Print enpls.ad object.
Usage
# S3 method for class 'enpls.ad'
print(x, ...)Arguments
- x
An object of class
enpls.ad.- ...
Additional parameters for
print.
See also
See enpls.ad for model applicability domain
evaluation with ensemble partial least squares regressions.
Author
Nan Xiao <https://nanx.me>
Examples
data("alkanes")
x <- alkanes$x
y <- alkanes$y
# training set
x.tr <- x[1:100, ]
y.tr <- y[1:100]
# two test sets
x.te <- list(
"test.1" = x[101:150, ],
"test.2" = x[151:207, ]
)
y.te <- list(
"test.1" = y[101:150],
"test.2" = y[151:207]
)
set.seed(42)
ad <- enpls.ad(
x.tr, y.tr, x.te, y.te,
space = "variable", method = "mc",
ratio = 0.9, reptimes = 50
)
ad
#> Model Applicability Domain Evaluation by ENPLS
#> ---
#> Absolute mean prediction error for each training set sample:
#> [1] 1.15659310 0.37135659 0.18687002 1.17664393 0.12373621 1.00392170
#> [7] 0.04434202 0.63828656 0.43718385 0.64726539 0.09051787 0.48966778
#> [13] 3.62308256 0.52948567 0.25251989 3.32711614 0.95156004 0.14091017
#> [19] 0.85464231 1.29109538 0.02019387 0.32245514 0.62770096 0.25487849
#> [25] 0.46810576 0.67759188 0.25457816 0.69278642 0.70787689 0.72829569
#> [31] 1.57186733 0.76802915 1.31022753 0.72248475 0.85636595 1.22138075
#> [37] 0.45068514 1.40704563 1.21935389 1.11988622 2.19718455 2.45031644
#> [43] 1.49666523 1.08541858 1.52751554 1.68620203 0.74572579 0.87057898
#> [49] 2.03595023 1.89786907 1.68599205 0.05692682 3.87440488 0.97422048
#> [55] 0.40778747 0.94140304 2.54484306 2.66435962 1.66527372 0.38685260
#> [61] 0.92713783 3.58843757 14.16899449 5.94141096 0.84436844 1.12210481
#> [67] 2.29468141 1.39277186 1.44998603 2.38993959 0.59566790 1.09886862
#> [73] 1.27593299 2.05666549 1.63679051 2.12750518 0.27833880 1.85919482
#> [79] 1.80079825 0.74364023 1.06912418 0.38683360 5.70973358 0.35817828
#> [85] 2.67017284 0.71305049 2.17485482 2.01172042 1.54421445 0.26828780
#> [91] 1.23313227 3.79410089 2.79472535 4.15099941 6.49989050 1.74711076
#> [97] 3.54480005 1.57668886 1.79632988 1.09721925
#> ---
#> Prediction error SD for each training set sample:
#> [1] 0.6723203 0.9925861 0.7916684 0.5353981 0.6833493 0.3750142 0.5384201
#> [8] 0.4394781 0.4478961 0.3115000 0.2965013 0.6564925 0.8581624 0.6408359
#> [15] 0.2112281 0.7276103 0.4012712 0.2924988 0.5139599 0.4312994 0.3225841
#> [22] 0.2810618 0.3927624 0.3747409 0.2372358 0.4391011 0.4310893 0.2127917
#> [29] 0.4711463 0.4231286 0.3089803 0.2203195 0.7273629 0.5681682 0.1253907
#> [36] 0.1542993 0.5196908 0.4079485 0.3314376 0.2223870 0.6052788 0.2461725
#> [43] 0.3104808 0.6333215 0.4917393 0.5680786 0.7958397 0.3688654 0.6318733
#> [50] 0.4057159 0.3159804 0.3805643 0.7569301 0.3793449 0.1648608 0.3351091
#> [57] 0.4672745 0.8267976 0.4487075 0.3364956 0.4968275 0.2133814 0.8887981
#> [64] 0.2061471 0.2362712 0.5270498 0.2774651 0.3162993 0.5402216 0.4409003
#> [71] 0.2945032 0.4952080 0.2839455 0.5987966 0.3229918 0.3881967 0.4959163
