:orphan: 





.. _blq_pk_norm_fit_fit:



Depot One Comp PK with BLQ observations set to LLQ
##################################################

[Generated automatically as a Fitting summary]

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


:Name: blq_pk_norm_fit

:Title: Depot One Comp PK with BLQ observations set to LLQ

:Author: PoPy for PK/PD

:Abstract: 

| Depot One Comp PK model, with BLQ (below level of quantification)
| observations set to LLQ (lower limit of quantification).

:Keywords: tutorial; pk; advan4; dep_two_cmp; blq

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

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[KA]                      1.0000          5.8108        4.8108         4.8108
f[CL]                      1.0000          0.9490        0.0510         0.0510
f[V1]                     20.0000         88.6022       68.6022         3.4301
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.1000          0.2207        0.1207         1.2071
===============  ================  ==============  ============  =============

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


================  ================  ==============  ============  =============
Variable Name       Starting Value    Fitted Value    Abs Change    Prop Change
================  ================  ==============  ============  =============
f[KA_isv]                   0.0500          1.3304        1.2804        25.6080
f[KA_isv;CL_isv]            0.0100         -0.0176        0.0276         2.7570
f[KA_isv;V1_isv]            0.0100          0.2359        0.2259        22.5922
f[CL_isv;KA_isv]            0.0100         -0.0176        0.0276         2.7570
f[CL_isv]                   0.0500          0.0002        0.0498         0.9953
f[CL_isv;V1_isv]            0.0100         -0.0031        0.0131         1.3114
f[V1_isv;KA_isv]            0.0100          0.2359        0.2259        22.5922
f[V1_isv;CL_isv]            0.0100         -0.0031        0.0131         1.3114
f[V1_isv]                   0.0500          0.0444        0.0056         0.1124
================  ================  ==============  ============  =============

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:`blq_pk_norm_fit_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    121897.7219


which required 1.30 iterations and took 74.11 seconds

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


.. code-block:: pyml

    f[KA] = 5.8108
    f[CL] = 0.9490
    f[V1] = 88.6022
    f[KA_isv,CL_isv,V1_isv] = [
        [ 1.3304, -0.0176, 0.2359 ],
        [ -0.0176, 0.0002, -0.0031 ],
        [ 0.2359, -0.0031, 0.0444 ],
    ]
    f[PNOISE] = 0.2207
    f[ANOISE] = 0.0100



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <blq_pk_norm_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


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


Starting f[X] values (before fitting)
=====================================


.. code-block:: pyml

    f[KA] = 1.0000
    f[CL] = 1.0000
    f[V1] = 20.0000
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0500, 0.0100, 0.0100 ],
        [ 0.0100, 0.0500, 0.0100 ],
        [ 0.0100, 0.0100, 0.0500 ],
    ]
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0100

