Draw bootstrap replicates of a fitted PLS model, refitting on each resample.
Arguments
- X
Predictor matrix.
- Y
Response matrix or vector.
- ncomp
Number of components.
- R
Number of bootstrap replications.
- algorithm
Backend algorithm ("simpls", "nipals", "kernelpls" or "widekernelpls").
- backend
Backend argument passed to the fitting routine.
- conf
Confidence level.
- seed
Optional seed.
- type
Character; bootstrap scheme, e.g.
"pairs","residual", or"parametric".- parallel
Logical or character; if
TRUEor one ofc("sequential", "multisession", "multicore"), uses the future framework.- future_seed
Logical or integer; forwarded to
future.seedfor reproducible parallel streams.- return_scores
Logical; if
TRUE, return component scores for each replicate (may be large).- ...
Additional arguments forwarded to
pls_fit().
Examples
set.seed(123)
X <- matrix(rnorm(60), nrow = 20)
y <- X[, 1] - 0.5 * X[, 2] + rnorm(20, sd = 0.1)
pls_bootstrap(X, y, ncomp = 2, R = 20)
#> $mean
#> [,1]
#> [1,] 0.9913106
#> [2,] -0.4513641
#> [3,] -0.0364948
#>
#> $lower
#> [,1]
#> [1,] 0.9008479
#> [2,] -0.5554312
#> [3,] -0.1578291
#>
#> $upper
#> [,1]
#> [1,] 1.04643103
#> [2,] -0.29747912
#> [3,] 0.03218767
#>
#> $samples
#> $samples[[1]]
#> [,1]
#> [1,] 1.01370235
#> [2,] -0.47362869
#> [3,] -0.01871238
#>
#> $samples[[2]]
#> [,1]
#> [1,] 1.00805220
#> [2,] -0.29373547
#> [3,] -0.03438166
#>
#> $samples[[3]]
#> [,1]
#> [1,] 1.02482494
#> [2,] -0.43981055
#> [3,] -0.06598034
#>
#> $samples[[4]]
#> [,1]
#> [1,] 0.9217837
#> [2,] -0.4195667
#> [3,] -0.1675766
#>
#> $samples[[5]]
#> [,1]
#> [1,] 0.90088847
#> [2,] -0.56898923
#> [3,] -0.06228952
#>
#> $samples[[6]]
#> [,1]
#> [1,] 1.03738707
#> [2,] -0.48335047
#> [3,] 0.02431306
#>
#> $samples[[7]]
#> [,1]
#> [1,] 1.03665669
#> [2,] -0.41794678
#> [3,] -0.01365786
#>
#> $samples[[8]]
#> [,1]
#> [1,] 0.90081111
#> [2,] -0.53015677
#> [3,] -0.06947427
#>
#> $samples[[9]]
#> [,1]
#> [1,] 1.046587228
#> [2,] -0.420771928
#> [3,] 0.006859411
#>
#> $samples[[10]]
#> [,1]
#> [1,] 1.0205916
#> [2,] -0.3137173
#> [3,] -0.0397945
#>
#> $samples[[11]]
#> [,1]
#> [1,] 1.04625839
#> [2,] -0.46446341
#> [3,] 0.00655102
#>
#> $samples[[12]]
#> [,1]
#> [1,] 0.9487480
#> [2,] -0.5126467
#> [3,] -0.1244670
#>
#> $samples[[13]]
#> [,1]
#> [1,] 0.96695471
#> [2,] -0.52643057
#> [3,] -0.01118588
#>
#> $samples[[14]]
#> [,1]
#> [1,] 1.00591617
#> [2,] -0.51301573
#> [3,] 0.03150559
#>
#> $samples[[15]]
#> [,1]
#> [1,] 1.00430562
#> [2,] -0.49498888
#> [3,] -0.03036961
#>
#> $samples[[16]]
#> [,1]
#> [1,] 1.006820717
#> [2,] -0.490124440
#> [3,] 0.006203915
#>
#> $samples[[17]]
#> [,1]
#> [1,] 1.0193070
#> [2,] -0.3016168
#> [3,] -0.1470557
#>
#> $samples[[18]]
#> [,1]
#> [1,] 0.9601683
#> [2,] -0.5404460
#> [3,] -0.0334001
#>
#> $samples[[19]]
#> [,1]
#> [1,] 0.99306219
#> [2,] -0.51709589
#> [3,] -0.01978846
#>
