Nothing
tests/testthat/_snaps/algorithm_minmax_FALSE.md
In rBiasCorrection: Correct Bias in DNA Methylation Analyses
algorithm test, type 1, minmax = FALSE
Code
round(table_prep(rv$reg_stats), 1)
Output
relative_error SSE_hyperbolic R2_hyperbolic a_hyperbolic b_hyperbolic
<num> <num> <num> <num> <num>
1: 22.9 77.3 1 -108.6 -937.7
2: 10.4 45.8 1 -141.9 -2393.1
3: 23.2 67.1 1 -170.4 -1477.5
4: 14.1 58.3 1 -162.2 -2115.9
5: 25.3 7.5 1 -151.4 -1383.3
6: 13.6 11.8 1 -125.0 -1550.6
7: 43.6 72.1 1 -75.3 -772.8
8: 9.4 75.3 1 -278.0 -3872.7
9: 25.8 33.1 1 -70.7 -843.9
10: 19.0 35.4 1 -127.7 -1525.8
d_hyperbolic b1_hyperbolic s_hyperbolic SSE_cubic R2_cubic a_cubic b_cubic
<num> <num> <num> <num> <num> <num> <num>
1: -232.1 0.6 4.1 71.0 1 0 0
2: -268.3 0.6 8.9 46.0 1 0 0
3: -327.2 0.7 4.5 66.8 1 0 0
4: -299.7 0.7 7.1 57.2 1 0 0
5: -304.2 0.7 4.6 6.4 1 0 0
6: -247.3 0.6 6.3 12.3 1 0 0
7: -226.7 0.6 3.5 69.0 1 0 0
8: -438.9 0.8 8.8 73.3 1 0 0
9: -184.5 0.5 4.6 34.5 1 0 0
10: -263.0 0.6 5.8 36.0 1 0 0
c_cubic d_cubic
<num> <num>
1: 0.8 1.9
2: 0.5 9.3
3: 0.6 3.9
4: 0.7 6.2
5: 0.4 5.2
6: 0.6 6.1
7: 0.6 1.7
8: 0.5 9.6
9: 0.7 2.7
10: 0.6 5.2
Code
round(table_prep(rv$final_results), 1)
Output
CpG#1_c CpG#2_h CpG#3_c CpG#4_c CpG#5_c CpG#6_h CpG#7_c CpG#8_c CpG#9_h
<num> <num> <num> <num> <num> <num> <num> <num> <num>
1: NA 76.7 99.5 NA 69.3 79.3 NA 69.5 81.0
2: 31.4 31.0 28.0 29.0 26.8 30.2 24.1 28.7 26.6
3: NA 42.7 45.1 49.4 43.5 41.8 NA 35.5 45.7
4: NA 58.9 65.1 77.4 51.9 56.8 NA 57.5 57.0
5: 9.3 5.0 8.1 10.2 10.2 8.9 11.2 9.9 6.8
6: 20.9 20.2 24.1 22.1 23.4 18.7 24.0 20.3 22.9
7: 22.4 17.8 26.4 27.6 26.4 29.9 28.9 38.9 38.9
8: NA 40.9 48.1 47.0 42.2 42.8 68.1 44.6 43.7
9: NA 86.0 NA NA 84.1 86.8 NA 79.7 88.0
10: 0.5 3.1 0.4 3.3 1.4 1.5 0.5 0.0 0.0
row_means_h
<num>
1: 77.1
2: 28.4
3: 42.5
4: 57.3
5: 8.8
6: 21.8
7: 28.7
8: 43.4
9: 86.4
10: 0.0
Code
round(table_prep(rv$substitutions), 1)
Output
Null data.table (0 rows and 0 cols)
Code
round(table_prep(solved_eq2[["results"]]), 1)
Output
CpG#1_c CpG#2_h CpG#3_c CpG#4_c CpG#5_c CpG#6_h CpG#7_c CpG#8_c CpG#9_h
<num> <num> <num> <num> <num> <num> <num> <num> <num>
1: 1.3 1.1 1.4 0.9 1.5 0.1 2.1 2.0 0.0
