R语言的scale()函数怎么用

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R语言中scale函数，可以对数据进行处理，标准化（归一化）在一定的范围，比较适合大范围变化数据归一化处理从而观察数据变化趋势 scale()函数 scale(x, center = TRUE, scale = TRUE) x一般...

R语言中scale函数，可以对数据进行处理，标准化（归一化）在一定的范围，比较适合大范围变化数据归一化处理从而观察数据变化趋势

scale()函数

scale(x, center = TRUE, scale = TRUE)

x一般是一个矩阵，也可以是一个数值向量

center--是否中心化

scale--是否标准化

1、以数值向量为例：

> A [1]  3.74149  7.36180  5.81734  5.71131  7.97054 10.37620  6.29949  5.55062  5.84779[10] 15.58810 14.76360 17.74670

长度12，scale=T,center=T:

> scaleA=scale(A)> scaleA            [,1] [1,] -1.1123845 [2,] -0.3313828 [3,] -0.6645658 [4,] -0.6874395 [5,] -0.2000606 [6,]  0.3189073 [7,] -0.5605527 [8,] -0.7221048 [9,] -0.6579969[10,]  1.4432593[11,]  1.2653916[12,]  1.9089294attr(,"scaled:center")[1] 8.897915attr(,"scaled:scale")[1] 4.63547>

长度12，scale=T,center=F,数字都是正数

> scaleA=scale(A,center=F)> scaleA           [,1] [1,] 0.3602619 [2,] 0.7088557 [3,] 0.5601421 [4,] 0.5499327 [5,] 0.7674702 [6,] 0.9991073 [7,] 0.6065676 [8,] 0.5344601 [9,] 0.5630741[10,] 1.5009526[11,] 1.4215629[12,] 1.7088007attr(,"scaled:scale")[1] 10.38547

注意：数值不能完全一致，否则返回NaN:

> scale(c(1,1,1,1,1,1))     [,1][1,]  NaN[2,]  NaN[3,]  NaN[4,]  NaN[5,]  NaN[6,]  NaNattr(,"scaled:center")[1] 1attr(,"scaled:scale")[1] 0

2、以数值矩阵为例：计算的是每列的scale结果（第一列数据刚好是上一步演示数据，可对比一下结果）

> dat1                 A       B        C        D          E         F        G        H         I        J         K       LCK-WT-1    3.74149 5.23528 2.821317 118.6600  1.8737693 1.7103460 30.26110  86.6405 1448.6278 173.9960  77.06166 3.19210CK-WT-2    7.36180 2.77070 1.563395 140.1430 16.9090246 0.7802436 33.65711 116.4700 1634.0417  51.0019  98.30970 4.69276CK-WT-3    5.81734 2.66859 1.931628 123.3830  0.9559375 2.7996091 31.46691 111.7380 1566.5626  52.3322 101.42702 3.58136CK-tdr1-1  5.71131 3.22632 3.194809  97.2229  0.4774184 4.7297117 30.96890  82.8809  648.4734  66.9486  46.86340 3.03234CK-tdr1-2  7.97054 1.32105 2.600854  95.2539  0.5273923 4.3637146 28.03340  85.7292  683.4113  41.1148  70.29293 2.11160CK-tdr1-3 10.37620 1.96726 2.301278  91.8525  0.4333881 3.3732144 27.62150  79.6027  647.2750  49.7169  57.09809 3.53808NaWT-1     6.29949 2.40259 2.044360 121.8080 39.1065780 2.2783575 35.59571 106.4650 1248.4062 192.7300 151.37454 4.79151NaWT-2     5.55062 3.23077 2.104095 125.1350 36.5302500 2.8043996 32.99440 111.3370 1117.6042 183.2700 160.54078 4.16132NaWT-3     5.84779 4.80378 2.630611 106.5070 19.4561309 2.9542534 32.77111  98.1677 1191.6926 111.2120 137.35694 3.40994Natdr1-1  15.58810 2.04301 2.289544  81.6997 13.2227038 3.1700429 19.02370  69.4519  501.2779  78.8024 101.08433 6.01932Natdr1-2  14.76360 2.29524 2.801336  84.8495 10.8897780 4.6643058 18.14860  69.7807  395.9033  96.2520  82.21420 5.59169Natdr1-3  17.74670 1.95286 2.450605  80.3895 12.2580100 4.0243357 15.79980  68.8929  468.8953  66.7984 108.79391 8.12127

