:orphan: 





.. _d1cmp_cl_isv_naive_fit:



One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0
###############################################################################

[Generated automatically as a Fitting summary]

Inputs
******



Description
===========

:Name: d1cmp_cl_isv_naive

:Title: One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0

:Author: Wright Dose Ltd

:Abstract: 

| Population one Compartment Model with Absorption and Inter-subject Variance
| Here f[CL_isv] is not estimated it is set to zero.

:Keywords: one compartment model; dep_one_cmp_cl

:Input Script: :download:`d1cmp_cl_isv_naive_fit.pyml <d1cmp_cl_isv_naive_fit.pyml>`

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

:Diagram: 


.. thumbnail:: d1cmp_cl_isv_naive_fit.pyml_output/fit/compartment_diagram.svg
    :width: 200px


Initial fixed effect estimates
==============================

.. code-block:: pyml

    f[KA] = 0.5000
    f[CL] = 1.0000
    f[V] = 15.0000
    f[PNOISE_STD] = 0.2000
    f[ANOISE_STD] = 0.2000
    f[CL_isv] = 0.0000



Outputs
*******



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

.. code-block:: pyml

    -200.9398


which required 1.21 iterations and took 176.17 seconds

Final fitted fixed effects
==========================

.. code-block:: pyml

    f[KA] = 0.2087
    f[CL] = 3.0963
    f[V] = 14.8138
    f[PNOISE_STD] = 0.3766
    f[ANOISE_STD] = 0.1619
    f[CL_isv] = 0.0000



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


:Fixed Effects: :download:`fx_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/fit/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/fit/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/fit/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/fit/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/fit/solN/px_params.csv>`



Plots
*****



Dense sim plots
===============



.. thumbnail:: images/fit_dense/000001.svg
    :width: 200px


.. thumbnail:: images/fit_dense/000002.svg
    :width: 200px


.. thumbnail:: images/fit_dense/000003.svg
    :width: 200px


Alternatively see :ref:`d1cmp_cl_isv_naive_dense_sim_plots`

Comparison
**********



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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[KA]                      0.5000          0.2087         0.5826        0.2913
f[CL]                      1.0000          3.0963         2.0963        2.0963
f[V]                      15.0000         14.8138         0.0124        0.1862
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE_STD]              0.2000          0.3766         0.8832        0.1766
f[ANOISE_STD]              0.2000          0.1619         0.1907        0.0381
===============  ================  ==============  =============  ============

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