2: 11.7 11.4 10.6 12.3 10.3 11.9 10.0 9.5 10.5
3: 24.1 26.2 25.6 24.4 24.5 26.5 23.1 26.4 26.9
4: 50.4 35.1 38.3 41.6 37.1 35.3 42.6 34.7 36.8
5: NA 47.7 57.4 57.9 49.5 50.1 NA 50.9 52.0
6: NA 67.1 80.1 100.0 59.9 65.0 NA 62.3 64.9
7: NA 75.8 100.0 NA 72.6 73.7 NA 74.2 74.6
8: NA 84.4 NA NA 81.5 87.1 NA 76.7 84.5
9: NA 100.0 NA NA 94.9 100.0 NA 94.3 100.0
row_means_h
<num>
1: 0.3
2: 11.0
3: 25.4
4: 36.5
5: 50.7
6: 65.3
7: 75.5
8: 83.2
9: 100.0
Code
round(table_prep(solved_eq2[["substitutions"]]), 1)
Output
Null data.table (0 rows and 0 cols)
Code
round(table_prep(rv$fileimport_cal_corrected), 1)
Output
CpG#1 CpG#2 CpG#3 CpG#4 CpG#5 CpG#6 CpG#7 CpG#8 CpG#9 row_means
<num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
1: 1.3 1.1 1.4 0.9 1.5 0.1 2.1 2.0 0.0 0.3
2: 11.7 11.4 10.6 12.3 10.3 11.9 10.0 9.5 10.5 11.0
3: 24.1 26.2 25.6 24.4 24.5 26.5 23.1 26.4 26.9 25.4
4: 50.4 35.1 38.3 41.6 37.1 35.3 42.6 34.7 36.8 36.5
5: NA 47.7 57.4 57.9 49.5 50.1 NA 50.9 52.0 50.7
6: NA 67.1 80.1 100.0 59.9 65.0 NA 62.3 64.9 65.3
7: NA 75.8 100.0 NA 72.6 73.7 NA 74.2 74.6 75.5
8: NA 84.4 NA NA 81.5 87.1 NA 76.7 84.5 83.2
9: NA 100.0 NA NA 94.9 100.0 NA 94.3 100.0 100.0
Code
round(table_prep(rv$fileimport_cal_corrected_h), 1)
Output
CpG#1 CpG#2 CpG#3 CpG#4 CpG#5 CpG#6 CpG#7 CpG#8 CpG#9 row_means
<num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
1: 0.0 1.1 0.5 0.0 2.4 0.1 0.0 2.8 0.0 0.3
2: 12.2 11.4 10.8 12.5 10.2 11.9 10.2 9.3 10.5 11.0
3: 24.5 26.2 25.5 24.3 24.0 26.5 24.6 25.5 26.9 25.4
4: 38.2 35.1 36.5 38.1 37.3 35.3 37.8 34.0 36.8 36.5
5: 52.3 47.7 50.8 48.6 50.9 50.1 53.6 51.8 52.0 50.7
6: 65.5 67.1 64.9 67.7 62.4 65.0 65.9 64.7 64.9 65.3
7: 75.0 75.8 77.6 74.2 76.4 73.7 75.7 78.4 74.6 75.5
8: 81.5 84.4 80.4 82.9 86.2 87.1 79.4 81.3 84.5 83.2
9: 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Code
round(table_prep(rv$substitutions_corrected_h), 1)
Output
Null data.table (0 rows and 0 cols)
Code
round(table_prep(rv$reg_stats_corrected_h), 0)
Output
relative_error SSE_hyperbolic R2_hyperbolic a_hyperbolic b_hyperbolic
<num> <num> <num> <num> <num>
1: 3 40 1 985 -658
2: 5 46 1 30222 3393
3: 4 60 1 1334 -946
4: 3 47 1 1637 -754
