This function is used by the VIM GUI for transformation and
standardization of the data.
a vector, matrix or data.frame.
the scaling to be applied to the data. Possible values are
"none", "classical", MCD, "robust" and
"onestep".
the transformation of the data. Possible values are
"none", "minus", "reciprocal", "logarithm",
"exponential", "boxcox", "clr", "ilr" and
"alr".
a numeric parameter controlling the size of the subset for the
MCD (if scaling="MCD"). See robustbase::covMcd().
a numeric vector giving the powers to be used in the Box-Cox
transformation (if transformation="boxcox"). If NULL, the
powers are calculated with function car::powerTransform().
a constant to be added prior to Box-Cox transformation (if
transformation="boxcox").
variable to be used as denominator in the additive logratio
transformation (if transformation="alr").
Transformed and standardized data.
Transformation:
"none": no transformation is used.
"logarithm": compute the the logarithm (to the base 10).
"boxcox": apply a Box-Cox transformation. Powers may be specified or
calculated with the function car::powerTransform().
Standardization:
"none": no standardization is used.
"classical": apply a z-Transformation on each variable by
using function scale().
"robust": apply a robustified z-Transformation by using median
and MAD.
data(sleep, package = "VIM")
x <- sleep[, c("BodyWgt", "BrainWgt")]
prepare(x, scaling = "robust", transformation = "logarithm")
#> BodyWgt BrainWgt
#> [1,] 2.29654423 2.065651791
#> [2,] -0.36479406 -0.341973693
#> [3,] 0.00384425 0.337397352
#> [4,] -0.39000221 -0.394162508
#> [5,] 2.00622319 1.988808266
#> [6,] 0.34751616 0.833886347
#> [7,] -1.50523403 -1.442342190
#> [8,] 1.16954595 0.812428746
#> [9,] -0.00384425 0.140573701
#> [10,] 0.83068522 1.153063414
#> [11,] -0.62348131 -0.352927979
#> [12,] 1.49208299 1.139036650
#> [13,] -0.54553373 -0.702089671
#> [14,] 1.21685020 1.135654333
#> [15,] -1.14789050 -0.948840510
#> [16,] -0.03265868 0.132130944
#> [17,] -0.43797777 -0.567778126
#> [18,] -0.85136360 -0.440806763
#> [19,] -0.26091911 0.005159582
#> [20,] 0.87301905 0.550618842
#> [21,] 1.53106780 1.308030646
#> [22,] 0.63891332 0.675385949
#> [23,] -1.00579776 -1.013744471
#> [24,] 1.24740764 1.124434445
#> [25,] 0.97832006 1.045217553
#> [26,] 0.72134356 0.689050210
#> [27,] -1.05790948 -0.520242791
#> [28,] -0.35293675 -0.406877653
#> [29,] 1.52646089 1.294696270
#> [30,] 1.02745321 0.786209326
#> [31,] 0.71006820 0.419225234
#> [32,] -1.96659542 -1.713653541
#> [33,] -1.75704133 -1.507246151
#> [34,] 0.88293216 1.544154242
#> [35,] -1.00080057 -0.622653643
#> [36,] -0.27406564 -0.269069705
#> [37,] -1.50523403 -1.339931339
#> [38,] -1.28281320 -1.408413080
#> [39,] -0.20437312 -0.358534177
#> [40,] 0.01394457 -0.166658854
#> [41,] 1.30446866 1.191378447
#> [42,] -0.58668957 -0.038043210
#> [43,] 0.33132958 0.675385949
#> [44,] -0.21894570 -0.147411668
#> [45,] 1.22466589 0.834876573
#> [46,] -0.08777861 -0.126197703
#> [47,] 0.07533343 0.292253871
#> [48,] -0.74964042 -0.785253337
#> [49,] 0.07157342 0.381718343
#> [50,] 0.21473507 0.832893358
#> [51,] -0.45176687 -0.120361733
#> [52,] 0.02246126 0.070063542
#> [53,] 0.45046498 0.619166501
#> [54,] 0.84944951 0.824848129
#> [55,] -0.26307088 -0.114619896
#> [56,] -1.21535185 -1.013744471
#> [57,] -0.39664693 -0.673595570
#> [58,] -0.15523996 -0.120361733
#> [59,] -1.04906038 -0.687557603
#> [60,] 0.06834383 0.431717260
#> [61,] 0.01394457 -0.529255582
#> [62,] 0.05806974 -0.005159582
#> attr(,"scaled:center")
#> BodyWgt BrainWgt
#> 0.5240363 1.2367435
#> attr(,"scaled:scale")
#> BodyWgt BrainWgt
#> 1.436526 1.219976