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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

0.2711

0.2289

0.4577

f[CL]

1.0000

2.4008

1.4008

1.4008

f[V]

15.0000

18.3926

3.3926

0.2262

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE_STD]

0.2000

0.0935

0.1065

0.5325

f[ANOISE_STD]

0.2000

0.0487

0.1513

0.7566

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[CL_isv]

0.0100

0.0829

0.0729

7.2858

f[CL_iov]

0.0100

0.1073

0.0973

9.7314

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-353.3355

which required 1.26 iterations and took 541.14 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2711
f[CL] = 2.4008
f[V] = 18.3926
f[PNOISE_STD] = 0.0935
f[ANOISE_STD] = 0.0487
f[CL_isv] = 0.0829
f[CL_iov] = 0.1073

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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