Depot One Comp PK with BLQ observations set to 0.5*LLQ¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | blq_pk_norm_fit_half |
|---|---|
| Title: | Depot One Comp PK with BLQ observations set to 0.5*LLQ |
| Author: | PoPy for PK/PD |
| Abstract: |
Depot One Comp PK model, with BLQ (below level of quantification)
observations set to 0.5*LLQ (lower limit of quantification).
| Keywords: | tutorial; pk; advan4; dep_two_cmp; blq |
|---|---|
| Input Script: | blq_pk_norm_fit_half.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA] | 1.0000 | 0.8242 | 0.1758 | 0.1758 |
| f[CL] | 1.0000 | 1.6865 | 0.6865 | 0.6865 |
| f[V1] | 20.0000 | 81.8435 | 61.8435 | 3.0922 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[PNOISE] | 0.1000 | 0.3395 | 0.2395 | 2.3951 |
Compare Variance f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA_isv] | 0.0500 | 0.0000 | 0.0500 | 0.9999 |
| f[KA_isv;CL_isv] | 0.0100 | 0.0002 | 0.0098 | 0.9793 |
| f[KA_isv;V1_isv] | 0.0100 | 0.0005 | 0.0095 | 0.9518 |
| f[CL_isv;KA_isv] | 0.0100 | 0.0002 | 0.0098 | 0.9793 |
| f[CL_isv] | 0.0500 | 0.0084 | 0.0416 | 0.8315 |
| f[CL_isv;V1_isv] | 0.0100 | 0.0196 | 0.0096 | 0.9598 |
| f[V1_isv;KA_isv] | 0.0100 | 0.0005 | 0.0095 | 0.9518 |
| f[V1_isv;CL_isv] | 0.0100 | 0.0196 | 0.0096 | 0.9598 |
| f[V1_isv] | 0.0500 | 0.0456 | 0.0044 | 0.0877 |
Population simulated (sim) plots¶
(No population graphs were requested.)
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 0.8242
f[CL] = 1.6865
f[V1] = 81.8435
f[KA_isv,CL_isv,V1_isv] = [
[ 0.0000, 0.0002, 0.0005 ],
[ 0.0002, 0.0084, 0.0196 ],
[ 0.0005, 0.0196, 0.0456 ],
]
f[PNOISE] = 0.3395
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