:orphan: 





.. _ao_gen_po_fit_fit:



Proportional only error model fitted to additive noise only synthetic data.
###########################################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: ao_gen_po_fit

:Title: Proportional only error model fitted to additive noise only synthetic 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 file contains additive noise but no proportional noise.

:Keywords: one compartment model; dep_one_cmp_cl; proportional error

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

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

:Diagram: 


.. thumbnail:: ao_gen_po_fit.pyml_output/fit/compartment_diagram.svg
    :width: 200px


Initial fixed effect estimates
==============================

.. code-block:: pyml

    f[PNOISE_STD] = 0.5000



Outputs
*******



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

.. code-block:: pyml

    -231.2926


which required N. iterations and took 0.41 seconds

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

.. code-block:: pyml

    f[PNOISE_STD] = 0.5142



Fitted parameter .csv files
===========================


:Fixed Effects: :download:`fx_params.csv (fit) <ao_gen_po_fit.pyml_output/fit/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <ao_gen_po_fit.pyml_output/fit/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <ao_gen_po_fit.pyml_output/fit/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <ao_gen_po_fit.pyml_output/fit/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <ao_gen_po_fit.pyml_output/fit/solN/px_params.csv>`



Plots
*****



Dense sim plots
===============



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


Alternatively see :ref:`ao_gen_po_fit_dense_sim_plots`

Comparison
**********



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




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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE_STD]            0.5142            0.5000         0.0283        0.0142
===============  ==============  ================  =============  ============

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


