This page was generated on 2023-01-02 09:00:44 -0500 (Mon, 02 Jan 2023).
R Under development (unstable) (2022-12-25 r83502 ucrt) -- "Unsuffered Consequences"
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> library(testthat)
> library(preciseTAD)
>
> test_check("preciseTAD")
+ Fold1: mtry= 2, ntree=500, nodesize=1
- Fold1: mtry= 2, ntree=500, nodesize=1
+ Fold1: mtry= 5, ntree=500, nodesize=1
- Fold1: mtry= 5, ntree=500, nodesize=1
+ Fold1: mtry= 8, ntree=500, nodesize=1
- Fold1: mtry= 8, ntree=500, nodesize=1
+ Fold1: mtry=10, ntree=500, nodesize=1
- Fold1: mtry=10, ntree=500, nodesize=1
+ Fold1: mtry=13, ntree=500, nodesize=1
- Fold1: mtry=13, ntree=500, nodesize=1
+ Fold1: mtry=16, ntree=500, nodesize=1
- Fold1: mtry=16, ntree=500, nodesize=1
+ Fold1: mtry=18, ntree=500, nodesize=1
- Fold1: mtry=18, ntree=500, nodesize=1
+ Fold1: mtry=21, ntree=500, nodesize=1
- Fold1: mtry=21, ntree=500, nodesize=1
+ Fold1: mtry=24, ntree=500, nodesize=1
- Fold1: mtry=24, ntree=500, nodesize=1
+ Fold1: mtry=26, ntree=500, nodesize=1
- Fold1: mtry=26, ntree=500, nodesize=1
+ Fold2: mtry= 2, ntree=500, nodesize=1
- Fold2: mtry= 2, ntree=500, nodesize=1
+ Fold2: mtry= 5, ntree=500, nodesize=1
- Fold2: mtry= 5, ntree=500, nodesize=1
+ Fold2: mtry= 8, ntree=500, nodesize=1
- Fold2: mtry= 8, ntree=500, nodesize=1
+ Fold2: mtry=10, ntree=500, nodesize=1
- Fold2: mtry=10, ntree=500, nodesize=1
+ Fold2: mtry=13, ntree=500, nodesize=1
- Fold2: mtry=13, ntree=500, nodesize=1
+ Fold2: mtry=16, ntree=500, nodesize=1
- Fold2: mtry=16, ntree=500, nodesize=1
+ Fold2: mtry=18, ntree=500, nodesize=1
- Fold2: mtry=18, ntree=500, nodesize=1
+ Fold2: mtry=21, ntree=500, nodesize=1
- Fold2: mtry=21, ntree=500, nodesize=1
+ Fold2: mtry=24, ntree=500, nodesize=1
- Fold2: mtry=24, ntree=500, nodesize=1
+ Fold2: mtry=26, ntree=500, nodesize=1
- Fold2: mtry=26, ntree=500, nodesize=1
+ Fold3: mtry= 2, ntree=500, nodesize=1
- Fold3: mtry= 2, ntree=500, nodesize=1
+ Fold3: mtry= 5, ntree=500, nodesize=1
- Fold3: mtry= 5, ntree=500, nodesize=1
+ Fold3: mtry= 8, ntree=500, nodesize=1
- Fold3: mtry= 8, ntree=500, nodesize=1
+ Fold3: mtry=10, ntree=500, nodesize=1
- Fold3: mtry=10, ntree=500, nodesize=1
+ Fold3: mtry=13, ntree=500, nodesize=1
- Fold3: mtry=13, ntree=500, nodesize=1
+ Fold3: mtry=16, ntree=500, nodesize=1
- Fold3: mtry=16, ntree=500, nodesize=1
+ Fold3: mtry=18, ntree=500, nodesize=1
- Fold3: mtry=18, ntree=500, nodesize=1
+ Fold3: mtry=21, ntree=500, nodesize=1
- Fold3: mtry=21, ntree=500, nodesize=1
+ Fold3: mtry=24, ntree=500, nodesize=1
