:orphan: 





.. _po_gen_pa_fit_fit:



Proportional and Additive error model fitted to proportional noise only synthetic data.
#######################################################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: po_gen_pa_fit

:Title: Proportional and Additive error model fitted to proportional noise only synthetic data.

:Author: Wright Dose Ltd

:Abstract: 

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

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

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

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

:Diagram: 


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


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

.. code-block:: pyml

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



Outputs
*******



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

.. code-block:: pyml

    -573.5936


which required 1.20 iterations and took 2.15 seconds

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

.. code-block:: pyml

    f[PNOISE_STD] = 0.0922
    f[ANOISE_STD] = 0.0010



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


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

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

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

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

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



Plots
*****



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



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


Alternatively see :ref:`po_gen_pa_fit_dense_sim_plots`

Comparison
**********



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




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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE_STD]              0.5000          0.0922         0.8156        0.4078
f[ANOISE_STD]              0.2500          0.0010         0.9960        0.2490
===============  ================  ==============  =============  ============

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


