: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

    -911.1884


which required 1.30 iterations and took 466.38 seconds

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

.. code-block:: pyml

    f[KA] = 0.1037
    f[CL] = 2.1890
    f[V1] = 24.4333
    f[Q] = 1.9623
    f[V2] = 57.4482
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.0419, 0.0155, 0.0370, 0.0056, -0.0810 ],
        [ 0.0155, 0.0148, 0.0388, 0.0040, -0.0306 ],
        [ 0.0370, 0.0388, 0.2833, 0.0233, -0.3170 ],
        [ 0.0056, 0.0040, 0.0233, 0.0046, -0.0198 ],
        [ -0.0810, -0.0306, -0.3170, -0.0198, 0.6653 ],
    ]
    f[PNOISE] = 0.1421



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          0.1037         0.8963        0.8963
f[CL]                      1.0000          2.1890         1.1890        1.1890
f[V1]                     20.0000         24.4333         0.2217        4.4333
f[Q]                       0.5000          1.9623         2.9246        1.4623
f[V2]                    100.0000         57.4482         0.4255       42.5518
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.1421         0.4214        0.0421
===============  ================  ==============  =============  ============

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


================  ================  ==============  =============  ============
Variable Name       Starting Value    Fitted Value    Prop Change    Abs Change
================  ================  ==============  =============  ============
f[KA_isv]                   0.0500          0.0419         0.1623        0.0081
f[KA_isv;CL_isv]            0.0100          0.0155         0.5519        0.0055
f[KA_isv;V1_isv]            0.0100          0.0370         2.7028        0.0270
f[KA_isv;Q_isv]             0.0100          0.0056         0.4392        0.0044
f[KA_isv;V2_isv]            0.0100         -0.0810         9.1006        0.0910
f[CL_isv;KA_isv]            0.0100          0.0155         0.5519        0.0055
f[CL_isv]                   0.0500          0.0148         0.7038        0.0352
f[CL_isv;V1_isv]            0.0100          0.0388         2.8823        0.0288
f[CL_isv;Q_isv]             0.0100          0.0040         0.5963        0.0060
f[CL_isv;V2_isv]            0.0100         -0.0306         4.0570        0.0406
f[V1_isv;KA_isv]            0.0100          0.0370         2.7028        0.0270
f[V1_isv;CL_isv]            0.0100          0.0388         2.8823        0.0288
f[V1_isv]                   0.0500          0.2833         4.6660        0.2333
f[V1_isv;Q_isv]             0.0100          0.0233         1.3252        0.0133
f[V1_isv;V2_isv]            0.0100         -0.3170        32.7038        0.3270
f[Q_isv;KA_isv]             0.0100          0.0056         0.4392        0.0044
f[Q_isv;CL_isv]             0.0100          0.0040         0.5963        0.0060
f[Q_isv;V1_isv]             0.0100          0.0233         1.3252        0.0133
f[Q_isv]                    0.0500          0.0046         0.9079        0.0454
f[Q_isv;V2_isv]             0.0100         -0.0198         2.9781        0.0298
f[V2_isv;KA_isv]            0.0100         -0.0810         9.1006        0.0910
f[V2_isv;CL_isv]            0.0100         -0.0306         4.0570        0.0406
f[V2_isv;V1_isv]            0.0100         -0.3170        32.7038        0.3270
f[V2_isv;Q_isv]             0.0100         -0.0198         2.9781        0.0298
f[V2_isv]                   0.0500          0.6653        12.3061        0.6153
================  ================  ==============  =============  ============