:orphan: 





.. _d1cmp_cl_iov_naive_fit:



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

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: d1cmp_cl_iov_naive

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

:Author: Wright Dose Ltd

:Abstract: 

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

:Keywords: one compartment model; dep_one_cmp_cl; iov

:Input Script: :download:`d1cmp_cl_iov_naive_fit.pyml <d1cmp_cl_iov_naive_fit.pyml>`

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

:Diagram: 


.. thumbnail:: d1cmp_cl_iov_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] = 0.0100
    f[CL_iov] = 0.0000



Outputs
*******



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

.. code-block:: pyml

    -253.7337


which required 1.17 iterations and took 580.87 seconds

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

.. code-block:: pyml

    f[KA] = 0.3201
    f[CL] = 2.5351
    f[V] = 20.9743
    f[PNOISE_STD] = 0.2297
    f[ANOISE_STD] = 0.0976
    f[CL_isv] = 0.1395
    f[CL_iov] = 0.0000



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


:Fixed Effects: :download:`fx_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/fit/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/fit/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/fit/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/fit/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <d1cmp_cl_iov_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_iov_naive_dense_sim_plots`

Comparison
**********



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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[KA]                      0.5000          0.3201         0.3597        0.1799
f[CL]                      1.0000          2.5351         1.5351        1.5351
f[V]                      15.0000         20.9743         0.3983        5.9743
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE_STD]              0.2000          0.2297         0.1485        0.0297
f[ANOISE_STD]              0.2000          0.0976         0.5122        0.1024
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[CL_isv]                  0.0100          0.1395        12.9534        0.1295
===============  ================  ==============  =============  ============