: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

    -913.5629


which required N. iterations and took 736.41 seconds

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

.. code-block:: pyml

    f[KA] = 0.1713
    f[CL] = 1.8060
    f[V1] = 43.8081
    f[Q] = 1.8123
    f[V2] = 85.0498
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.1113, 0.0309, -0.0452, -0.0276, 0.0024 ],
        [ 0.0309, 0.1342, 0.0223, -0.1133, -0.2044 ],
        [ -0.0452, 0.0223, 0.0280, -0.0182, -0.0579 ],
        [ -0.0276, -0.1133, -0.0182, 0.2058, 0.2750 ],
        [ 0.0024, -0.2044, -0.0579, 0.2750, 0.4314 ],
    ]
    f[PNOISE] = 0.1339



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]                    0.1713            1.0000         0.8287        0.8287
f[CL]                    1.8060            1.0000         0.8060        0.8060
f[V1]                   43.8081           20.0000         1.1904       23.8081
f[Q]                     1.8123            0.5000         2.6247        1.3123
f[V2]                   85.0498          100.0000         0.1495       14.9502
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE]                0.1339            0.1000         0.3393        0.0339
===============  ==============  ================  =============  ============

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


================  ==============  ================  =============  ============
Variable Name       Fitted Value    Starting Value    Prop Change    Abs Change
================  ==============  ================  =============  ============
f[KA_isv]                 0.1113            0.0500         1.2255        0.0613
f[KA_isv;CL_isv]          0.0309            0.0100         2.0870        0.0209
f[KA_isv;V1_isv]         -0.0452            0.0100         5.5155        0.0552
f[KA_isv;Q_isv]          -0.0276            0.0100         3.7559        0.0376
f[KA_isv;V2_isv]          0.0024            0.0100         0.7560        0.0076
f[CL_isv;KA_isv]          0.0309            0.0100         2.0870        0.0209
f[CL_isv]                 0.1342            0.0500         1.6849        0.0842
f[CL_isv;V1_isv]          0.0223            0.0100         1.2346        0.0123
f[CL_isv;Q_isv]          -0.1133            0.0100        12.3350        0.1233
f[CL_isv;V2_isv]         -0.2044            0.0100        21.4427        0.2144
f[V1_isv;KA_isv]         -0.0452            0.0100         5.5155        0.0552
f[V1_isv;CL_isv]          0.0223            0.0100         1.2346        0.0123
f[V1_isv]                 0.0280            0.0500         0.4398        0.0220
f[V1_isv;Q_isv]          -0.0182            0.0100         2.8178        0.0282
f[V1_isv;V2_isv]         -0.0579            0.0100         6.7942        0.0679
f[Q_isv;KA_isv]          -0.0276            0.0100         3.7559        0.0376
f[Q_isv;CL_isv]          -0.1133            0.0100        12.3350        0.1233
f[Q_isv;V1_isv]          -0.0182            0.0100         2.8178        0.0282
f[Q_isv]                  0.2058            0.0500         3.1168        0.1558
f[Q_isv;V2_isv]           0.2750            0.0100        26.5026        0.2650
f[V2_isv;KA_isv]          0.0024            0.0100         0.7560        0.0076
f[V2_isv;CL_isv]         -0.2044            0.0100        21.4427        0.2144
f[V2_isv;V1_isv]         -0.0579            0.0100         6.7942        0.0679
f[V2_isv;Q_isv]           0.2750            0.0100        26.5026        0.2650
f[V2_isv]                 0.4314            0.0500         7.6275        0.3814
================  ==============  ================  =============  ============