:orphan: 





.. _builtin_fit_example_fit:



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

[Generated automatically as a Fitting summary]

Model Description
*****************


:Name: builtin_fit_example

:Title: First order absorption model with peripheral compartment

:Author: PoPy for PK/PD

: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>`

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[KA]                      1.0000          0.1045        0.8955         0.8955
f[CL]                      1.0000          2.2200        1.2200         1.2200
f[V1]                     20.0000         24.8947        4.8947         0.2447
f[Q]                       0.5000          1.9247        1.4247         2.8495
f[V2]                    100.0000         54.8367       45.1633         0.4516
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.1000          0.1397        0.0397         0.3974
===============  ================  ==============  ============  =============

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


================  ================  ==============  ============  =============
Variable Name       Starting Value    Fitted Value    Abs Change    Prop Change
================  ================  ==============  ============  =============
f[KA_isv]                   0.0500          0.0597        0.0097         0.1931
f[KA_isv;CL_isv]            0.0100          0.0265        0.0165         1.6477
f[KA_isv;V1_isv]            0.0100          0.0392        0.0292         2.9234
f[KA_isv;Q_isv]             0.0100          0.0084        0.0016         0.1560
f[KA_isv;V2_isv]            0.0100         -0.1074        0.1174        11.7363
f[CL_isv;KA_isv]            0.0100          0.0265        0.0165         1.6477
f[CL_isv]                   0.0500          0.0214        0.0286         0.5715
f[CL_isv;V1_isv]            0.0100          0.0394        0.0294         2.9408
f[CL_isv;Q_isv]             0.0100          0.0053        0.0047         0.4708
f[CL_isv;V2_isv]            0.0100         -0.0500        0.0600         5.9991
f[V1_isv;KA_isv]            0.0100          0.0392        0.0292         2.9234
f[V1_isv;CL_isv]            0.0100          0.0394        0.0294         2.9408
f[V1_isv]                   0.0500          0.2501        0.2001         4.0011
f[V1_isv;Q_isv]             0.0100          0.0143        0.0043         0.4311
f[V1_isv;V2_isv]            0.0100         -0.2982        0.3082        30.8220
f[Q_isv;KA_isv]             0.0100          0.0084        0.0016         0.1560
f[Q_isv;CL_isv]             0.0100          0.0053        0.0047         0.4708
f[Q_isv;V1_isv]             0.0100          0.0143        0.0043         0.4311
f[Q_isv]                    0.0500          0.0046        0.0454         0.9084
f[Q_isv;V2_isv]             0.0100         -0.0126        0.0226         2.2645
f[V2_isv;KA_isv]            0.0100         -0.1074        0.1174        11.7363
f[V2_isv;CL_isv]            0.0100         -0.0500        0.0600         5.9991
f[V2_isv;V1_isv]            0.0100         -0.2982        0.3082        30.8220
f[V2_isv;Q_isv]             0.0100         -0.0126        0.0226         2.2645
f[V2_isv]                   0.0500          0.7221        0.6721        13.4426
================  ================  ==============  ============  =============

Individual simulated (sim) plots
================================



.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000003.svg
    :width: 200px


Alternatively see :ref:`builtin_fit_example_simulated_sim_plots`

Population simulated (sim) plots
================================


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    -910.0447


which required 1.30 iterations and took 71.09 seconds

Fitted f[X] values (after fitting)
==================================


.. code-block:: pyml

    f[KA] = 0.1045
    f[CL] = 2.2200
    f[V1] = 24.8947
    f[Q] = 1.9247
    f[V2] = 54.8367
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.0597, 0.0265, 0.0392, 0.0084, -0.1074 ],
        [ 0.0265, 0.0214, 0.0394, 0.0053, -0.0500 ],
        [ 0.0392, 0.0394, 0.2501, 0.0143, -0.2982 ],
        [ 0.0084, 0.0053, 0.0143, 0.0046, -0.0126 ],
        [ -0.1074, -0.0500, -0.2982, -0.0126, 0.7221 ],
    ]
    f[PNOISE] = 0.1397



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


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

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

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

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

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

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



Inputs
******


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


Starting f[X] values (before fitting)
=====================================


.. 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

