: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

    -912.2423


which required 1.30 iterations and took 361.27 seconds

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

.. code-block:: pyml

    f[KA] = 0.1019
    f[CL] = 2.1528
    f[V1] = 24.1372
    f[Q] = 1.9547
    f[V2] = 61.7683
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.0322, 0.0145, 0.0383, -0.0011, -0.0925 ],
        [ 0.0145, 0.0165, 0.0431, -0.0013, -0.0482 ],
        [ 0.0383, 0.0431, 0.3030, 0.0110, -0.3540 ],
        [ -0.0011, -0.0013, 0.0110, 0.0040, 0.0023 ],
        [ -0.0925, -0.0482, -0.3540, 0.0023, 0.7273 ],
    ]
    f[PNOISE] = 0.1399



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      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[KA]                      1.0000          0.1019         0.8981        0.8981
f[CL]                      1.0000          2.1528         1.1528        1.1528
f[V1]                     20.0000         24.1372         0.2069        4.1372
f[Q]                       0.5000          1.9547         2.9093        1.4547
f[V2]                    100.0000         61.7683         0.3823       38.2317
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.1399         0.3988        0.0399
===============  ================  ==============  =============  ============

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


================  ================  ==============  =============  ============
Variable Name       Starting Value    Fitted Value    Prop Change    Abs Change
================  ================  ==============  =============  ============
f[KA_isv]                   0.0500          0.0322         0.3564        0.0178
f[KA_isv;CL_isv]            0.0100          0.0145         0.4470        0.0045
f[KA_isv;V1_isv]            0.0100          0.0383         2.8265        0.0283
f[KA_isv;Q_isv]             0.0100         -0.0011         1.1127        0.0111
f[KA_isv;V2_isv]            0.0100         -0.0925        10.2460        0.1025
f[CL_isv;KA_isv]            0.0100          0.0145         0.4470        0.0045
f[CL_isv]                   0.0500          0.0165         0.6706        0.0335
f[CL_isv;V1_isv]            0.0100          0.0431         3.3118        0.0331
f[CL_isv;Q_isv]             0.0100         -0.0013         1.1291        0.0113
f[CL_isv;V2_isv]            0.0100         -0.0482         5.8199        0.0582
f[V1_isv;KA_isv]            0.0100          0.0383         2.8265        0.0283
f[V1_isv;CL_isv]            0.0100          0.0431         3.3118        0.0331
f[V1_isv]                   0.0500          0.3030         5.0596        0.2530
f[V1_isv;Q_isv]             0.0100          0.0110         0.0974        0.0010
f[V1_isv;V2_isv]            0.0100         -0.3540        36.3998        0.3640
f[Q_isv;KA_isv]             0.0100         -0.0011         1.1127        0.0111
f[Q_isv;CL_isv]             0.0100         -0.0013         1.1291        0.0113
f[Q_isv;V1_isv]             0.0100          0.0110         0.0974        0.0010
f[Q_isv]                    0.0500          0.0040         0.9198        0.0460
f[Q_isv;V2_isv]             0.0100          0.0023         0.7675        0.0077
f[V2_isv;KA_isv]            0.0100         -0.0925        10.2460        0.1025
f[V2_isv;CL_isv]            0.0100         -0.0482         5.8199        0.0582
f[V2_isv;V1_isv]            0.0100         -0.3540        36.3998        0.3640
f[V2_isv;Q_isv]             0.0100          0.0023         0.7675        0.0077
f[V2_isv]                   0.0500          0.7273        13.5452        0.6773
================  ================  ==============  =============  ============