:orphan: 





.. _po_gen_po_fit_fit:



Model containing proportional error only, with proportional only data
#####################################################################

[Generated automatically as a Fitting summary]

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


:Name: po_gen_po_fit

:Title: Model containing proportional error only, with proportional only data

:Author: PoPy for PK/PD

:Abstract: 

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

:Keywords: one compartment model; dep_one_cmp_cl; proportional error

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

:Diagram: 


.. thumbnail:: po_gen_po_fit_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[PNOISE_STD]              0.5000          0.0932        0.4068         0.8136
===============  ================  ==============  ============  =============

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/CWRES(DV_CENTRAL)_wrt_IPRED(DV_CENTRAL)/pop_scatter.svg
            :width: 200px
      - CWRES(DV_CENTRAL)_wrt_IPRED(DV_CENTRAL)
    * - .. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
            :width: 200px
      - indOBS_vs_TIME
    * - .. thumbnail:: images/fit_sim_grph_outputs/RES(DV_CENTRAL)_wrt_IPRED(DV_CENTRAL)/pop_scatter.svg
            :width: 200px
      - RES(DV_CENTRAL)_wrt_IPRED(DV_CENTRAL)
    * - .. thumbnail:: images/fit_sim_grph_outputs/WRES(DV_CENTRAL)_wrt_PPRED(DV_CENTRAL)/pop_scatter.svg
            :width: 200px
      - WRES(DV_CENTRAL)_wrt_PPRED(DV_CENTRAL)

Outputs
*******



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

.. code-block:: pyml

    -572.8210


which required 1.7 iterations and took 9.06 seconds

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


.. code-block:: pyml

    f[PNOISE_STD] = 0.0932



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <po_gen_po_fit_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[PNOISE_STD] = 0.5000