5: 4 17 1 -5621 -1917
6: 3 15 1 13767 -1255
7: 5 77 1 722 -1065
8: 7 86 1 4785 68
9: 5 22 1 1274 -1023
10: 3 27 1 1741 -1167
d_hyperbolic b1_hyperbolic s_hyperbolic SSE_cubic R2_cubic a_cubic b_cubic
<num> <num> <num> <num> <num> <num> <num>
1: 904 1 1 39 1 0 0
2: 30298 1 0 43 1 0 0
3: 1253 1 1 59 1 0 0
4: 1558 1 0 47 1 0 0
5: -5734 1 0 11 1 0 0
6: 13682 1 0 15 1 0 0
7: 634 1 2 76 1 0 0
8: 4739 1 0 74 1 0 0
9: 1182 1 1 22 1 0 0
10: 1654 1 1 26 1 0 0
c_cubic d_cubic
<num> <num>
1: 1 -1
2: 1 1
3: 1 0
4: 1 0
5: 1 1
6: 1 0
7: 1 -2
8: 1 2
9: 1 -1
10: 1 0
Code
round(table_prep(rv$fileimport_cal_corrected_c), 1)
Output
CpG#1 CpG#2 CpG#3 CpG#4 CpG#5 CpG#6 CpG#7 CpG#8 CpG#9 row_means
<num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
1: 1.3 0.5 1.4 0.9 1.5 0.3 2.1 2.0 1.5 1.3
2: 11.7 11.5 10.6 12.3 10.3 11.8 10.0 9.5 10.2 11.0
3: 24.1 26.8 25.6 24.4 24.5 27.0 23.1 26.4 27.0 26.0
4: 50.4 35.9 38.3 41.6 37.1 36.8 42.6 34.7 45.9 39.5
5: NA 48.7 57.4 57.9 49.5 54.4 NA 50.9 NA 60.3
6: NA 69.2 80.1 100.0 59.9 73.9 NA 62.3 NA 87.2
7: NA 78.6 100.0 NA 72.6 86.3 NA 74.2 NA NA
8: NA 88.3 NA NA 81.5 100.0 NA 76.7 NA NA
9: NA 100.0 NA NA 94.9 NA NA 94.3 NA NA
Code
round(table_prep(rv$substitutions_corrected_c), 1)
Output
Null data.table (0 rows and 0 cols)
Code
round(table_prep(rv$reg_stats_corrected_c), 0)
Output
relative_error SSE_hyperbolic R2_hyperbolic a_hyperbolic b_hyperbolic
<num> <num> <num> <num> <num>
1: 15 2 1 -37 -125
2: 5 50 1 1861 -1362
3: 16 15 1 -190 -51
4: 18 31 1 -61 -295
5: 5 11 1 1782 210
6: 10 34 1 -867 642
7: 14 0 1 -43 -153
8: 7 71 1 801 -55
9: 16 4 1 -75 -85
10: 16 3 1 -106 -180
d_hyperbolic b1_hyperbolic s_hyperbolic SSE_cubic R2_cubic a_cubic b_cubic
<num> <num> <num> <num> <num> <num> <num>
1: -67 0 2 0 1 0 0
2: 1725 1 1 36 1 0 0
3: -216 1 0 8 1 0 0
4: -104 0 3 25 1 0 0
5: 1791 1 0 11 1 0 0
6: -827 1 1 21 1 0 0
7: -79 0 2 0 1 0 0
8: 765 1 0 69 1 0 0
9: -100 0 1 0 1 0 0
10: -140 0 1 4 1 0 0
c_cubic d_cubic
<num> <num>
1: 1 1
2: 1 1
3: 1 2
4: 1 0
5: 1 0
6: 1 1
7: 0 2
8: 1 1
9: 0 1
10: 1 1
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rBiasCorrection documentation built on April 12, 2025, 1:16 a.m.
