Score impact of each sample on correlation sturucture
Source:R/other_corr_tests.R
delaneau.score.RdScore impact of each sample on correlation sturucture. Compute correlation using all samples (i.e. C), then compute correlation omitting sample i (i.e. Ci). The score the sample i is based on the difference between C and Ci.
Usage
delaneau.score(Y, method = c("pearson", "kendall", "spearman"))Arguments
- Y
data matrix with samples on rows and variables on columns
- method
specify which correlation method: "pearson", "kendall" or "spearman"
Examples
# load iris data
data(iris)
# Evalaute score on each sample
delaneau.score( iris[,1:4] )
#> 1 2 3 4 5
#> 2.392020e-03 -2.023052e-03 -2.254228e-04 -1.175789e-03 3.067485e-03
#> 6 7 8 9 10
#> 3.613378e-03 1.739786e-03 1.610689e-03 -3.581280e-03 -1.058259e-03
#> 11 12 13 14 15
#> 3.585132e-03 1.608228e-03 -2.264272e-03 -2.863378e-03 5.269956e-03
#> 16 17 18 19 20
#> 4.101427e-03 4.175666e-03 2.310130e-03 3.590239e-03 3.938222e-03
#> 21 22 23 24 25
#> 1.847168e-03 3.298703e-03 3.512710e-03 8.622414e-04 1.644556e-03
#> 26 27 28 29 30
#> -1.787499e-03 1.532360e-03 2.385847e-03 1.754538e-03 -1.226307e-04
#> 31 32 33 34 35
#> -1.039851e-03 1.740320e-03 5.640836e-03 5.352945e-03 -1.002131e-03
#> 36 37 38 39 40
#> -2.111842e-05 2.801364e-03 3.216784e-03 -2.461947e-03 1.652132e-03
#> 41 42 43 44 45
#> 2.358733e-03 -1.152656e-02 -1.921528e-04 2.156260e-03 3.406659e-03
#> 46 47 48 49 50
#> -2.001511e-03 4.051521e-03 -2.039643e-04 3.560882e-03 8.334199e-04
#> 51 52 53 54 55
#> -2.621935e-04 -7.436557e-04 -1.444262e-04 -1.576373e-03 8.006617e-04
#> 56 57 58 59 60
#> 2.988601e-04 -1.430851e-03 -3.516053e-03 7.904226e-04 1.251058e-04
#> 61 62 63 64 65
#> -6.620587e-03 1.136425e-04 -1.713778e-03 3.127337e-04 -4.507567e-05
#> 66 67 68 69 70
#> 8.323117e-05 4.918621e-04 -3.178555e-04 6.151750e-04 -1.254188e-03
#> 71 72 73 74 75
#> -4.442575e-04 2.056908e-04 1.251319e-03 5.066490e-04 4.963374e-04
#> 76 77 78 79 80
#> 3.672432e-04 1.391915e-03 -7.654247e-05 2.766486e-04 -1.282107e-03
#> 81 82 83 84 85
#> -1.954137e-03 -2.335353e-03 -3.072779e-04 1.005528e-03 8.558707e-04
#> 86 87 88 89 90
#> -1.385069e-03 -2.419081e-04 5.224943e-04 1.949653e-04 -7.713257e-04
#> 91 92 93 94 95
#> -2.010578e-04 6.732146e-05 -4.417269e-04 -4.271265e-03 -3.506618e-05
#> 96 97 98 99 100
#> 1.276182e-04 1.455780e-04 2.810288e-04 -2.715923e-03 2.715293e-05
#> 101 102 103 104 105
#> -2.811279e-03 1.608949e-03 -8.295545e-04 4.658131e-04 -4.556453e-04
#> 106 107 108 109 110
#> -9.522773e-04 1.776821e-03 7.473937e-04 2.684696e-03 -9.533994e-03
#> 111 112 113 114 115
#> -1.643350e-03 1.352727e-03 -5.915504e-04 2.258187e-03 2.590909e-03
#> 116 117 118 119 120
#> -1.672388e-03 -2.684843e-04 -1.375366e-02 3.536663e-03 8.647845e-04
#> 121 122 123 124 125
#> -2.868901e-03 1.845266e-03 1.851005e-03 1.123129e-03 -3.362203e-03
#> 126 127 128 129 130
#> -2.206105e-03 7.682669e-04 8.593464e-05 1.053592e-03 3.582294e-04
#> 131 132 133 134 135
#> 1.653224e-03 -1.216428e-02 1.149715e-03 7.533122e-04 1.591714e-03
#> 136 137 138 139 140
#> -9.175706e-04 -3.515552e-03 -8.308748e-04 2.086295e-04 -1.421053e-03
#> 141 142 143 144 145
#> -1.533218e-03 -1.239890e-03 1.608949e-03 -2.904417e-03 -3.752880e-03
#> 146 147 148 149 150
#> -3.482134e-04 1.903139e-03 -2.864917e-04 -2.970967e-03 3.940631e-04