: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

    -887.8029


which required 1.30 iterations and took 343.34 seconds

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

.. code-block:: pyml

    f[KA] = 0.1209
    f[CL] = 1.5555
    f[V1] = 33.9425
    f[Q] = 2.2223
    f[V2] = 119.5563
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.0564, 0.0202, 0.0672, -0.0035, 0.0082 ],
        [ 0.0202, 0.1257, 0.1163, -0.0664, -0.2501 ],
        [ 0.0672, 0.1163, 0.2004, -0.0317, -0.1448 ],
        [ -0.0035, -0.0664, -0.0317, 0.0584, 0.2049 ],
        [ 0.0082, -0.2501, -0.1448, 0.2049, 0.7798 ],
    ]
    f[PNOISE] = 0.1480



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          0.1209         0.8791        0.8791
f[CL]                      1.0000          1.5555         0.5555        0.5555
f[V1]                     20.0000         33.9425         0.6971       13.9425
f[Q]                       0.5000          2.2223         3.4447        1.7223
f[V2]                    100.0000        119.5563         0.1956       19.5563
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.1480         0.4795        0.0480
===============  ================  ==============  =============  ============

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


================  ================  ==============  =============  ============
Variable Name       Starting Value    Fitted Value    Prop Change    Abs Change
================  ================  ==============  =============  ============
f[KA_isv]                   0.0500          0.0564         0.1273        0.0064
f[KA_isv;CL_isv]            0.0100          0.0202         1.0206        0.0102
f[KA_isv;V1_isv]            0.0100          0.0672         5.7204        0.0572
f[KA_isv;Q_isv]             0.0100         -0.0035         1.3471        0.0135
f[KA_isv;V2_isv]            0.0100          0.0082         0.1848        0.0018
f[CL_isv;KA_isv]            0.0100          0.0202         1.0206        0.0102
f[CL_isv]                   0.0500          0.1257         1.5144        0.0757
f[CL_isv;V1_isv]            0.0100          0.1163        10.6261        0.1063
f[CL_isv;Q_isv]             0.0100         -0.0664         7.6386        0.0764
f[CL_isv;V2_isv]            0.0100         -0.2501        26.0114        0.2601
f[V1_isv;KA_isv]            0.0100          0.0672         5.7204        0.0572
f[V1_isv;CL_isv]            0.0100          0.1163        10.6261        0.1063
f[V1_isv]                   0.0500          0.2004         3.0082        0.1504
f[V1_isv;Q_isv]             0.0100         -0.0317         4.1725        0.0417
f[V1_isv;V2_isv]            0.0100         -0.1448        15.4750        0.1548
f[Q_isv;KA_isv]             0.0100         -0.0035         1.3471        0.0135
f[Q_isv;CL_isv]             0.0100         -0.0664         7.6386        0.0764
f[Q_isv;V1_isv]             0.0100         -0.0317         4.1725        0.0417
f[Q_isv]                    0.0500          0.0584         0.1686        0.0084
f[Q_isv;V2_isv]             0.0100          0.2049        19.4901        0.1949
f[V2_isv;KA_isv]            0.0100          0.0082         0.1848        0.0018
f[V2_isv;CL_isv]            0.0100         -0.2501        26.0114        0.2601
f[V2_isv;V1_isv]            0.0100         -0.1448        15.4750        0.1548
f[V2_isv;Q_isv]             0.0100          0.2049        19.4901        0.1949
f[V2_isv]                   0.0500          0.7798        14.5952        0.7298
================  ================  ==============  =============  ============