0.1779
f[V1_isv;KA_isv]         -0.0532            0.0100         6.3232        0.0632
f[V1_isv;CL_isv]          0.0079            0.0100         0.2129        0.0021
f[V1_isv]                 0.0369            0.0500         0.2610        0.0131
f[V1_isv;Q_isv]          -0.0123            0.0100         2.2338        0.0223
f[V1_isv;V2_isv]         -0.0301            0.0100         4.0071        0.0401
f[Q_isv;KA_isv]          -0.0200            0.0100         3.0007        0.0300
f[Q_isv;CL_isv]          -0.1026            0.0100        11.2632        0.1126
f[Q_isv;V1_isv]          -0.0123            0.0100         2.2338        0.0223
f[Q_isv]                  0.1982            0.0500         2.9646        0.1482
f[Q_isv;V2_isv]           0.2090            0.0100        19.9007        0.1990
f[V2_isv;KA_isv]         -0.0172            0.0100         2.7245        0.0272
f[V2_isv;CL_isv]         -0.1679            0.0100        17.7906        0.1779
f[V2_isv;V1_isv]         -0.0301            0.0100         4.0071        0.0401
f[V2_isv;Q_isv]           0.2090            0.0100        19.9007        0.1990
f[V2_isv]                 0.2667            0.0500         4.3341        0.2167
================  ==============  ================  =============  ============