:orphan: 





.. _linear_pd_fit:



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

[Generated automatically as a Fitting summary]

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


:Name: linear_pd

:Title: Linear PD model

:Author: PoPy for PK/PD

:Abstract: 

| A simple Linear PD Model.
| Model consists of a baseline which increases linearly with concentration.

:Keywords: pd; linear; one compartment model

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

:Diagram: 


.. thumbnail:: linear_pd_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[BL]                     15.0000          9.9937        5.0063         0.3338
f[SLOPE]                   0.5000          1.0412        0.5412         1.0825
f[ANOISE]                  5.0000          0.4626        4.5374         0.9075
===============  ================  ==============  ============  =============

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




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


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

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

Outputs
*******



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

.. code-block:: pyml

    -54.1617


which required 1.15 iterations and took 10.70 seconds

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


.. code-block:: pyml

    f[BL] = 9.9937
    f[SLOPE] = 1.0412
    f[ANOISE] = 0.4626



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <linear_pd_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[ANOISE] = 5.0000

