: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

    -908.9331


which required 1.30 iterations and took 357.29 seconds

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

.. code-block:: pyml

    f[KA] = 0.1089
    f[CL] = 2.2693
    f[V1] = 27.1342
    f[Q] = 1.8655
    f[V2] = 52.0726
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.0807, 0.0287, 0.0288, 0.0131, -0.0713 ],
        [ 0.0287, 0.0228, 0.0284, 0.0062, -0.0334 ],
        [ 0.0288, 0.0284, 0.1865, 0.0233, -0.1768 ],
        [ 0.0131, 0.0062, 0.0233, 0.0109, -0.0022 ],
        [ -0.0713, -0.0334, -0.1768, -0.0022, 0.5211 ],
    ]
    f[PNOISE] = 0.1395



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          0.1089         0.8911        0.8911
f[CL]                      1.0000          2.2693         1.2693        1.2693
f[V1]                     20.0000         27.1342         0.3567        7.1342
f[Q]                       0.5000          1.8655         2.7310        1.3655
f[V2]                    100.0000         52.0726         0.4793       47.9274
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.1395         0.3946        0.0395
===============  ================  ==============  =============  ============

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


================  ================  ==============  =============  ============
Variable Name       Starting Value    Fitted Value    Prop Change    Abs Change
================  ================  ==============  =============  ============
f[KA_isv]                   0.0500          0.0807         0.6132        0.0307
f[KA_isv;CL_isv]            0.0100          0.0287         1.8699        0.0187
f[KA_isv;V1_isv]            0.0100          0.0288         1.8843        0.0188
f[KA_isv;Q_isv]             0.0100          0.0131         0.3122        0.0031
f[KA_isv;V2_isv]            0.0100         -0.0713         8.1342        0.0813
f[CL_isv;KA_isv]            0.0100          0.0287         1.8699        0.0187
f[CL_isv]                   0.0500          0.0228         0.5430        0.0272
f[CL_isv;V1_isv]            0.0100          0.0284         1.8411        0.0184
f[CL_isv;Q_isv]             0.0100          0.0062         0.3750        0.0038
f[CL_isv;V2_isv]            0.0100         -0.0334         4.3411        0.0434
f[V1_isv;KA_isv]            0.0100          0.0288         1.8843        0.0188
f[V1_isv;CL_isv]            0.0100          0.0284         1.8411        0.0184
f[V1_isv]                   0.0500          0.1865         2.7307        0.1365
f[V1_isv;Q_isv]             0.0100          0.0233         1.3299        0.0133
f[V1_isv;V2_isv]            0.0100         -0.1768        18.6798        0.1868
f[Q_isv;KA_isv]             0.0100          0.0131         0.3122        0.0031
f[Q_isv;CL_isv]             0.0100          0.0062         0.3750        0.0038
f[Q_isv;V1_isv]             0.0100          0.0233         1.3299        0.0133
f[Q_isv]                    0.0500          0.0109         0.7829        0.0391
f[Q_isv;V2_isv]             0.0100         -0.0022         1.2245        0.0122
f[V2_isv;KA_isv]            0.0100         -0.0713         8.1342        0.0813
f[V2_isv;CL_isv]            0.0100         -0.0334         4.3411        0.0434
f[V2_isv;V1_isv]            0.0100         -0.1768        18.6798        0.1868
f[V2_isv;Q_isv]             0.0100         -0.0022         1.2245        0.0122
f[V2_isv]                   0.0500          0.5211         9.4217        0.4711
================  ================  ==============  =============  ============