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	0.3202	0.1798	0.3596
f[CL]	1.0000	2.5349	1.5349	1.5349
f[V]	15.0000	20.9761	5.9761	0.3984

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE_STD]	0.2000	0.2297	0.0297	0.1484
f[ANOISE_STD]	0.2000	0.0976	0.1024	0.5122

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL_isv]	0.0100	0.1395	0.1295	12.9504

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-253.7337

which required 1.16 iterations and took 37.36 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.3202
f[CL] = 2.5349
f[V] = 20.9761
f[PNOISE_STD] = 0.2297
f[ANOISE_STD] = 0.0976
f[CL_isv] = 0.1395
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