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	0.2157	0.7843	0.7843
f[CL]	1.0000	1.8093	0.8093	0.8093
f[V1]	20.0000	51.2961	31.2961	1.5648

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.1000	0.1482	0.0482	0.4822

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA_isv]	0.0500	0.0000	0.0500	0.9998
f[KA_isv;CL_isv]	0.0100	0.0001	0.0099	0.9946
f[KA_isv;V1_isv]	0.0100	0.0003	0.0097	0.9681
f[CL_isv;KA_isv]	0.0100	0.0001	0.0099	0.9946
f[CL_isv]	0.0500	0.0516	0.0016	0.0321
f[CL_isv;V1_isv]	0.0100	-0.0033	0.0133	1.3329
f[V1_isv;KA_isv]	0.0100	0.0003	0.0097	0.9681
f[V1_isv;CL_isv]	0.0100	-0.0033	0.0133	1.3329
f[V1_isv]	0.0500	0.1236	0.0736	1.4715

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-850.5084

which required 1.30 iterations and took 50.79 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2157
f[CL] = 1.8093
f[V1] = 51.2961
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0000, 0.0001, 0.0003 ],
    [ 0.0001, 0.0516, -0.0033 ],
    [ 0.0003, -0.0033, 0.1236 ],
]
f[PNOISE] = 0.1482
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