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Depot One Comp PK with BLQ observations set to LLQ

[Generated automatically as a Fitting summary]

Model Description

Name:blq_pk_norm_fit
Title:Depot One Comp PK with BLQ observations set to LLQ
Author:PoPy for PK/PD
Abstract:
Depot One Comp PK model, with BLQ (below level of quantification)
observations set to LLQ (lower limit of quantification).
Keywords:tutorial; pk; advan4; dep_two_cmp; blq
Input Script:blq_pk_norm_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA] 1.0000 1.6306 0.6306 0.6306
f[CL] 1.0000 0.9667 0.0333 0.0333
f[V1] 20.0000 86.8189 66.8189 3.3409

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE] 0.1000 0.2353 0.1353 1.3530

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA_isv] 0.0500 0.1541 0.1041 2.0829
f[KA_isv;CL_isv] 0.0100 -0.0011 0.0111 1.1142
f[KA_isv;V1_isv] 0.0100 -0.0022 0.0122 1.2157
f[CL_isv;KA_isv] 0.0100 -0.0011 0.0111 1.1142
f[CL_isv] 0.0500 0.0000 0.0500 0.9997
f[CL_isv;V1_isv] 0.0100 -0.0004 0.0104 1.0440
f[V1_isv;KA_isv] 0.0100 -0.0022 0.0122 1.2157
f[V1_isv;CL_isv] 0.0100 -0.0004 0.0104 1.0440
f[V1_isv] 0.0500 0.0428 0.0072 0.1432

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

122017.3925

which required 1.30 iterations and took 491.59 seconds

Fitted f[X] values (after fitting)

f[KA] = 1.6306
f[CL] = 0.9667
f[V1] = 86.8189
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.1541, -0.0011, -0.0022 ],
    [ -0.0011, 0.0000, -0.0004 ],
    [ -0.0022, -0.0004, 0.0428 ],
]
f[PNOISE] = 0.2353
f[ANOISE] = 0.0100

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:synthetic_data.csv

Starting f[X] values (before fitting)

f[KA] = 1.0000
f[CL] = 1.0000
f[V1] = 20.0000
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0500, 0.0100, 0.0100 ],
    [ 0.0100, 0.0500, 0.0100 ],
    [ 0.0100, 0.0100, 0.0500 ],
]
f[PNOISE] = 0.1000
f[ANOISE] = 0.0100
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