:orphan: 





.. _tut_example1_fit:



Simple Tut Example
##################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: tut_example1

:Title: Simple Tut Example

:Author: J.R. Hartley

:Abstract: 

| One compartment model with elimination rate constant KE.

:Keywords: one compartment model; iv_one_cmp_k

:Input Script: :download:`tut_example1_fit.pyml <tut_example1_fit.pyml>`

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

:Diagram: 


.. thumbnail:: tut_example1_fit.pyml_output/fit/compartment_diagram.svg
    :width: 200px


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

.. code-block:: pyml

    f[KE] = 0.0500
    f[PNOISE] = 0.1000
    f[KE_isv] = 0.1000



Outputs
*******



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

.. code-block:: pyml

    -48.4038


which required 1.11 iterations and took 80.85 seconds

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

.. code-block:: pyml

    f[KE] = 0.1062
    f[PNOISE] = 0.0450
    f[KE_isv] = 0.0260



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


:Fixed Effects: :download:`fx_params.csv (fit) <tut_example1_fit.pyml_output/fit/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <tut_example1_fit.pyml_output/fit/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <tut_example1_fit.pyml_output/fit/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <tut_example1_fit.pyml_output/fit/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <tut_example1_fit.pyml_output/fit/solN/px_params.csv>`



Plots
*****



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



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


.. thumbnail:: images/fit_dense/000002.svg
    :width: 200px


.. thumbnail:: images/fit_dense/000003.svg
    :width: 200px


Alternatively see :ref:`tut_example1_dense_sim_plots`

Comparison
**********



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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[KE]                      0.0500          0.1062         1.1233        0.0562
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.0450         0.5503        0.0550
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[KE_isv]                  0.1000          0.0260         0.7398        0.0740
===============  ================  ==============  =============  ============