:orphan: 





.. _exp_error_fit:



Model containing exponential error
##################################

[Generated automatically as a Fitting summary]

Model Description
*****************


:Name: exp_error

:Title: Model containing exponential error

:Author: PoPy for PK/PD

:Abstract: 

| One compartment model with a depot leading to a central compartment.
| This model contains exponential error.

:Keywords: one compartment model; exponential error

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

:Diagram: 


.. thumbnail:: exp_error_fit.pyml_output/compartment_diagram.svg
    :width: 200px


Comparison
**********



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




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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[EXP_NOISE]               0.2000          0.0935        0.1065         0.5325
===============  ================  ==============  ============  =============

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




Population simulated (sim) plots
================================


.. list-table:: 
    :width: 90%

    * - .. thumbnail:: images/fit_sim_grph_outputs/allOBS_vs_TIME/comb_spag.svg
            :width: 200px
      - allOBS_vs_TIME
    * - .. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
            :width: 200px
      - indOBS_vs_TIME

Outputs
*******



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

.. code-block:: pyml

    37.7135


which required 1.5 iterations and took 10.30 seconds

Fitted f[X] values (after fitting)
==================================


.. code-block:: pyml

    f[EXP_NOISE] = 0.0935



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <exp_error_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


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


Starting f[X] values (before fitting)
=====================================


.. code-block:: pyml

    f[EXP_NOISE] = 0.2000

