: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

    -908.4657


which required 1.30 iterations and took 346.70 seconds

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

.. code-block:: pyml

    f[KA] = 0.1015
    f[CL] = 2.3617
    f[V1] = 24.5062
    f[Q] = 1.8140
    f[V2] = 45.0126
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.0677, 0.0196, 0.0351, 0.0427, -0.0393 ],
        [ 0.0196, 0.0149, 0.0174, 0.0094, 0.0124 ],
        [ 0.0351, 0.0174, 0.2453, 0.0783, -0.1548 ],
        [ 0.0427, 0.0094, 0.0783, 0.0475, -0.0534 ],
        [ -0.0393, 0.0124, -0.1548, -0.0534, 0.3086 ],
    ]
    f[PNOISE] = 0.1400



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      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[KA]                      1.0000          0.1015         0.8985        0.8985
f[CL]                      1.0000          2.3617         1.3617        1.3617
f[V1]                     20.0000         24.5062         0.2253        4.5062
f[Q]                       0.5000          1.8140         2.6280        1.3140
f[V2]                    100.0000         45.0126         0.5499       54.9874
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.1400         0.4005        0.0400
===============  ================  ==============  =============  ============

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


================  ================  ==============  =============  ============
Variable Name       Starting Value    Fitted Value    Prop Change    Abs Change
================  ================  ==============  =============  ============
f[KA_isv]                   0.0500          0.0677         0.3549        0.0177
f[KA_isv;CL_isv]            0.0100          0.0196         0.9605        0.0096
f[KA_isv;V1_isv]            0.0100          0.0351         2.5060        0.0251
f[KA_isv;Q_isv]             0.0100          0.0427         3.2706        0.0327
f[KA_isv;V2_isv]            0.0100         -0.0393         4.9343        0.0493
f[CL_isv;KA_isv]            0.0100          0.0196         0.9605        0.0096
f[CL_isv]                   0.0500          0.0149         0.7025        0.0351
f[CL_isv;V1_isv]            0.0100          0.0174         0.7415        0.0074
f[CL_isv;Q_isv]             0.0100          0.0094         0.0559        0.0006
f[CL_isv;V2_isv]            0.0100          0.0124         0.2420        0.0024
f[V1_isv;KA_isv]            0.0100          0.0351         2.5060        0.0251
f[V1_isv;CL_isv]            0.0100          0.0174         0.7415        0.0074
f[V1_isv]                   0.0500          0.2453         3.9068        0.1953
f[V1_isv;Q_isv]             0.0100          0.0783         6.8291        0.0683
f[V1_isv;V2_isv]            0.0100         -0.1548        16.4829        0.1648
f[Q_isv;KA_isv]             0.0100          0.0427         3.2706        0.0327
f[Q_isv;CL_isv]             0.0100          0.0094         0.0559        0.0006
f[Q_isv;V1_isv]             0.0100          0.0783         6.8291        0.0683
f[Q_isv]                    0.0500          0.0475         0.0499        0.0025
f[Q_isv;V2_isv]             0.0100         -0.0534         6.3370        0.0634
f[V2_isv;KA_isv]            0.0100         -0.0393         4.9343        0.0493
f[V2_isv;CL_isv]            0.0100          0.0124         0.2420        0.0024
f[V2_isv;V1_isv]            0.0100         -0.1548        16.4829        0.1648
f[V2_isv;Q_isv]             0.0100         -0.0534         6.3370        0.0634
f[V2_isv]                   0.0500          0.3086         5.1711        0.2586
================  ================  ==============  =============  ============