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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 0.3179 0.1821 0.3641
f[CL] 1.0000 3.3331 2.3331 2.3331
f[V] 15.0000 19.3470 4.3470 0.2898

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE_STD] 0.2000 0.0946 0.1054 0.5271
f[ANOISE_STD] 0.2000 0.0495 0.1505 0.7524

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[CL_isv] 0.0100 0.1464 0.1364 13.6369
f[CL_iov] 0.0100 0.0608 0.0508 5.0802

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-398.8099

which required 1.19 iterations and took 437.13 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.3179
f[CL] = 3.3331
f[V] = 19.3470
f[PNOISE_STD] = 0.0946
f[ANOISE_STD] = 0.0495
f[CL_isv] = 0.1464
f[CL_iov] = 0.0608

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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