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How to standardize matrix elements in R?
The standardization is the process of converting a value to another value so that the mean of the set of values from which the original value was taken becomes zero and the standard deviation becomes one. To standardize matrix elements, we can use data.Normalization function of clusterSim package but we need to make sure that we set the type argument to n1 because that corresponds to standardization with mean zero and standard deviation 1.
Loading clusterSim package −
library("clusterSim")Example
M1<-matrix(rnorm(25,5,1),ncol=5) M1
output
[,1] [,2] [,3] [,4] [,5] [1,] 5.556224 2.934854 6.239076 4.501244 5.697287 [2,] 5.663404 4.404059 4.458465 2.875686 2.939572 [3,] 4.254188 4.168798 5.716965 5.003396 5.501523 [4,] 4.720976 5.032672 5.511445 4.678973 5.289942 [5,] 2.882521 5.694891 4.996887 4.825759 3.951424
Example
data.Normalization(M1,type="n1")
output
[,1] [,2] [,3] [,4] [,5] [1,] -0.84326235 -1.4331856 0.03959949 0.006214853 -0.6799208 [2,] 0.93062056 0.5714407 -0.31945831 0.065871281 -0.7808809 [3,] -1.18376086 -0.6408459 0.33301120 -0.026702496 -0.6877277 [4,] 1.00673967 0.5514687 -1.40208868 -1.435823004 1.3452576 [5,] 0.08966297 0.9511221 1.34893630 1.390439366 0.8032717
Example
attr(,"normalized:shift")
output
1 2 3 4 5 5.473020 4.598571 5.143872 4.673848 4.880121
Example
attr(,"normalized:scale")
output
1 2 3 4 5 1.0620129 0.9269254 0.9739280 1.2254604 0.9488868
Example
M2<-matrix(rpois(100,10),ncol=10) M2
output
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 15 10 14 15 5 3 11 11 11 7 [2,] 10 8 6 13 4 15 8 6 13 14 [3,] 2 10 5 15 4 10 9 7 6 13 [4,] 12 5 14 11 7 13 8 12 8 7 [5,] 11 11 12 15 10 9 9 12 19 8 [6,] 10 12 8 9 6 12 10 15 11 10 [7,] 13 12 11 9 7 8 17 17 18 13 [8,] 11 8 8 6 10 6 9 8 13 12 [9,] 9 8 10 13 12 14 8 7 4 13 [10,] 7 10 4 7 14 8 10 13 11 11
Example
data.Normalization(M2,type="n1")
output
data.Normalization(M2,type="n1") [,1] [,2] [,3] [,4] [,5] [,6] [1,] -1.9409899 -0.8923761 1.86543426 -1.67507682 -1.1484061 -1.4356319 [2,] 0.9704950 1.5101749 -0.64037295 -0.09481567 -1.6477131 -0.8114441 [3,] 0.3234983 0.1372886 1.03016519 -0.09481567 1.8474359 -0.4993502 [4,] 0.3234983 0.8237318 -0.64037295 -1.04297236 0.3495149 2.3094948 [5,] 0.9704950 0.1372886 0.75174216 0.53728879 -0.1497921 -0.1872563 [6,] -0.3234983 1.5101749 0.19489612 -0.09481567 0.8488219 0.4369314 [7,] -0.6469966 -0.5491545 -1.47564202 -0.41086790 -0.6490991 -0.4993502 [8,] -0.9704950 -1.2355977 -0.08352691 -0.09481567 -0.1497921 0.4369314 [9,] 0.0000000 -0.8923761 -0.91879598 1.80149771 0.3495149 0.1248376 [10,] 1.2939933 -0.5491545 -0.08352691 1.16939325 0.3495149 0.1248376 [,7] [,8] [,9] [,10] [1,] -0.9091373 2.07152663 0.76931647 1.4367622 [2,] 0.6060915 0.74362494 -0.62944075 -0.2535463 [3,] 1.2121831 -1.11543742 0.06993786 0.1690309 [4,] -1.5152288 0.21246427 0.06993786 -0.6761234 [5,] -0.6060915 -0.84985708 -0.27975144 -1.5212777 [6,] -0.3030458 0.47804461 -1.67850865 -0.6761234 [7,] -0.9091373 -0.05311607 0.06993786 0.5916080 [8,] 1.5152288 -0.05311607 0.06993786 1.0141851 [9,] 0.3030458 -0.05311607 2.16807368 1.0141851 [10,] 0.6060915 -1.38101775 -0.62944075 -1.0987005
Example
attr(,"normalized:shift")
output
1 2 3 4 5 6 7 8 9 10 12.0 10.6 10.3 9.3 10.3 10.6 10.0 9.2 8.8 8.6
Example
attr(,"normalized:scale")
output
1 2 3 4 5 6 7 8 3.091206 2.913570 3.591657 3.164034 2.002776 3.204164 3.299832 3.765339 9 10 2.859681 2.366432
Example
M3<-matrix(round(runif(36,2,10),0),ncol=6) M3
output
[,1] [,2] [,3] [,4] [,5] [,6] [1,] 4 9 4 8 7 5 [2,] 8 3 9 7 9 3 [3,] 9 3 8 4 9 4 [4,] 6 10 4 7 3 3 [5,] 7 8 10 9 4 6 [6,] 7 9 6 9 3 7
Example
data.Normalization(M3,type="n1")
output
[,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.8017837 1.4647150 1.5430335 -0.6358384 0.1331559 0.8017837 [2,] -1.3363062 -1.3315591 -0.7715167 -1.1808427 0.9320914 -0.2672612 [3,] -0.8017837 -0.5326236 0.1543033 1.5441789 -0.2663118 -0.8017837 [4,] -0.2672612 -0.5326236 -0.3086067 0.4541703 -1.0652473 -1.3363062 [5,] 0.2672612 0.6657796 -1.2344268 -0.6358384 -1.0652473 0.2672612 [6,] 1.3363062 0.2663118 0.6172134 0.4541703 1.3315591 1.3363062
Example
attr(,"normalized:shift")
output
1 2 3 4 5 6 5.500000 5.333333 4.666667 5.166667 6.666667 6.500000
Example
attr(,"normalized:scale")
output
1 2 3 4 5 6 1.870829 2.503331 2.160247 1.834848 2.503331 1.870829
Example
M4<-matrix(rexp(16,0.50),nrow=4) M4
output
[,1] [,2] [,3] [,4] [1,] 1.8392684 0.1260047 1.8536475 0.3727895 [2,] 2.3926115 2.9282159 0.5356917 0.6675259 [3,] 0.6198705 5.3994087 0.7795360 1.6238094 [4,] 3.9293381 0.6119497 0.8212652 0.6498672
Example
data.Normalization(M4,type="n1")
output
[,1] [,2] [,3] [,4] [1,] -0.76247841 0.6334982 0.2251928 1.2625561 [2,] -0.08745082 -1.0914747 -0.8013569 -0.6296948 [3,] 1.43733539 -0.5780098 -0.7465997 -0.9525044 [4,] -0.58740616 1.0359864 1.3227638 0.3196432
Example
attr(,"normalized:shift")
output
1 2 3 4 1.587821 1.762592 2.272075 3.611091
Example
attr(,"normalized:scale")
output
1 2 3 4 0.4923935 1.5823407 2.2370054 1.3130271
Updated on: 14-Oct-2020
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