One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2

[Generated automatically as a Fitting summary]

Model Description

Name:

d1cmp_cl_iov

Title:

One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2

Author:

PoPy for PK/PD

Abstract:

Population one Compartment Model with Absorption and Inter-occasion Variance

Keywords:

one compartment model; dep_one_cmp_cl; iov

Input Script:

d1cmp_cl_iov_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA]	0.5000	0.3345	0.1655	0.3309
f[CL]	1.0000	2.5855	1.5855	1.5855
f[V]	15.0000	20.2051	5.2051	0.3470

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE_STD]	0.2000	0.1000	0.1000	0.4998
f[ANOISE_STD]	0.2000	0.0479	0.1521	0.7605

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL_isv]	0.0100	0.1200	0.1100	10.9960
f[CL_iov]	0.0100	0.0783	0.0683	6.8312

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-363.8118

which required 1.19 iterations and took 46.47 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.3345
f[CL] = 2.5855
f[V] = 20.2051
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0479
f[CL_isv] = 0.1200
f[CL_iov] = 0.0783

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:

cx_obs_params.csv

Starting f[X] values (before fitting)

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] = 0.0100