One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0

[Generated automatically as a Fitting summary]

Model Description

Name:

d1cmp_cl_iov_naive

Title:

One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0

Author:

PoPy for PK/PD

Abstract:

Population one Compartment Model with Absorption and Inter-occasion Variance

Here f[CL_iov] is not estimated it is set to zero.

Keywords:

one compartment model; dep_one_cmp_cl; iov

Input Script:

d1cmp_cl_iov_naive_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA]	0.5000	0.2913	0.2087	0.4174
f[CL]	1.0000	2.4780	1.4780	1.4780
f[V]	15.0000	22.5113	7.5113	0.5008

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE_STD]	0.2000	0.4125	0.2125	1.0625
f[ANOISE_STD]	0.2000	0.0709	0.1291	0.6456

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL_isv]	0.0100	0.1414	0.1314	13.1363

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-203.5525

which required 1.19 iterations and took 49.52 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2913
f[CL] = 2.4780
f[V] = 22.5113
f[PNOISE_STD] = 0.4125
f[ANOISE_STD] = 0.0709
f[CL_isv] = 0.1414
f[CL_iov] = 0.0000

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] = 0.0000