: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

    -913.0781


which required N. iterations and took 384.88 seconds

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

.. code-block:: pyml

    f[KA] = 0.1836
    f[CL] = 1.5613
    f[V1] = 46.1503
    f[Q] = 1.9135
    f[V2] = 121.3791
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.1126, 0.0235, -0.0555, -0.0183, 0.0169 ],
        [ 0.0235, 0.1664, 0.0113, -0.1084, -0.1801 ],
        [ -0.0555, 0.0113, 0.0330, -0.0080, -0.0346 ],
        [ -0.0183, -0.1084, -0.0080, 0.1897, 0.1934 ],
        [ 0.0169, -0.1801, -0.0346, 0.1934, 0.2644 ],
    ]
    f[PNOISE] = 0.1327



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]                    0.1836            1.0000         0.8164        0.8164
f[CL]                    1.5613            1.0000         0.5613        0.5613
f[V1]                   46.1503           20.0000         1.3075       26.1503
f[Q]                     1.9135            0.5000         2.8271        1.4135
f[V2]                  121.3791          100.0000         0.2138       21.3791
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE]                0.1327            0.1000         0.3268        0.0327
===============  ==============  ================  =============  ============

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


================  ==============  ================  =============  ============
Variable Name       Fitted Value    Starting Value    Prop Change    Abs Change
================  ==============  ================  =============  ============
f[KA_isv]                 0.1126            0.0500         1.2517        0.0626
f[KA_isv;CL_isv]          0.0235            0.0100         1.3462        0.0135
f[KA_isv;V1_isv]         -0.0555            0.0100         6.5490        0.0655
f[KA_isv;Q_isv]          -0.0183            0.0100         2.8345        0.0283
f[KA_isv;V2_isv]          0.0169            0.0100         0.6921        0.0069
f[CL_isv;KA_isv]          0.0235            0.0100         1.3462        0.0135
f[CL_isv]                 0.1664            0.0500         2.3286        0.1164
f[CL_isv;V1_isv]          0.0113            0.0100         0.1259        0.0013
f[CL_isv;Q_isv]          -0.1084            0.0100        11.8438        0.1184
f[CL_isv;V2_isv]         -0.1801            0.0100        19.0146        0.1901
f[V1_isv;KA_isv]         -0.0555            0.0100         6.5490        0.0655
f[V1_isv;CL_isv]          0.0113            0.0100         0.1259        0.0013
f[V1_isv]                 0.0330            0.0500         0.3391        0.0170
f[V1_isv;Q_isv]          -0.0080            0.0100         1.7978        0.0180
f[V1_isv;V2_isv]         -0.0346            0.0100         4.4574        0.0446
f[Q_isv;KA_isv]          -0.0183            0.0100         2.8345        0.0283
f[Q_isv;CL_isv]          -0.1084            0.0100        11.8438        0.1184
f[Q_isv;V1_isv]          -0.0080            0.0100         1.7978        0.0180
f[Q_isv]                  0.1897            0.0500         2.7946        0.1397
f[Q_isv;V2_isv]           0.1934            0.0100        18.3362        0.1834
f[V2_isv;KA_isv]          0.0169            0.0100         0.6921        0.0069
f[V2_isv;CL_isv]         -0.1801            0.0100        19.0146        0.1901
f[V2_isv;V1_isv]         -0.0346            0.0100         4.4574        0.0446
f[V2_isv;Q_isv]           0.1934            0.0100        18.3362        0.1834
f[V2_isv]                 0.2644            0.0500         4.2890        0.2144
================  ==============  ================  =============  ============