Print enpls.od object.
Usage
# S3 method for class 'enpls.od'
print(x, ...)Arguments
- x
An object of class
enpls.od.- ...
Additional parameters for
print.
See also
See enpls.od for outlier detection with
ensemble partial least squares regressions.
Author
Nan Xiao <https://nanx.me>
Examples
data("alkanes")
x <- alkanes$x
y <- alkanes$y
set.seed(42)
od <- enpls.od(x, y, reptimes = 40)
od
#> Outlier Detection by Ensemble Partial Least Squares
#> ---
#> Mean residual for each sample:
#> [1] 1.88619274 10.90589635 3.50508166 3.38765122 2.80640829 0.78893412
#> [7] 2.52860922 0.03359353 1.55523763 0.48649170 0.43432626 0.60264392
#> [13] 4.60743523 0.67644470 0.53639715 4.43822997 1.47247790 0.29597845
#> [19] 1.79350103 2.18053241 0.17188372 0.11895953 2.41099045 0.35806773
#> [25] 0.26331275 3.06233743 0.15693996 0.47571654 3.58846460 0.75389595
#> [31] 2.50497874 0.18582900 0.58313578 0.19857068 0.93819095 1.30236929
#> [37] 1.78072059 1.74212844 1.81681614 0.56607629 0.11198885 2.51452857
#> [43] 0.95370707 2.29811304 1.92107913 1.25562046 1.43286292 2.09648342
#> [49] 2.36905445 2.12219678 1.02728317 1.16916512 6.31964132 2.40059584
#> [55] 1.48499676 0.90542576 3.08868401 3.91110096 0.95160646 0.07916320
#> [61] 0.18462919 3.17758683 17.27644064 6.39155053 0.32285733 2.47408085
#> [67] 2.70747954 0.41655958 1.69315765 1.50239800 0.10204587 1.15648225
#> [73] 0.62900446 1.64331655 0.55503176 3.31420980 1.27605642 1.39046876
#> [79] 2.23278137 1.51773588 0.20539385 0.33542097 6.67689517 0.58165439
#> [85] 2.64143148 1.63360300 1.67683506 1.64568659 0.65114573 0.72723380
#> [91] 1.04133381 4.38394410 0.97271337 3.13695344 7.43233908 2.09214321
#> [97] 3.55130795 1.70256745 1.61429257 0.67112325 1.55760643 1.66972207
#> [103] 1.17502778 0.44144264 0.62612126 1.63479798 0.16358120 0.65135135
#> [109] 3.11193984 2.39647052 2.11746126 3.37532333 3.08305474 2.69521595
#> [115] 0.86578014 0.13498366 3.70654747 1.29143687 2.34495998 3.88766352
#> [121] 1.80232307 3.44598270 0.06232686 2.53641137 0.44721954 1.09185683
#> [127] 0.38141453 0.20658348 0.16049292 4.42702163 5.76809366 4.19214667
#> [133] 1.50250723 0.81938085 3.55811038 0.06429908 1.04534298 4.71816950
#> [139] 4.80303317 7.30939897 2.97319816 0.41884796 2.48666014 3.91764922
#> [145] 0.69902110 0.82941173 4.12322010 3.34525976 0.14185730 4.09483062
#> [151] 2.83445432 2.44515884 0.14714117 1.74312000 10.78168799 2.67905773
#> [157] 2.27492015 0.81146521 3.28294805 3.76191119 1.21226660 0.70449836
#> [163] 5.44513387 0.98019630 5.54076365 1.11716981 13.40151891 8.10166836
#> [169] 5.05451974 4.16001828 1.44731150 3.30748113 3.67192369 9.34781239
#> [175] 5.37195673 4.14563113 5.10334839 1.04458199 0.86669564 3.11206791
