Depot One Comp PK ignoring BLQ observations.¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | blq_pk_norm_fit_ignore |
|---|---|
| Title: | Depot One Comp PK ignoring BLQ observations. |
| Author: | PoPy for PK/PD |
| Abstract: |
Depot One Comp PK model, with BLQ (below level of quantification)
observations removed from data set.
| Keywords: | tutorial; pk; advan4; dep_two_cmp; blq |
|---|---|
| Input Script: | blq_pk_norm_fit_ignore.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA] | 1.0000 | 0.2317 | 0.7683 | 0.7683 |
| f[CL] | 1.0000 | 1.8509 | 0.8509 | 0.8509 |
| f[V1] | 20.0000 | 52.4457 | 32.4457 | 1.6223 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[PNOISE] | 0.1000 | 0.1498 | 0.0498 | 0.4981 |
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 | 0.9824 |
| f[KA_isv;V1_isv] | 0.0100 | 0.0004 | 0.0096 | 0.9638 |
| f[CL_isv;KA_isv] | 0.0100 | 0.0002 | 0.0098 | 0.9824 |
| f[CL_isv] | 0.0500 | 0.0393 | 0.0107 | 0.2150 |
| f[CL_isv;V1_isv] | 0.0100 | 0.0064 | 0.0036 | 0.3620 |
| f[V1_isv;KA_isv] | 0.0100 | 0.0004 | 0.0096 | 0.9638 |
| f[V1_isv;CL_isv] | 0.0100 | 0.0064 | 0.0036 | 0.3620 |
| f[V1_isv] | 0.0500 | 0.0990 | 0.0490 | 0.9793 |
Population simulated (sim) plots¶
(No population graphs were requested.)
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 0.2317
f[CL] = 1.8509
f[V1] = 52.4457
f[KA_isv,CL_isv,V1_isv] = [
[ 0.0000, 0.0002, 0.0004 ],
[ 0.0002, 0.0393, 0.0064 ],
[ 0.0004, 0.0064, 0.0990 ],
]
f[PNOISE] = 0.1498
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