每列长度12，scale=T,center=T,返回scale后的矩阵

> scaleDat1                   A           B          C           D           E           F             G          H          I           J           KCK-WT-1   -1.1123845  2.06922600  0.9498394  0.65959663 -0.79734415 -1.19085395  0.3345230824 -0.2241247  1.0711933  1.37750741 -0.62155046CK-WT-2   -0.3313828 -0.04789386 -1.8494507  1.74255232  0.30794653 -1.96684043  0.8433687097  1.4659006  1.4799090 -0.82335259 -0.02949209CK-WT-3   -0.6645658 -0.13560824 -1.0300104  0.89768254 -0.86481696 -0.28207930  0.5151965742  1.1978036  1.3311618 -0.79954817  0.05736930CK-tdr1-1 -0.6874395  0.34349216  1.7809813 -0.42104526 -0.89999446  1.32820957  0.4405772141 -0.4371293 -0.6926209 -0.53800188 -1.46299915CK-tdr1-2 -0.2000606 -1.29317006  0.4592366 -0.52030233 -0.89632071  1.02285734  0.0007314704 -0.2757555 -0.6156058 -1.00027265 -0.81015531CK-tdr1-3  0.3189073 -0.73806370 -0.2074192 -0.69176654 -0.90323127  0.19648085 -0.0609863755 -0.6228596 -0.6952627 -0.84634642 -1.17781819NaWT-1    -0.5605527 -0.36410717 -0.7791451  0.81828696  1.93976112 -0.71696069  1.1338423951  0.8990556  0.6298363  1.71273416  1.44911408NaWT-2    -0.7221048  0.34731479 -0.6462152  0.98600067  1.75036684 -0.27808262  0.7440715578  1.1750845  0.3415040  1.54345664  1.70452348NaWT-3    -0.6579969  1.69855951  0.5254554  0.04696522  0.49519263 -0.15305922  0.7106135334  0.4289623  0.5048200  0.25404869  1.05852570Natdr1-1   1.4432593 -0.67299305 -0.2335303 -1.20356808  0.03695307  0.02697441 -1.3492524881 -1.1979651 -1.0170902 -0.32588964  0.04782059Natdr1-2   1.2653916 -0.45632281  0.9053748 -1.04478701 -0.13454792  1.27364125 -1.4803744502 -1.1793365 -1.2493721 -0.01364599 -0.47797937Natdr1-3   1.9089294 -0.75043357  0.1248833 -1.26961512 -0.03396473  0.73971279 -1.8323112232 -1.2296359 -1.0884727 -0.54068956  0.26264141                   LCK-WT-1   -0.7138772CK-WT-2    0.2084474CK-WT-3   -0.4746331CK-tdr1-1 -0.8120677CK-tdr1-2 -1.3779661CK-tdr1-3 -0.5012335NaWT-1     0.2691404NaWT-2    -0.1181823NaWT-3    -0.5799900Natdr1-1   1.0237679Natdr1-2   0.7609411Natdr1-3   2.3156530attr(,"scaled:center")         A          B          C          D          E          F          G          H          I          J          K          L   8.897915   2.826454   2.394486 105.575333  12.720032   3.137711  28.028521  90.596375 962.680951  97.014600  99.368125   4.353607 attr(,"scaled:scale")          A           B           C           D           E           F           G           H           I           J           K   4.6354700   1.1641193   0.4493719  19.8373766  13.6029875   1.1986064   6.6739314  17.6503265 453.6500351  55.8845631  35.8884205           L   1.6270411 >

3、矩阵巨大，或者指定行或者列进行标准化，可以用apply批量进行，譬如：12X2000的矩阵（结构类似上一步）：

> dim(dat2)[1]   12 2000

默认可以直接scale(dat2)获得列结果，返回结果同第二步，如果指定行结果呢？

             CK-WT-1    CK-WT-2    CK-WT-3  CK-tdr1-1  CK-tdr1-2  CK-tdr1-3     NaWT-1     NaWT-2     NaWT-3   Natdr1-1   Natdr1-2   Natdr1-3AT1G01010 -0.2386968 -0.2245197 -0.2270909 -0.2677180 -0.2599348 -0.2392684 -0.2021240 -0.2093897 -0.2163308 -0.1800695 -0.1880828 -0.1720898AT1G01030 -0.2322447 -0.2436961 -0.2411622 -0.2852821 -0.3091559 -0.2995012 -0.2159012 -0.2178163 -0.2205802 -0.2509797 -0.2544241 -0.2586433 AT1G01010  AT1G01030  AT1G01040  AT1G01050  AT1G01060  AT1G01070  AT1G01080  AT1G01090  AT1G01100  AT1G01120 -0.2386968 -0.2322447 -0.2426714  0.2576744 -0.2467641 -0.2474700 -0.1241498  0.1193716  6.0022478  0.4966890

注意指定行，也就是apply(dat2,1,scale)，中1，返回结果将是一个大矩阵，对行进行scale，相当于多次对2000个数据做处理，返回矩阵结构和原矩阵发生了行列转置的情况，也就是说：对行处理的结果，出现在返回值的列中。

譬如经如上处理，第一行的返回值是ScaleDat2_row的第一列，如上展示了其中的前十个，对dat2[1,]处理的返回值前10则如下（二者一致）：

注意事项，直接取第一列scale(dat2[1,]将返回NaN,需要先将其转换成纯数值向量as.numeric

> Row1=dat2[1,]> scale(as.numeric(Row1))[1:10] [1] -0.2386968 -0.2322447 -0.2426714  0.2576744 -0.2467641 -0.2474700 -0.1241498  0.1193716  6.0022478  0.4966890

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