Depot One Comp PK ignoring BLQ observations.

[Generated automatically as a Fitting summary]

Model Description

Name:: blq_pk_norm_fit_ignore
Title:: Depot One Comp PK ignoring BLQ observations.
Author:: PoPy for PK/PD
Abstract:

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

observations removed from data set.

Keywords:: tutorial; pk; advan4; dep_two_cmp; blq
Input Script:: blq_pk_norm_fit_ignore.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA]	1.0000	0.2348	0.7652	0.7652
f[CL]	1.0000	1.8185	0.8185	0.8185
f[V1]	20.0000	54.7515	34.7515	1.7376

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.1000	0.1422	0.0422	0.4223

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA_isv]	0.0500	0.0000	0.0500	0.9997
f[KA_isv;CL_isv]	0.0100	-0.0003	0.0103	1.0329
f[KA_isv;V1_isv]	0.0100	-0.0001	0.0101	1.0083
f[CL_isv;KA_isv]	0.0100	-0.0003	0.0103	1.0329
f[CL_isv]	0.0500	0.0317	0.0183	0.3658
f[CL_isv;V1_isv]	0.0100	-0.0061	0.0161	1.6135
f[V1_isv;KA_isv]	0.0100	-0.0001	0.0101	1.0083
f[V1_isv;CL_isv]	0.0100	-0.0061	0.0161	1.6135
f[V1_isv]	0.0500	0.1028	0.0528	1.0553

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-889.8945

which required 1.30 iterations and took 52.27 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2348
f[CL] = 1.8185
f[V1] = 54.7515
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0000, -0.0003, -0.0001 ],
    [ -0.0003, 0.0317, -0.0061 ],
    [ -0.0001, -0.0061, 0.1028 ],
]
f[PNOISE] = 0.1422
f[ANOISE] = 0.0100

Fitted parameter .csv files

Fixed Effects:: fx_params.csv (fit)
Random Effects:: rx_params.csv (fit)
Model params:: mx_params.csv (fit)
State values:: sx_params.csv (fit)
Predictions:: px_params.csv (fit)
Likelihoods:: lx_params.csv (fit)

Inputs

Input Data:: synthetic_data.csv

Starting f[X] values (before fitting)

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