- Fold3: mtry=24, ntree=500, nodesize=1
+ Fold3: mtry=26, ntree=500, nodesize=1
- Fold3: mtry=26, ntree=500, nodesize=1
Aggregating results
Selecting tuning parameters
Fitting mtry = 2, ntree = 500, nodesize = 1 on full training set
+(rfe) fit Fold1 size: 26
-(rfe) fit Fold1 size: 26
+(rfe) imp Fold1
-(rfe) imp Fold1
+(rfe) fit Fold1 size: 16
-(rfe) fit Fold1 size: 16
+(rfe) fit Fold1 size: 8
-(rfe) fit Fold1 size: 8
+(rfe) fit Fold1 size: 4
-(rfe) fit Fold1 size: 4
+(rfe) fit Fold1 size: 2
-(rfe) fit Fold1 size: 2
+(rfe) fit Fold2 size: 26
-(rfe) fit Fold2 size: 26
+(rfe) imp Fold2
-(rfe) imp Fold2
+(rfe) fit Fold2 size: 16
-(rfe) fit Fold2 size: 16
+(rfe) fit Fold2 size: 8
-(rfe) fit Fold2 size: 8
+(rfe) fit Fold2 size: 4
-(rfe) fit Fold2 size: 4
+(rfe) fit Fold2 size: 2
-(rfe) fit Fold2 size: 2
+(rfe) fit Fold3 size: 26
-(rfe) fit Fold3 size: 26
+(rfe) imp Fold3
-(rfe) imp Fold3
+(rfe) fit Fold3 size: 16
-(rfe) fit Fold3 size: 16
+(rfe) fit Fold3 size: 8
-(rfe) fit Fold3 size: 8
+(rfe) fit Fold3 size: 4
-(rfe) fit Fold3 size: 4
+(rfe) fit Fold3 size: 2
-(rfe) fit Fold3 size: 2
+(rfe) fit Fold4 size: 26
-(rfe) fit Fold4 size: 26
+(rfe) imp Fold4
-(rfe) imp Fold4
+(rfe) fit Fold4 size: 16
-(rfe) fit Fold4 size: 16
+(rfe) fit Fold4 size: 8
-(rfe) fit Fold4 size: 8
+(rfe) fit Fold4 size: 4
-(rfe) fit Fold4 size: 4
+(rfe) fit Fold4 size: 2
-(rfe) fit Fold4 size: 2
+(rfe) fit Fold5 size: 26
-(rfe) fit Fold5 size: 26
+(rfe) imp Fold5
-(rfe) imp Fold5
+(rfe) fit Fold5 size: 16
-(rfe) fit Fold5 size: 16
+(rfe) fit Fold5 size: 8
-(rfe) fit Fold5 size: 8
+(rfe) fit Fold5 size: 4
-(rfe) fit Fold5 size: 4
+(rfe) fit Fold5 size: 2
-(rfe) fit Fold5 size: 2
+ Fold1: mtry=2, ntree=500, nodesize=1
- Fold1: mtry=2, ntree=500, nodesize=1
+ Fold2: mtry=2, ntree=500, nodesize=1
- Fold2: mtry=2, ntree=500, nodesize=1
+ Fold3: mtry=2, ntree=500, nodesize=1
- Fold3: mtry=2, ntree=500, nodesize=1
Aggregating results
Fitting final model on full training set
[1] "Establishing bp resolution test data using a distance type feature space"
[1] "Establishing probability vector"
[1] "Determining optimal combination of epsilon neighborhood (eps) and minimum number of points (MinPts)"
[1] "Initializing DBSCAN for MinPts=3 and eps=10000"
[1] " preciseTAD identified 13 PTBRs"
[1] " Establishing PTBPs"
[1] " Cluster 1 out of 13"
[1] " Cluster 2 out of 13"
[1] " Cluster 3 out of 13"
[1] " Cluster 4 out of 13"
[1] " Cluster 5 out of 13"
[1] " Cluster 6 out of 13"
[1] " Cluster 7 out of 13"
[1] " Cluster 8 out of 13"
[1] " Cluster 9 out of 13"
