:orphan: 





.. _tut_example1_fit:



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

[Generated automatically as a Fitting summary]

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


:Name: tut_example1

:Title: Simple Tut Example

:Author: PoPy for PK/PD

: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>`

:Diagram: 


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


Comparison
**********



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


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

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


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

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


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

Individual simulated (sim) plots
================================



.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000003.svg
    :width: 200px


Alternatively see :ref:`tut_example1_simulated_sim_plots`

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


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

    * - .. thumbnail:: images/fit_sim_grph_outputs/allOBS(DV_CENTRAL)_vs_IPRED(CEN)/pop_scatter.svg
            :width: 200px
      - allOBS(DV_CENTRAL)_vs_IPRED(CEN)
    * - .. thumbnail:: images/fit_sim_grph_outputs/allOBS(DV_CENTRAL)_vs_PPRED(CEN)/pop_scatter.svg
            :width: 200px
      - allOBS(DV_CENTRAL)_vs_PPRED(CEN)
    * - .. thumbnail:: images/fit_sim_grph_outputs/allOBS_vs_TIME/comb_spag.svg
            :width: 200px
      - allOBS_vs_TIME
    * - .. thumbnail:: images/fit_sim_grph_outputs/CWRES(DV_CENTRAL)_wrt_IPRED(CEN)/pop_scatter.svg
            :width: 200px
      - CWRES(DV_CENTRAL)_wrt_IPRED(CEN)
    * - .. thumbnail:: images/fit_sim_grph_outputs/RES(DV_CENTRAL)_wrt_IPRED(CEN)/pop_scatter.svg
            :width: 200px
      - RES(DV_CENTRAL)_wrt_IPRED(CEN)
    * - .. thumbnail:: images/fit_sim_grph_outputs/WRES(DV_CENTRAL)_wrt_IPRED(CEN)/pop_scatter.svg
            :width: 200px
      - WRES(DV_CENTRAL)_wrt_IPRED(CEN)
    * - .. thumbnail:: images/fit_sim_grph_outputs/WRES(DV_CENTRAL)_wrt_PPRED(CEN)/pop_scatter.svg
            :width: 200px
      - WRES(DV_CENTRAL)_wrt_PPRED(CEN)

Outputs
*******



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

.. code-block:: pyml

    -48.4038


which required 1.11 iterations and took 34.89 seconds

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


.. 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/solN/fx_params.csv>`

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <tut_example1_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[KE] = 0.0500
    f[PNOISE] = 0.1000
    f[KE_isv] = 0.1000