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algorithm test, type 1, minmax = FALSE
Code
round(table_prep(rv$reg_stats), 1)
Output
relative_error SSE_hyperbolic R2_hyperbolic a_hyperbolic b_hyperbolic
<num> <num> <num> <num> <num>
1: 22.9 77.3 1 -108.6 -937.7
2: 10.4 45.8 1 -141.9 -2393.1
3: 23.2 67.1 1 -170.4 -1477.5
4: 14.1 58.3 1 -162.2 -2115.9
5: 25.3 7.5 1 -151.4 -1383.3
6: 13.6 11.8 1 -125.0 -1550.6
7: 43.6 72.1 1 -75.3 -772.8
8: 9.4 75.3 1 -278.0 -3872.7
9: 25.8 33.1 1 -70.7 -843.9
10: 19.0 35.4 1 -127.7 -1525.8
d_hyperbolic b1_hyperbolic s_hyperbolic SSE_cubic R2_cubic a_cubic b_cubic
<num> <num> <num> <num> <num> <num> <num>
1: -232.1 0.6 4.1 71.0 1 0 0
2: -268.3 0.6 8.9 46.0 1 0 0
3: -327.2 0.7 4.5 66.8 1 0 0
4: -299.7 0.7 7.1 57.2 1 0 0
5: -304.2 0.7 4.6 6.4 1 0 0
6: -247.3 0.6 6.3 12.3 1 0 0
7: -226.7 0.6 3.5 69.0 1 0 0
8: -438.9 0.8 8.8 73.3 1 0 0
9: -184.5 0.5 4.6 34.5 1 0 0
10: -263.0 0.6 5.8 36.0 1 0 0
c_cubic d_cubic
<num> <num>
1: 0.8 1.9
2: 0.5 9.3
3: 0.6 3.9
4: 0.7 6.2
5: 0.4 5.2
6: 0.6 6.1
7: 0.6 1.7
8: 0.5 9.6
9: 0.7 2.7
10: 0.6 5.2
Code
round(table_prep(rv$final_results), 1)
Output
CpG#1_c CpG#2_h CpG#3_c CpG#4_c CpG#5_c CpG#6_h CpG#7_c CpG#8_c CpG#9_h
<num> <num> <num> <num> <num> <num> <num> <num> <num>
1: NA 76.7 99.5 NA 69.3 79.3 NA 69.5 81.0
2: 31.4 31.0 28.0 29.0 26.8 30.2 24.1 28.7 26.6
3: NA 42.7 45.1 49.4 43.5 41.8 NA 35.5 45.7
4: NA 58.9 65.1 77.4 51.9 56.8 NA 57.5 57.0
5: 9.3 5.0 8.1 10.2 10.2 8.9 11.2 9.9 6.8
6: 20.9 20.2 24.1 22.1 23.4 18.7 24.0 20.3 22.9
7: 22.4 17.8 26.4 27.6 26.4 29.9 28.9 38.9 38.9
8: NA 40.9 48.1 47.0 42.2 42.8 68.1 44.6 43.7
9: NA 86.0 NA NA 84.1 86.8 NA 79.7 88.0
10: 0.5 3.1 0.4 3.3 1.4 1.5 0.5 0.0 0.0
row_means_h
<num>
1: 77.1
2: 28.4
3: 42.5
4: 57.3
5: 8.8
6: 21.8
7: 28.7
8: 43.4
9: 86.4
10: 0.0
Code
round(table_prep(rv$substitutions), 1)
Output
Null data.table (0 rows and 0 cols)
Code
round(table_prep(solved_eq2[["results"]]), 1)
Output
CpG#1_c CpG#2_h CpG#3_c CpG#4_c CpG#5_c CpG#6_h CpG#7_c CpG#8_c CpG#9_h
<num> <num> <num> <num> <num> <num> <num> <num> <num>
1: 1.3 1.1 1.4 0.9 1.5 0.1 2.1 2.0 0.0
