Two-Sample Wilcoxon Rank Sum and Signed Rank Tests — rowTwoSampleWilcoxonT • featscreen

Computes the two-sample Wilcoxon test for each feature.

See the Details section below for further information.

Usage

rowTwoSampleWilcoxonT(
  x,
  g,
  alternative = c("two.sided", "greater", "less"),
  paired = FALSE,
  null = 0,
  exact = NA,
  correct = TRUE
)

Arguments

x: matrix or data.frame.
g: a vector or factor object giving the group for the corresponding elements of x.
alternative: character string or vector of length nrow(x). The alternative hypothesis for each row of x. Values must be one of "two.sided" (default), "greater" or "less".
paired: logical, whether to compute a paired Wilcoxon test.
null: numerical value or numeric vector of length nrow(x). The true values of the difference in means between the two groups of observations for each row.
exact: logical or NA (default) indicator whether an exact p-value should be computed (see Details). A single value or a logical vector with values for each observation.
correct: logical indicator whether continuity correction should be applied in the cases where p-values are obtained using normal approximation. A single value or logical vector with values for each observation.

Value

A list containing two elements:

statistic: A numeric vector, the values of the test statistic
significance: A numeric vector, the p-values of the selected test

Details

The function internally calls row_wilcoxon_paired for the paired test, and row_wilcoxon_twosample for the unpaired test.

Author

Alessandro Barberis

Examples

#Seed
set.seed(1010)

#Data
x = matrix(rnorm(100 * 20), 100, 20)
g = c(rep(0,10),rep(1,10))

#Compute
rowTwoSampleWilcoxonT(x = x, g = g)
#> $statistic
#>   [1] 48 35 64 35 85 59 57 51 41 59 38 22 36 33 80 42 55 29 53 47 42 81 16 35 48
#>  [26] 61 58 41 36 24 39 92 69 45 34 68 43 43 41 38 53 44 35 60 61 40 76 66 66 85
#>  [51] 79 64 30 63 63 19 53 47 70 32 60 18 59 52 52 35 36 37 35 49 52 22 44 46 45
#>  [76] 25 51 72 44 57 46 39 51 41 57 40 67 50 55 27 66 81 40 61 69 56 39 32 63 29
#> 
#> $significance
#>   [1] 0.9117971811 0.2798610059 0.3149992422 0.2798610059 0.0068414558
#>   [6] 0.5288488601 0.6305289138 0.9705124597 0.5288488601 0.5288488601
#>  [11] 0.3930481283 0.0354629890 0.3149992422 0.2175626231 0.0232306393
#>  [16] 0.5787416917 0.7393643508 0.1230054775 0.8534283054 0.8534283054
#>  [21] 0.5787416917 0.0185433761 0.0089306978 0.2798610059 0.9117971811
#>  [26] 0.4358721774 0.5787416917 0.5288488601 0.3149992422 0.0524259023
#>  [31] 0.4358721774 0.0007252809 0.1654939488 0.7393643508 0.2474506917
#>  [36] 0.1903158761 0.6305289138 0.6305289138 0.5288488601 0.3930481283
#>  [41] 0.8534283054 0.6842105263 0.2798610059 0.4812509472 0.4358721774
#>  [46] 0.4812509472 0.0524259023 0.2474506917 0.2474506917 0.0068414558
#>  [51] 0.0288055598 0.3149992422 0.1431401416 0.3526813744 0.3526813744
#>  [56] 0.0185433761 0.8534283054 0.8534283054 0.1431401416 0.1903158761
#>  [61] 0.4812509472 0.0146896447 0.5288488601 0.9117971811 0.9117971811
#>  [66] 0.2798610059 0.3149992422 0.3526813744 0.2798610059 0.9705124597
#>  [71] 0.9117971811 0.0354629890 0.6842105263 0.7959362619 0.7393643508
#>  [76] 0.0630128386 0.9705124597 0.1051224317 0.6842105263 0.6305289138
#>  [81] 0.7959362619 0.4358721774 0.9705124597 0.5288488601 0.6305289138
#>  [86] 0.4812509472 0.2175626231 1.0000000000 0.7393643508 0.0892095521
#>  [91] 0.2474506917 0.0185433761 0.4812509472 0.4358721774 0.1654939488
#>  [96] 0.6842105263 0.4358721774 0.1903158761 0.3526813744 0.1230054775
#> 

#Compute paired
rowTwoSampleWilcoxonT(x = x, g = g, paired = TRUE)
#> $statistic
#>   [1] 30 18 36 16 52 38 37 19 22 35 22  8 19 12 37 24 28 12 30 30 23 51  9 16 27
#>  [26] 33 31 20 18 12 20 52 43 30 12 40 21 17 14 20 27 29 17 33 29 22 44 36 39 52
#>  [51] 48 40 13 40 33  7 28 26 42 14 35  6 33 30 26 15 21  7 18 28 27  8 24 18 21
#>  [76]  8 29 44 22 36 21 18 28 23 32 19 35 30 32 16 38 44 20 35 46 37 19 15 37 11
#> 
#> $significance
#>   [1] 0.845703125 0.375000000 0.431640625 0.275390625 0.009765625 0.322265625
#>   [7] 0.375000000 0.431640625 0.625000000 0.492187500 0.625000000 0.048828125
#>  [13] 0.431640625 0.130859375 0.375000000 0.769531250 1.000000000 0.130859375
#>  [19] 0.845703125 0.845703125 0.695312500 0.013671875 0.064453125 0.275390625
#>  [25] 1.000000000 0.625000000 0.769531250 0.492187500 0.375000000 0.130859375
#>  [31] 0.492187500 0.009765625 0.130859375 0.845703125 0.130859375 0.232421875
#>  [37] 0.556640625 0.322265625 0.193359375 0.492187500 1.000000000 0.921875000
#>  [43] 0.322265625 0.625000000 0.921875000 0.625000000 0.105468750 0.431640625
#>  [49] 0.275390625 0.009765625 0.037109375 0.232421875 0.160156250 0.232421875
#>  [55] 0.625000000 0.037109375 1.000000000 0.921875000 0.160156250 0.193359375
#>  [61] 0.492187500 0.027343750 0.625000000 0.845703125 0.921875000 0.232421875
#>  [67] 0.556640625 0.037109375 0.375000000 1.000000000 1.000000000 0.048828125
#>  [73] 0.769531250 0.375000000 0.556640625 0.048828125 0.921875000 0.105468750
#>  [79] 0.625000000 0.431640625 0.556640625 0.375000000 1.000000000 0.695312500
#>  [85] 0.695312500 0.431640625 0.492187500 0.845703125 0.695312500 0.275390625
#>  [91] 0.322265625 0.105468750 0.492187500 0.492187500 0.064453125 0.375000000
#>  [97] 0.431640625 0.232421875 0.375000000 0.105468750
#>