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First order absorption model with peripheral compartment

[Generated automatically as a Fitting summary]

Inputs

Description

Name:builtin_fit_example
Title:First order absorption model with peripheral compartment
Author:J.R. Hartley
Abstract:
A two compartment PK model with bolus dose and
first order absorption, similar to a Nonmem advan4trans4 model.
Keywords:fitting; pk; advan4; dep_two_cmp; first order
Input Script:builtin_fit_example.pyml
Input Data:builtin_fit_example_data.csv
Diagram:

Initial fixed effect estimates

f[KA] = 1
f[CL] = 1
f[V1] = 20
f[Q] = 0.5
f[V2] = 100
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.05, 0.01, 0.01, 0.01, 0.01 ],
    [ 0.01, 0.05, 0.01, 0.01, 0.01 ],
    [ 0.01, 0.01, 0.05, 0.01, 0.01 ],
    [ 0.01, 0.01, 0.01, 0.05, 0.01 ],
    [ 0.01, 0.01, 0.01, 0.01, 0.05 ]
]
f[PNOISE] = 0.1

Outputs

Final objective value

-896.875222683

which required 33 iterations and took 208.05 seconds

Final fitted fixed effects

f[KA] = 0.22502
f[CL] = 2.0883
f[V1] = 54.663
f[Q] = 0.94563
f[V2] = 105.35
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.14689, 0.013803, -0.056237, 0.101, -0.011802 ],
    [ 0.013803, 0.033066, 0.0062645, -0.0050783, 0.00014454 ],
    [ -0.056237, 0.0062645, 0.043295, -0.047294, 0.014371 ],
    [ 0.101, -0.0050783, -0.047294, 0.23317, -0.033465 ],
    [ -0.011802, 0.00014454, 0.014371, -0.033465, 0.05129 ]
]
f[PNOISE] = 0.14293

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)

Plots

Dense sim plots

Alternatively see All dense_sim graph plots

Comparison

Compare Main f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA] 0.225015 1 0.774985 0.774985
f[CL] 2.08828 1 1.08828 1.08828
f[V1] 54.6634 20 1.73317 34.6634
f[Q] 0.945631 0.5 0.891262 0.445631
f[V2] 105.351 100 0.0535103 5.35103

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE] 0.14293 0.1 0.429302 0.0429302

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA_isv] 0.146886 0.05 1.93773 0.0968864
f[KA_isv;CL_isv] 0.0138028 0.01 0.380285 0.00380285
f[KA_isv;V1_isv] -0.056237 0.01 6.6237 0.066237
f[KA_isv;Q_isv] 0.100996 0.01 9.09964 0.0909964
f[KA_isv;V2_isv] -0.0118023 0.01 2.18023 0.0218023
f[CL_isv;KA_isv] 0.0138028 0.01 0.380285 0.00380285
f[CL_isv] 0.0330657 0.05 0.338686 0.0169343
f[CL_isv;V1_isv] 0.00626453 0.01 0.373547 0.00373547
f[CL_isv;Q_isv] -0.00507828 0.01 1.50783 0.0150783
f[CL_isv;V2_isv] 0.000144541 0.01 0.985546 0.00985546
f[V1_isv;KA_isv] -0.056237 0.01 6.6237 0.066237
f[V1_isv;CL_isv] 0.00626453 0.01 0.373547 0.00373547
f[V1_isv] 0.0432946 0.05 0.134108 0.00670542
f[V1_isv;Q_isv] -0.047294 0.01 5.7294 0.057294
f[V1_isv;V2_isv] 0.0143706 0.01 0.437063 0.00437063
f[Q_isv;KA_isv] 0.100996 0.01 9.09964 0.0909964
f[Q_isv;CL_isv] -0.00507828 0.01 1.50783 0.0150783
f[Q_isv;V1_isv] -0.047294 0.01 5.7294 0.057294
f[Q_isv] 0.233175 0.05 3.6635 0.183175
f[Q_isv;V2_isv] -0.033465 0.01 4.3465 0.043465
f[V2_isv;KA_isv] -0.0118023 0.01 2.18023 0.0218023
f[V2_isv;CL_isv] 0.000144541 0.01 0.985546 0.00985546
f[V2_isv;V1_isv] 0.0143706 0.01 0.437063 0.00437063
f[V2_isv;Q_isv] -0.033465 0.01 4.3465 0.043465
f[V2_isv] 0.05129 0.05 0.0258006 0.00129003
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