[1] " Cluster 10 out of 13"
[1] " Cluster 11 out of 13"
[1] " Cluster 12 out of 13"
[1] " Cluster 13 out of 13"
[1] "Optimal combination of MinPts and eps is = (3, 10000)"
[1] "preciseTAD identified a total of 12897 base pairs whose predictive probability was equal to or exceeded a threshold of 1"
[1] "Initializing DBSCAN for MinPts = 3 and eps = 10000"
[1] "preciseTAD identified 13 PTBRs"
[1] "Establishing PTBPs"
[1] "Cluster 1 out of 13"
[1] "Cluster 2 out of 13"
[1] "Cluster 3 out of 13"
[1] "Cluster 4 out of 13"
[1] "Cluster 5 out of 13"
[1] "Cluster 6 out of 13"
[1] "Cluster 7 out of 13"
[1] "Cluster 8 out of 13"
[1] "Cluster 9 out of 13"
[1] "Cluster 10 out of 13"
[1] "Cluster 11 out of 13"
[1] "Cluster 12 out of 13"
[1] "Cluster 13 out of 13"
+ Fold1: mtry=2, ntree=500, nodesize=1
- Fold1: mtry=2, ntree=500, nodesize=1
+ Fold2: mtry=2, ntree=500, nodesize=1
- Fold2: mtry=2, ntree=500, nodesize=1
+ Fold3: mtry=2, ntree=500, nodesize=1
- Fold3: mtry=2, ntree=500, nodesize=1
Aggregating results
Fitting final model on full training set
[1] "Establishing bp resolution test data using a distance type feature space"
[1] "Establishing probability vector"
[1] "Determining optimal combination of epsilon neighborhood (eps) and minimum number of points (MinPts)"
[1] "Initializing DBSCAN for MinPts=1000 and eps=5000"
[1] " preciseTAD identified 14 PTBRs"
[1] " Establishing PTBPs"
[1] " Cluster 1 out of 14"
[1] " Cluster 2 out of 14"
[1] " Cluster 3 out of 14"
[1] " Cluster 4 out of 14"
[1] " Cluster 5 out of 14"
[1] " Cluster 6 out of 14"
[1] " Cluster 7 out of 14"
[1] " Cluster 8 out of 14"
[1] " Cluster 9 out of 14"
[1] " Cluster 10 out of 14"
[1] " Cluster 11 out of 14"
[1] " Cluster 12 out of 14"
[1] " Cluster 13 out of 14"
[1] " Cluster 14 out of 14"
[1] "Optimal combination of MinPts and eps is = (1000, 5000)"
[1] "preciseTAD identified a total of 59929 base pairs whose predictive probability was equal to or exceeded a threshold of 0.99"
[1] "Initializing DBSCAN for MinPts = 1000 and eps = 5000"
[1] "preciseTAD identified 14 PTBRs"
[1] "Establishing PTBPs"
[1] "Cluster 1 out of 14"
[1] "Cluster 2 out of 14"
[1] "Cluster 3 out of 14"
[1] "Cluster 4 out of 14"
[1] "Cluster 5 out of 14"
[1] "Cluster 6 out of 14"
[1] "Cluster 7 out of 14"
[1] "Cluster 8 out of 14"
[1] "Cluster 9 out of 14"
[1] "Cluster 10 out of 14"
[1] "Cluster 11 out of 14"
[1] "Cluster 12 out of 14"
[1] "Cluster 13 out of 14"
[1] "Cluster 14 out of 14"
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 17 ]
>
> proc.time()
user system elapsed
139.25 7.00 189.26