:orphan: 





.. _po_gen_po_fit_tut:



Model containing proportional error only, with proportional only data
#####################################################################

[Generated automatically as a Tutorial summary]

Inputs
******



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

:Name: po_gen_po_fit

:Title: Model containing proportional error only, with proportional only data

:Author: Wright Dose Ltd

:Abstract: 

| One compartment model with a depot leading to a central compartment.
| This model contains proportional error and no additive error. The synthetic input data contains only proportional error too.

:Keywords: one compartment model; dep_one_cmp_cl; proportional error

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

:Diagram: 


.. thumbnail:: compartment_diagram.pdf
    :width: 200px


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

.. code-block:: pyml

    f[PNOISE_STD] = 0.1000



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

.. code-block:: pyml

    f[PNOISE_STD] = 0.5000



Outputs
*******



Generated data .csv file
========================


:Synthetic Data: :download:`synthetic_data.csv <synthetic_data.csv>`


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

* Gen: :ref:`po_gen_po_fit_gen` (gen)
* Fit: :ref:`po_gen_po_fit_fit` (fit)

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

.. code-block:: pyml

    f[PNOISE_STD] = 0.0928



Plots
*****



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



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


Alternatively see :ref:`po_gen_po_fit_dense_comp_plots`

Comparison
**********



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


.. code-block:: pyml

    -572.6714



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


.. code-block:: pyml

    -573.7490



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.