:orphan: 





.. _linear_pd_pop_fit:



Linear PD model
###############

[Generated automatically as a Fitting summary]

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


:Name: linear_pd_pop

:Title: Linear PD model

:Author: PoPy for PK/PD

:Abstract: 

| A simple (i.e. no PD compartments) Linear PD Model.
| Model consists of a baseline which increases linearly with concentration.

:Keywords: pd; linear; one compartment model

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

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[BL]                     15.0000          9.8634        5.1366         0.3424
f[SLOPE]                   0.5000          1.0393        0.5393         1.0786
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.0300          0.0148        0.0152         0.5064
f[ANOISE]                  0.2000          0.4364        0.2364         1.1819
===============  ================  ==============  ============  =============

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


===================  ================  ==============  ============  =============
Variable Name          Starting Value    Fitted Value    Abs Change    Prop Change
===================  ================  ==============  ============  =============
f[BL_isv]                      0.1000          0.0537        0.0463         0.4628
f[BL_isv;SLOPE_isv]            0.0000          0.0062        0.0062       INF
f[SLOPE_isv;BL_isv]            0.0000          0.0062        0.0062       INF
f[SLOPE_isv]                   0.0500          0.0403        0.0097         0.1948
===================  ================  ==============  ============  =============

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:`linear_pd_pop_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    178.3795


which required 1.20 iterations and took 70.72 seconds

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


.. code-block:: pyml

    f[BL] = 9.8634
    f[SLOPE] = 1.0393
    f[PNOISE] = 0.0148
    f[ANOISE] = 0.4364
    f[BL_isv,SLOPE_isv] = [
        [ 0.0537, 0.0062 ],
        [ 0.0062, 0.0403 ],
    ]



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <linear_pd_pop_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[BL] = 15.0000
    f[SLOPE] = 0.5000
    f[PNOISE] = 0.0300
    f[ANOISE] = 0.2000
    f[BL_isv,SLOPE_isv] = [
        [ 0.1000, 0.0000 ],
        [ 0.0000, 0.0500 ],
    ]