#> $samples[[20]]
#> [,1]
#> [1,] 0.96338505
#> [2,] -0.30477937
#> [3,] 0.03280478
#>
#>
#> $type
#> [1] "xy"
#>
#> $base_fit
#> $coefficients
#> [,1]
#> [1,] 1.01956855
#> [2,] -0.45295054
#> [3,] -0.01325522
#>
#> $intercept
#> [1] -0.01093997
#>
#> $x_weights
#> [,1] [,2]
#> [1,] 0.20997486 0.04194897
#> [2,] -0.08441327 -0.08227311
#> [3,] -0.03572358 0.23617705
#>
#> $x_loadings
#> [,1] [,2]
#> [1,] 3.972259 0.3468121
#> [2,] -1.452865 -0.7870847
#> [3,] -1.211650 3.8983283
#>
#> $y_loadings
#> [,1] [,2]
#> [1,] 4.724127 0.658436
#>
#> $x_means
#> [1] 0.14162380 -0.05125716 0.10648523
#>
#> $y_means
#> [1] 0.1552607
#>
#> $ncomp
#> [1] 2
#>
#> $mode
#> [1] "pls1"
#>
#> $algorithm
#> [1] "simpls"
#>
#> $x_center
#> [1] 0.14162380 -0.05125716 0.10648523
#>
#> $y_center
#> [1] 0.1552607
#>
#> $X
#> [,1] [,2] [,3]
#> [1,] -0.56047565 -1.06782371 -0.69470698
#> [2,] -0.23017749 -0.21797491 -0.20791728
#> [3,] 1.55870831 -1.02600445 -1.26539635
#> [4,] 0.07050839 -0.72889123 2.16895597
#> [5,] 0.12928774 -0.62503927 1.20796200
#> [6,] 1.71506499 -1.68669331 -1.12310858
#> [7,] 0.46091621 0.83778704 -0.40288484
#> [8,] -1.26506123 0.15337312 -0.46665535
#> [9,] -0.68685285 -1.13813694 0.77996512
#> [10,] -0.44566197 1.25381492 -0.08336907
#> [11,] 1.22408180 0.42646422 0.25331851
#> [12,] 0.35981383 -0.29507148 -0.02854676
#> [13,] 0.40077145 0.89512566 -0.04287046
#> [14,] 0.11068272 0.87813349 1.36860228
#> [15,] -0.55584113 0.82158108 -0.22577099
#> [16,] 1.78691314 0.68864025 1.51647060
#> [17,] 0.49785048 0.55391765 -1.54875280
#> [18,] -1.96661716 -0.06191171 0.58461375
#> [19,] 0.70135590 -0.30596266 0.12385424
#> [20,] -0.47279141 -0.38047100 0.21594157
#>
#> attr(,"class")
#> [1] "big_plsr" "list"
#>
#> $bca_lower
#> [,1]
#> [1,] 0.9569592
#> [2,] -0.5388344
#> [3,] -0.1286506
#>
#> $bca_upper
#> [,1]
#> [1,] 1.04658723
#> [2,] -0.29373547
#> [3,] 0.03280478
#>
#> $jackknife
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 1.02017326 1.01810673 1.0157243 1.022873634 1.022063121 1.006261044
#> [2,] -0.45607710 -0.45505536 -0.4721037 -0.447478457 -0.450290543 -0.459143899
#> [3,] -0.01054496 -0.01446393 -0.0163173 -0.007862433 -0.008440752 -0.001161789
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] 1.02218562 1.01821092 1.0298271 1.033548831 1.02008830 1.02213589
#> [2,] -0.44599976 -0.46178303 -0.4318445 -0.451637856 -0.46045832 -0.45635818
#> [3,] -0.01287389 -0.01462013 -0.0257894 0.001207447 -0.01504661 -0.01400757
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] 1.02077655 1.018276698 1.01973985 0.97161991 1.02227656 0.99412356
#> [2,] -0.45207275 -0.453293180 -0.43544349 -0.51802509 -0.44269325 -0.48281199
#> [3,] -0.01111254 -0.005173051 -0.01309007 -0.03708979 -0.02380684 -0.01061448
#> [,19] [,20]
#> [1,] 1.01867452 1.01955556
#> [2,] -0.45201139 -0.45425090
#> [3,] -0.01344972 -0.01466117
#>