Print enpls.od object.
Usage
# S3 method for 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] 1.92330900 10.89021255 3.27046898 3.44664155 2.78719736 1.00721093
#> [7] 2.32621585 0.15516618 1.49258397 0.50951677 0.19598423 0.37894319
#> [13] 4.43421626 0.48301514 0.46327275 4.53530921 1.58350187 0.22256084
#> [19] 1.82285515 2.19105280 0.14492503 0.11669131 2.37152147 0.41554215
#> [25] 0.24372355 3.03750326 0.11900180 0.49344478 3.58562713 0.75971852
#> [31] 2.50370236 0.18596822 0.62717089 0.22202733 0.95571546 1.31467423
#> [37] 1.69682775 1.75866125 1.74541520 0.59886200 0.15590344 2.52512176
#> [43] 0.94597983 2.01631116 2.00243170 1.25466867 1.43118935 2.10773058
#> [49] 2.37800560 2.09036202 1.08161933 1.18210080 6.31361052 2.38609257
#> [55] 1.50573384 0.91316064 3.07453404 3.91117478 1.05337202 0.08206764
#> [61] 0.19754492 3.19042773 17.31570083 6.37110043 0.40148348 2.38478712
#> [67] 2.69960493 0.42132555 1.69524072 1.54012000 0.11009454 1.20152745
#> [73] 0.65641423 1.61667859 0.68379966 3.31536455 1.22339435 1.41000966
#> [79] 2.24609395 1.51285921 0.10342766 0.30636847 6.66882111 0.58845492
#> [85] 2.63725148 1.65695792 1.63390152 1.61835598 0.57600256 0.72731511
#> [91] 1.05198101 4.36533755 1.05663524 3.08473948 7.43305443 2.09063428
#> [97] 3.55520392 1.65515761 1.61513447 0.63884967 1.58664742 1.67695751
#> [103] 1.27496896 0.40203138 0.50182230 1.63273307 0.14090982 0.65288976
#> [109] 3.11853398 2.36473487 2.11093608 3.53194269 3.08191944 2.69316941
#> [115] 0.90240623 0.02872732 3.70403651 1.29183732 2.23125273 3.85398723
#> [121] 1.84217151 3.66076738 0.10484361 2.54633226 0.46617427 1.16805320
#> [127] 0.39139204 0.16443923 0.08003652 4.42158712 5.73478695 4.16317725
#> [133] 1.49768800 0.84443803 3.48408280 0.04720127 1.15212362 4.71514260
#> [139] 4.78587567 7.08535845 3.01501307 0.28435017 2.45823906 3.83891306
#> [145] 0.74162634 0.81445493 4.13218270 3.24855964 0.14203270 4.18057865
#> [151] 2.78935621 2.43358909 0.15734928 1.74646711 11.01319565 2.62325287
#> [157] 2.24596141 0.82329103 3.26220014 3.77020529 1.18146477 0.75000697
#> [163] 5.42889310 0.97793800 5.53820064 1.10714760 13.35689245 8.10495666
#> [169] 5.08334965 3.59961335 1.44661616 3.31217366 3.63769784 9.21946333
#> [175] 5.36676320 4.14898335 5.07866592 1.06404762 0.85409149 3.01818476
#> [181] 3.09981869 1.82048171 4.08481150 0.81430555 4.86950932 1.30042868
#> [187] 3.04547591 2.36659361 1.30922974 0.67546767 4.83813159 0.76363716
#> [193] 4.85278793 1.42203141 3.29097215 0.22473577 11.03793378 4.04759625
#> [199] 8.66377110 3.56129270 4.11964350 2.27536705 0.79659843 5.18525972
#> [205] 2.02486439 2.61653055 5.50770355
#> ---
#> Residual SD for each sample:
#> [1] 3.2833878 3.8273794 1.0560186 2.0025722 1.1289381 0.8097269 1.3784385
#> [8] 0.7644119 1.7038686 0.6949922 0.6511539 0.8188688 0.7642990 0.9576901
#> [15] 0.5493217 0.3829378 0.3262016 0.5612040 0.5585085 0.5914671 0.3469040
#> [22] 0.6198233 0.3967101 0.4385455 0.4501116 0.6626386 0.5248715 0.4872178
#> [29] 0.9541322 0.4900806 0.7301441 0.7965155 0.5781910 0.2309336 0.6417451
#> [36] 0.4647038 2.4019232 0.5483285 0.8192984 0.4314438 0.3179872 0.3312354
#> [43] 0.3733198 1.4769004 0.2945059 0.4482088 0.9344860 0.6220916 0.7005594
#> [50] 0.4662778 0.4220982 0.6338109 0.2638589 0.5120856 0.4077683 0.3573461
#> [57] 0.4410739 0.5874401 0.5968931 0.6777397 0.4197461 0.4003001 0.4686458
#> [64] 0.4263087 1.0492434 0.7580662 0.4304740 0.9193812 0.3237159 0.3706392
#> [71] 0.3702639 0.6251546 0.6000215 0.5021201 1.7171270 0.4327897 1.3029738
#> [78] 0.1964857 0.1818725 0.3704031 0.3831923 0.2224975 0.2599175 0.5383308
#> [85] 0.3500381 0.2374213 0.3201375 0.6122773 0.4425255 0.3409247 0.3270531
#> [92] 0.4027017 0.6982817 0.4485474 0.3433372 0.1496763 0.3733111 0.6743161
#> [99] 0.3734439 0.3823817 0.4611125 0.4078577 0.3041751 0.5314541 0.6941926
#> [106] 0.4185355 0.6356265 0.4368110 0.3118178 0.9631201 0.6241170 0.8467951
#> [113] 0.5370068 0.8182740 1.0267493 0.6014406 0.7439188 1.0417452 0.9159984
#> [120] 1.0912602 1.2090378 0.2448170 0.3614916 0.7161885 0.8897647 1.3832014
#> [127] 0.5998433 0.4049862 0.5840936 0.3372879 0.5792705 2.5575032 0.8360641
#> [134] 0.6016499 0.5772573 0.3279032 0.7315307 0.3090083 0.9269372 2.3859418
#> [141] 0.5067978 0.7311754 0.3569741 0.7275507 0.6003814 0.1884859 0.2617410
#> [148] 0.7306165 0.3746068 1.2490920 0.4336852 0.5923654 0.9873645 0.3988957
#> [155] 1.3101247 0.6287461 0.5560878 1.3617496 0.9841517 1.3764557 0.7021863
#> [162] 0.4625153 0.6237591 0.8036729 0.7978216 0.9488726 0.9800277 1.0070299
#> [169] 1.0055290 4.2708204 1.1540947 0.9365893 0.6317219 1.3354158 0.4766210
#> [176] 0.6604796 0.5105765 0.6308363 0.1133016 0.5834894 0.5800087 0.7344611
#> [183] 0.1725119 0.3842890 0.6562976 0.4601188 0.1435015 1.6094283 0.5524734
#> [190] 0.4649504 0.5169569 0.2957184 0.6396079 0.7796620 0.9774836 0.3619579
#> [197] 0.7784514 0.6964077 0.4218187 0.2469681 0.6395878 0.6537583 0.2377910
#> [204] 0.8467404 0.4538872 0.7481834 0.4360923