One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5

[Generated automatically as a Fitting summary]

Model Description

Name:

d1cmp_cl_iov_05

Title:

One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5

Author:

PoPy for PK/PD

Abstract:

Population one Compartment Model with Absorption and Inter-occasion Variance

Here f[CL_isv] true value is 0.5

Keywords:

one compartment model; dep_one_cmp_cl; iov

Input Script:

d1cmp_cl_iov_05_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA]	0.5000	0.2686	0.2314	0.4628
f[CL]	1.0000	2.0943	1.0943	1.0943
f[V]	15.0000	18.1651	3.1651	0.2110

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE_STD]	0.2000	0.0889	0.1111	0.5553
f[ANOISE_STD]	0.2000	0.0486	0.1514	0.7570

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL_isv]	0.0100	0.2835	0.2735	27.3455
f[CL_iov]	0.0100	0.0105	0.0005	0.0518

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-365.3290

which required 1.22 iterations and took 83.60 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2686
f[CL] = 2.0943
f[V] = 18.1651
f[PNOISE_STD] = 0.0889
f[ANOISE_STD] = 0.0486
f[CL_isv] = 0.2835
f[CL_iov] = 0.0105

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