2: 11.7 11.4 10.6 12.3 10.3 11.9 10.0 9.5 10.5
3: 24.1 26.2 25.6 24.4 24.5 26.5 23.1 26.4 26.9
4: 50.4 35.1 38.3 41.6 37.1 35.3 42.6 34.7 36.8
5: NA 47.7 57.4 57.9 49.5 50.1 NA 50.9 52.0
6: NA 67.1 80.1 100.0 59.9 65.0 NA 62.3 64.9
7: NA 75.8 100.0 NA 72.6 73.7 NA 74.2 74.6
8: NA 84.4 NA NA 81.5 87.1 NA 76.7 84.5
9: NA 100.0 NA NA 94.9 100.0 NA 94.3 100.0
row_means_h
<num>
1: 0.3
2: 11.0
3: 25.4
4: 36.5
5: 50.7
6: 65.3
7: 75.5
8: 83.2
9: 100.0
Code
round(table_prep(solved_eq2[["substitutions"]]), 1)
Output
Null data.table (0 rows and 0 cols)
Code
round(table_prep(rv$fileimport_cal_corrected), 1)
Output
CpG#1 CpG#2 CpG#3 CpG#4 CpG#5 CpG#6 CpG#7 CpG#8 CpG#9 row_means
<num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
1: 1.3 1.1 1.4 0.9 1.5 0.1 2.1 2.0 0.0 0.3
2: 11.7 11.4 10.6 12.3 10.3 11.9 10.0 9.5 10.5 11.0
3: 24.1 26.2 25.6 24.4 24.5 26.5 23.1 26.4 26.9 25.4
4: 50.4 35.1 38.3 41.6 37.1 35.3 42.6 34.7 36.8 36.5
5: NA 47.7 57.4 57.9 49.5 50.1 NA 50.9 52.0 50.7
6: NA 67.1 80.1 100.0 59.9 65.0 NA 62.3 64.9 65.3
7: NA 75.8 100.0 NA 72.6 73.7 NA 74.2 74.6 75.5
8: NA 84.4 NA NA 81.5 87.1 NA 76.7 84.5 83.2
9: NA 100.0 NA NA 94.9 100.0 NA 94.3 100.0 100.0
Code
round(table_prep(rv$fileimport_cal_corrected_h), 1)
Output
CpG#1 CpG#2 CpG#3 CpG#4 CpG#5 CpG#6 CpG#7 CpG#8 CpG#9 row_means
<num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
1: 0.0 1.1 0.5 0.0 2.4 0.1 0.0 2.8 0.0 0.3
2: 12.2 11.4 10.8 12.5 10.2 11.9 10.2 9.3 10.5 11.0
3: 24.5 26.2 25.5 24.3 24.0 26.5 24.6 25.5 26.9 25.4
4: 38.2 35.1 36.5 38.1 37.3 35.3 37.8 34.0 36.8 36.5
5: 52.3 47.7 50.8 48.6 50.9 50.1 53.6 51.8 52.0 50.7
6: 65.5 67.1 64.9 67.7 62.4 65.0 65.9 64.7 64.9 65.3
7: 75.0 75.8 77.6 74.2 76.4 73.7 75.7 78.4 74.6 75.5
8: 81.5 84.4 80.4 82.9 86.2 87.1 79.4 81.3 84.5 83.2
9: 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Code
round(table_prep(rv$substitutions_corrected_h), 1)
Output
Null data.table (0 rows and 0 cols)
Code
round(table_prep(rv$reg_stats_corrected_h), 0)
Output
relative_error SSE_hyperbolic R2_hyperbolic a_hyperbolic b_hyperbolic
<num> <num> <num> <num> <num>
1: 3 40 1 985 -658
2: 5 46 1 30222 3393
