:orphan: 





.. _builtin_tut_example_fit:



First order absorption model with peripheral compartment
########################################################

[Generated automatically as a Fitting summary]

Inputs
******



Description
===========

:Name: builtin_tut_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: tutorial; pk; advan4; dep_two_cmp; first order

:Input Script: :download:`builtin_tut_example_fit.pyml <builtin_tut_example_fit.pyml>`

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

:Diagram: 


.. thumbnail:: builtin_tut_example_fit.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

    -898.8908


which required 33 iterations and took 90.94 seconds

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

.. code-block:: pyml

    f[KA] = 0.2115
    f[CL] = 2.0587
    f[V1] = 53.0562
    f[Q] = 0.9970
    f[V2] = 104.3821
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.1078, 0.0156, -0.0551, 0.0615, -0.0371 ],
        [ 0.0156, 0.0658, 0.0054, -0.0648, -0.0879 ],
        [ -0.0551, 0.0054, 0.0535, -0.0610, 0.0215 ],
        [ 0.0615, -0.0648, -0.0610, 0.3495, 0.0985 ],
        [ -0.0371, -0.0879, 0.0215, 0.0985, 0.1857 ]
    ]
    f[PNOISE] = 0.1415



Fitted parameter .csv files
===========================


:Fixed Effects: :download:`fx_params.csv (fit) <builtin_tut_example_fit.pyml_output/fit/solN/fx_params.csv>`

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

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

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

:Predictions: :download:`px_params.csv (fit) <builtin_tut_example_fit.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_tut_example_dense_sim_plots`

Comparison
**********



Compare Main f[X]
=================


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[KA]                    0.2115            1.0000         0.7885        0.7885
f[CL]                    2.0587            1.0000         1.0587        1.0587
f[V1]                   53.0562           20.0000         1.6528       33.0562
f[Q]                     0.9970            0.5000         0.9941        0.4970
f[V2]                  104.3821          100.0000         0.0438        4.3821
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE]                0.1415            0.1000         0.4146        0.0415
===============  ==============  ================  =============  ============

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


================  ==============  ================  =============  ============
Variable Name       Fitted Value    Starting Value    Prop Change    Abs Change
================  ==============  ================  =============  ============
f[KA_isv]                 0.1078            0.0500         1.1563        0.0578
f[KA_isv;CL_isv]          0.0156            0.0100         0.5561        0.0056
f[KA_isv;V1_isv]         -0.0551            0.0100         6.5113        0.0651
f[KA_isv;Q_isv]           0.0615            0.0100         5.1516        0.0515
f[KA_isv;V2_isv]         -0.0371            0.0100         4.7117        0.0471
f[CL_isv;KA_isv]          0.0156            0.0100         0.5561        0.0056
f[CL_isv]                 0.0658            0.0500         0.3153        0.0158
f[CL_isv;V1_isv]          0.0054            0.0100         0.4612        0.0046
f[CL_isv;Q_isv]          -0.0648            0.0100         7.4834        0.0748
f[CL_isv;V2_isv]         -0.0879            0.0100         9.7885        0.0979
f[V1_isv;KA_isv]         -0.0551            0.0100         6.5113        0.0651
f[V1_isv;CL_isv]          0.0054            0.0100         0.4612        0.0046
f[V1_isv]                 0.0535            0.0500         0.0708        0.0035
f[V1_isv;Q_isv]          -0.0610            0.0100         7.1003        0.0710
f[V1_isv;V2_isv]          0.0215            0.0100         1.1532        0.0115
f[Q_isv;KA_isv]           0.0615            0.0100         5.1516        0.0515
f[Q_isv;CL_isv]          -0.0648            0.0100         7.4834        0.0748
f[Q_isv;V1_isv]          -0.0610            0.0100         7.1003        0.0710
f[Q_isv]                  0.3495            0.0500         5.9891        0.2995
f[Q_isv;V2_isv]           0.0985            0.0100         8.8473        0.0885
f[V2_isv;KA_isv]         -0.0371            0.0100         4.7117        0.0471
f[V2_isv;CL_isv]         -0.0879            0.0100         9.7885        0.0979
f[V2_isv;V1_isv]          0.0215            0.0100         1.1532        0.0115
f[V2_isv;Q_isv]           0.0985            0.0100         8.8473        0.0885
f[V2_isv]                 0.1857            0.0500         2.7141        0.1357
================  ==============  ================  =============  ============