:orphan: 





.. _pa_gen_ao_fit_fit:



Additive error model fitted to proportional + additive noise synthetic data.
############################################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: pa_gen_ao_fit

:Title: Additive error model fitted to proportional + additive noise synthetic data.

:Author: Wright Dose Ltd

:Abstract: 

| One compartment model with a depot leading to a central compartment.
| This model contains both additive error only. Synthetic input data contain proportional and additive noise.

:Keywords: one compartment model; dep_one_cmp_cl; additive error

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

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

:Diagram: 


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


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

.. code-block:: pyml

    f[ANOISE_STD] = 0.2500



Outputs
*******



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

.. code-block:: pyml

    -327.7084


which required N. iterations and took 0.39 seconds

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

.. code-block:: pyml

    f[ANOISE_STD] = 0.1179



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


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

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

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

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

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



Plots
*****



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



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


Alternatively see :ref:`pa_gen_ao_fit_dense_sim_plots`

Comparison
**********



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




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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[ANOISE_STD]            0.1179            0.2500         0.5283        0.1321
===============  ==============  ================  =============  ============

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