3: 4 60 1 1334 -946
4: 3 47 1 1637 -754
5: 4 17 1 -5621 -1917
6: 3 15 1 13767 -1255
7: 5 77 1 722 -1065
8: 7 86 1 4785 68
9: 5 22 1 1274 -1023
10: 3 27 1 1741 -1167
d_hyperbolic b1_hyperbolic s_hyperbolic SSE_cubic R2_cubic a_cubic b_cubic
<num> <num> <num> <num> <num> <num> <num>
1: 904 1 1 39 1 0 0
2: 30298 1 0 43 1 0 0
3: 1253 1 1 59 1 0 0
4: 1558 1 0 47 1 0 0
5: -5734 1 0 11 1 0 0
6: 13682 1 0 15 1 0 0
7: 634 1 2 76 1 0 0
8: 4739 1 0 74 1 0 0
9: 1182 1 1 22 1 0 0
10: 1654 1 1 26 1 0 0
c_cubic d_cubic
<num> <num>
1: 1 -1
2: 1 1
3: 1 0
4: 1 0
5: 1 1
6: 1 0
7: 1 -2
8: 1 2
9: 1 -1
10: 1 0
Code
round(table_prep(rv$fileimport_cal_corrected_c), 1)
Output
CpG#1 CpG#2 CpG#3 CpG#4 CpG#5 CpG#6 CpG#7 CpG#8 CpG#9 row_means
<num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
1: 1.3 0.5 1.4 0.9 1.5 0.3 2.1 2.0 1.5 1.3
2: 11.7 11.5 10.6 12.3 10.3 11.8 10.0 9.5 10.2 11.0
3: 24.1 26.8 25.6 24.4 24.5 27.0 23.1 26.4 27.0 26.0
4: 50.4 35.9 38.3 41.6 37.1 36.8 42.6 34.7 45.9 39.5
5: NA 48.7 57.4 57.9 49.5 54.4 NA 50.9 NA 60.3
6: NA 69.2 80.1 100.0 59.9 73.9 NA 62.3 NA 87.2
7: NA 78.6 100.0 NA 72.6 86.3 NA 74.2 NA NA
8: NA 88.3 NA NA 81.5 100.0 NA 76.7 NA NA
9: NA 100.0 NA NA 94.9 NA NA 94.3 NA NA
Code
round(table_prep(rv$substitutions_corrected_c), 1)
Output
Null data.table (0 rows and 0 cols)
Code
round(table_prep(rv$reg_stats_corrected_c), 0)
Output
relative_error SSE_hyperbolic R2_hyperbolic a_hyperbolic b_hyperbolic
<num> <num> <num> <num> <num>
1: 15 2 1 -37 -125
2: 5 50 1 1861 -1362
3: 16 15 1 -190 -51
4: 18 31 1 -61 -295
5: 5 11 1 1782 210
6: 10 34 1 -867 642
7: 14 0 1 -43 -153
8: 7 71 1 801 -55
9: 16 4 1 -75 -85
10: 16 3 1 -106 -180
d_hyperbolic b1_hyperbolic s_hyperbolic SSE_cubic R2_cubic a_cubic b_cubic
<num> <num> <num> <num> <num> <num> <num>
1: -67 0 2 0 1 0 0
2: 1725 1 1 36 1 0 0
3: -216 1 0 8 1 0 0
4: -104 0 3 25 1 0 0
5: 1791 1 0 11 1 0 0
6: -827 1 1 21 1 0 0
7: -79 0 2 0 1 0 0
8: 765 1 0 69 1 0 0
9: -100 0 1 0 1 0 0
10: -140 0 1 4 1 0 0
c_cubic d_cubic
<num> <num>
1: 1 1
2: 1 1
3: 1 2
4: 1 0
5: 1 0
6: 1 1
7: 0 2
8: 1 1
9: 0 1
10: 1 1
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