: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

    -887.8461


which required 1.30 iterations and took 451.80 seconds

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

.. code-block:: pyml

    f[KA] = 0.1207
    f[CL] = 1.5708
    f[V1] = 33.8896
    f[Q] = 2.2287
    f[V2] = 114.9338
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.0630, 0.0208, 0.0700, -0.0019, 0.0091 ],
        [ 0.0208, 0.1208, 0.1138, -0.0618, -0.2448 ],
        [ 0.0700, 0.1138, 0.1965, -0.0260, -0.1460 ],
        [ -0.0019, -0.0618, -0.0260, 0.0574, 0.1992 ],
        [ 0.0091, -0.2448, -0.1460, 0.1992, 0.7660 ],
    ]
    f[PNOISE] = 0.1478



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          0.1207         0.8793        0.8793
f[CL]                      1.0000          1.5708         0.5708        0.5708
f[V1]                     20.0000         33.8896         0.6945       13.8896
f[Q]                       0.5000          2.2287         3.4574        1.7287
f[V2]                    100.0000        114.9338         0.1493       14.9338
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.1478         0.4784        0.0478
===============  ================  ==============  =============  ============

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


================  ================  ==============  =============  ============
Variable Name       Starting Value    Fitted Value    Prop Change    Abs Change
================  ================  ==============  =============  ============
f[KA_isv]                   0.0500          0.0630         0.2604        0.0130
f[KA_isv;CL_isv]            0.0100          0.0208         1.0763        0.0108
f[KA_isv;V1_isv]            0.0100          0.0700         6.0041        0.0600
f[KA_isv;Q_isv]             0.0100         -0.0019         1.1930        0.0119
f[KA_isv;V2_isv]            0.0100          0.0091         0.0859        0.0009
f[CL_isv;KA_isv]            0.0100          0.0208         1.0763        0.0108
f[CL_isv]                   0.0500          0.1208         1.4156        0.0708
f[CL_isv;V1_isv]            0.0100          0.1138        10.3827        0.1038
f[CL_isv;Q_isv]             0.0100         -0.0618         7.1801        0.0718
f[CL_isv;V2_isv]            0.0100         -0.2448        25.4805        0.2548
f[V1_isv;KA_isv]            0.0100          0.0700         6.0041        0.0600
f[V1_isv;CL_isv]            0.0100          0.1138        10.3827        0.1038
f[V1_isv]                   0.0500          0.1965         2.9309        0.1465
f[V1_isv;Q_isv]             0.0100         -0.0260         3.6005        0.0360
f[V1_isv;V2_isv]            0.0100         -0.1460        15.5955        0.1560
f[Q_isv;KA_isv]             0.0100         -0.0019         1.1930        0.0119
f[Q_isv;CL_isv]             0.0100         -0.0618         7.1801        0.0718
f[Q_isv;V1_isv]             0.0100         -0.0260         3.6005        0.0360
f[Q_isv]                    0.0500          0.0574         0.1479        0.0074
f[Q_isv;V2_isv]             0.0100          0.1992        18.9217        0.1892
f[V2_isv;KA_isv]            0.0100          0.0091         0.0859        0.0009
f[V2_isv;CL_isv]            0.0100         -0.2448        25.4805        0.2548
f[V2_isv;V1_isv]            0.0100         -0.1460        15.5955        0.1560
f[V2_isv;Q_isv]             0.0100          0.1992        18.9217        0.1892
f[V2_isv]                   0.0500          0.7660        14.3195        0.7160
================  ================  ==============  =============  ============