#> [181] 3.08644803 1.73851775 4.08310808 0.80871455 4.90638801 1.30481757
#> [187] 3.06970899 2.05049667 1.30072269 0.66595903 4.79211893 0.75554939
#> [193] 4.85263504 1.38490605 3.36337975 0.22302993 10.81017088 4.07946712
#> [199] 8.66505567 3.56557630 4.11653349 2.23784104 0.80040234 5.02267651
#> [205] 2.00460709 2.60222215 5.51750096
#> ---
#> Residual SD for each sample:
#> [1] 3.3041016 3.7528097 0.6287911 1.9469013 1.1232235 0.5485301 1.4045617
#> [8] 0.7522304 1.6296649 0.7087677 0.6081811 0.7877093 0.7357322 0.9403901
#> [15] 0.5816742 0.5833505 0.2736066 0.4684476 0.5743993 0.6081003 0.3609794
#> [22] 0.6143993 0.4156304 0.4969925 0.4222710 0.6887305 0.5441532 0.4896297
#> [29] 0.9879193 0.4818513 0.7219000 0.7966308 0.5409247 0.2278245 0.6380086
#> [36] 0.4632656 2.3365104 0.5400322 0.7725710 0.4365486 0.3134155 0.3446047
#> [43] 0.3583251 1.1410511 0.3402650 0.4380000 0.9321529 0.6509107 0.7128634
#> [50] 0.4828276 0.3868975 0.5957191 0.2625778 0.5127357 0.3956559 0.3615582
#> [57] 0.4242556 0.5874111 0.5127348 0.6773799 0.4045032 0.3769534 0.4074678
#> [64] 0.4403505 0.9213356 0.7140239 0.4342494 0.9126194 0.3348556 0.3009267
#> [71] 0.3598233 0.6590692 0.6474388 0.5066233 1.4182923 0.4292809 1.2572876
#> [78] 0.1624436 0.2101038 0.3498939 0.3325393 0.2033827 0.2622584 0.5774602
#> [85] 0.3567808 0.2461149 0.2700110 0.5744636 0.5003152 0.3410442 0.3156831
#> [92] 0.4918909 0.6209790 0.3658942 0.3441131 0.1515226 0.3688352 0.6401491
#> [99] 0.3691702 0.3843657 0.4641710 0.4174310 0.3142639 0.5659825 0.6242340
#> [106] 0.4084429 0.6188155 0.4350521 0.3124214 0.9171589 0.6235125 0.8630642
#> [113] 0.5404761 0.8207514 1.0411927 0.5276077 0.7303770 1.0421700 0.8401458
#> [120] 1.2000292 0.9213492 0.3967389 0.3975921 0.7426849 0.8909864 1.5279479
#> [127] 0.6080130 0.4071117 0.6661871 0.3400878 0.5534629 2.5201942 0.8317301
#> [134] 0.6339794 0.5362687 0.3243553 0.8748678 0.3212679 0.8949716 2.2450596
#> [141] 0.5762151 0.6539516 0.3739754 0.6863370 0.6122660 0.2150478 0.2614764
#> [148] 0.6111684 0.3714130 1.1798078 0.4321432 0.5970870 0.9981701 0.4296940
#> [155] 1.2661150 0.5744207 0.5538887 1.3648434 0.9944283 1.3725367 0.6595768
#> [162] 0.4879736 0.5810827 0.8249397 0.7857057 0.9300459 0.8972485 0.9926545
#> [169] 1.0199147 4.5089205 1.1541768 0.8915807 0.6226884 1.2169453 0.4724696
#> [176] 0.6514313 0.5224253 0.6593973 0.1569680 0.5847442 0.5682814 0.6729592
#> [183] 0.1733294 0.3895711 0.6323498 0.4605052 0.1481093 1.1716547 0.5450096
#> [190] 0.5687975 0.5589908 0.2825736 0.6398288 0.7914032 0.8694980 0.3622459
#> [197] 0.5724749 0.7551469 0.4205248 0.2548245 0.6299627 0.7509742 0.2003678
#> [204] 0.7194789 0.4089175 0.7621391 0.4433501