:orphan: 





.. _pa_gen_pa_fit_tut:



Model containing both proportional and additive error
#####################################################

[Generated automatically as a Tutorial summary]

Inputs
******



Description
===========

:Name: pa_gen_pa_fit

:Title: Model containing both proportional and additive error

:Author: Wright Dose Ltd

:Abstract: 

| One compartment model with a depot leading to a central compartment.
| This model contains both proportional and additive error.

:Keywords: one compartment model; one_two_cmp_cl; proportional and additive error

:Input Script: :download:`pa_tut.pyml <pa_tut.pyml>`

:Diagram: 


.. thumbnail:: ./compartment_diagram.svg
    :width: 200px


True f[X] values
================

.. code-block:: pyml

    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500



Starting f[X] values
====================

.. code-block:: pyml

    f[PNOISE_STD] = 0.5000
    f[ANOISE_STD] = 0.2500



Outputs
*******



Generating and Fitting Summaries
================================

* Gen: :ref:`pa_gen_pa_fit_gen` (gen)
* Fit: :ref:`pa_gen_pa_fit_fit` (fit)

Fitted f[X] values
==================

.. code-block:: pyml

    f[PNOISE_STD] = 0.0950
    f[ANOISE_STD] = 0.0453



Plots
*****



Dense comp plots
================



.. thumbnail:: images/comp_dense/000001.svg
    :width: 200px


Alternatively see :ref:`pa_gen_pa_fit_dense_comp_plots`

Comparison
**********



True objective value
====================


.. code-block:: pyml

    -395.5169



Final fitted objective value
============================


.. code-block:: pyml

    -396.6595



Compare Main f[X]
=================


No Main f[X] values to compare.

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



.. csv-table:: 
    :file: fx_comp_noise.csv
    :header-rows: 1


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


No Variance f[X] values to compare.