#> [78] 0.5784472 0.2844325 0.8621228 0.4714674 0.1963147 0.2992360 0.4816676
#> [85] 0.1823169 0.4285107 0.1955258 0.5412362 0.2689507 0.8016830 0.3348107
#> [92] 0.4141135 0.6989277 0.5368645 0.2533462 0.1989506 0.3307565 0.7953010
#> [99] 0.2301105 0.5033743
#> ---
#> Absolute mean prediction error for each test set sample:
#> [[1]]
#> [1] 1.6168531 0.5129803 1.6250825 0.0203619 4.3383400 0.1106109
#> [7] 0.8148653 1.9913556 3.2096770 2.2171926 2.5990837 2.6064094
#> [13] 3.3267257 1.5533525 0.2446492 2.7310229 3.3531130 0.6680140
#> [19] 1.9791727 12.2374458 13.4166499 11.1587762 14.5567185 8.4080822
#> [25] 10.1272429 10.6439947 13.7889019 12.3442711 12.6224187 7.7835221
#> [31] 4.2295455 12.6785692 12.9451491 13.7247623 5.0281196 13.6627983
#> [37] 12.7484447 5.1156233 3.4747344 13.4024321 6.8780031 12.3931679
#> [43] 5.5321643 3.3919701 13.4337748 8.3609657 4.5468609 3.7926681
#> [49] 8.4599080 7.8685360
#>
#> [[2]]
#> [1] 4.3639558 3.3964642 12.0544328 11.3324274 1.2463920 3.4669273
#> [7] 2.2877420 8.5348824 2.0397521 0.3601160 38.9955576 33.2649875
#> [13] 37.2145440 33.8217957 37.3135529 31.7305484 42.5682817 37.0789591
#> [19] 25.5740117 28.1372472 31.0069656 25.5649259 27.7147261 37.1938925
#> [25] 33.1063600 24.4682094 22.2051201 0.2364423 1.0464166 3.5595735
#> [31] 2.7676125 2.3996208 1.7344534 0.7284600 6.1337782 1.5540024
#> [37] 5.0926006 5.2653924 0.7332907 1.3058880 4.0156528 1.0809554
#> [43] 4.9419237 0.7271527 3.9547319 16.5062741 3.9317236 15.0340504
#> [49] 3.5288351 7.1680305 13.2197061 11.5264492 12.0580371 13.5543787
#> [55] 8.0233824 13.7150370 12.3711093
#>
#> ---
#> Prediction error SD for each test set sample:
#> [[1]]
#> [1] 0.4797399 0.9133170 0.2210427 0.4467299 0.5539222 0.7202601
#> [7] 0.3420715 0.4083844 0.7906344 1.3627053 0.8435695 0.6415281
#> [13] 0.8357520 0.9553027 0.8306586 0.8760966 0.6962081 1.5891505
#> [19] 1.0981642 12.2938695 12.2441362 11.7821422 11.3326666 9.8410896
#> [25] 7.9849343 376.9162082 11.1413626 10.5392662 10.4817856 10.3927164
#> [31] 11.0753343 9.4622277 10.1555745 10.7601625 10.8400841 10.0669724
#> [37] 10.0677476 10.2535897 10.6672633 9.0113683 10.2352971 10.4262657
#> [43] 10.3678646 10.4748206 9.7797714 10.3870910 9.8668577 10.2321430
#> [49] 10.0652508 9.5121937
#>
#> [[2]]
#> [1] 10.1638483 10.1759666 9.5177953 9.5385654 9.5055823 9.9554471
#> [7] 10.0826334 8.5796991 9.4517702 9.3947894 30.8269969 26.2958694
#> [13] 26.7640324 28.8511638 27.9602633 28.0373320 28.6187614 29.0240439
#> [19] 27.9815166 359.7986836 28.5880154 28.7358163 27.3902084 27.9664479
#> [25] 28.3790335 27.1663120 27.9724002 0.2617093 1.4992584 1.3071739
#> [31] 0.5646261 0.4787582 0.3340600 0.6052250 0.7380154 0.3165511
#> [37] 0.4492955 384.6771020 0.2690984 0.4416264 0.8652585 0.1814238
#> [43] 0.3736143 0.3488580 0.9745984 11.3696246 10.2310526 11.3901862
#> [49] 10.9868325 10.5839028 11.0100009 8.9886192 10.0022008 10.6591029
#> [55] 11.0407728 10.8211976 10.5118858
#>