:orphan: 





.. _blq_pk_fit:



Depot + One compartment PK with BLQ
###################################

[Generated automatically as a Fitting summary]

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


:Name: blq_pk

:Title: Depot + One compartment PK with BLQ

:Author: PoPy for PK/PD

:Abstract: 

| Depot One Comp PK model, with BLQ (below level of quantification) observations.

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

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

:Diagram: 


.. thumbnail:: blq_pk_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          0.1991        0.8009         0.8009
f[CL]                      1.0000          1.9475        0.9475         0.9475
f[V1]                     20.0000         48.7184       28.7184         1.4359
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.1000          0.1489        0.0489         0.4888
===============  ================  ==============  ============  =============

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


================  ================  ==============  ============  =============
Variable Name       Starting Value    Fitted Value    Abs Change    Prop Change
================  ================  ==============  ============  =============
f[KA_isv]                   0.0500          0.0681        0.0181         0.3615
f[KA_isv;CL_isv]            0.0100          0.0330        0.0230         2.2991
f[KA_isv;V1_isv]            0.0100         -0.0008        0.0108         1.0844
f[CL_isv;KA_isv]            0.0100          0.0330        0.0230         2.2991
f[CL_isv]                   0.0500          0.0385        0.0115         0.2291
f[CL_isv;V1_isv]            0.0100          0.0310        0.0210         2.1029
f[V1_isv;KA_isv]            0.0100         -0.0008        0.0108         1.0844
f[V1_isv;CL_isv]            0.0100          0.0310        0.0210         2.1029
f[V1_isv]                   0.0500          0.1029        0.0529         1.0581
================  ================  ==============  ============  =============

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_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    -767.6650


which required 1.13 iterations and took 135.24 seconds

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


.. code-block:: pyml

    f[KA] = 0.1991
    f[CL] = 1.9475
    f[V1] = 48.7184
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0681, 0.0330, -0.0008 ],
        [ 0.0330, 0.0385, 0.0310 ],
        [ -0.0008, 0.0310, 0.1029 ],
    ]
    f[PNOISE] = 0.1489
    f[ANOISE] = 0.0100



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <blq_pk_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[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

