- Language: en
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 |
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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 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) |
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Random Effects: | rx_params.csv (fit) |
Model params: | mx_params.csv (fit) |
State values: | sx_params.csv (fit) |
Predictions: | px_params.csv (fit) |
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 |