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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

0.3156

0.1844

0.3689

f[CL]

1.0000

2.3650

1.3650

1.3650

f[V]

15.0000

19.7234

4.7234

0.3149

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE_STD]

0.2000

0.0984

0.1016

0.5082

f[ANOISE_STD]

0.2000

0.0486

0.1514

0.7569

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[CL_isv]

0.0100

0.3028

0.2928

29.2843

f[CL_iov]

0.0100

0.0029

0.0071

0.7093

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-384.2726

which required 1.29 iterations and took 150.10 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.3156
f[CL] = 2.3650
f[V] = 19.7234
f[PNOISE_STD] = 0.0984
f[ANOISE_STD] = 0.0486
f[CL_isv] = 0.3028
f[CL_iov] = 0.0029

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
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