scale()进行数组的标准化，先减去数据集的均值（中心化）再除以数据集的标准差（标准化）。
为了消除量纲对数据结构的影响。
输入：
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2x<-read.table("C:\Users\Administrator\Desktop\R\data.exam5.2.1.txt",header = TRUE) std1.x<-scale(x[2:9]);std1.x
执行结果如下：
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232> std1.x<-scale(x[2:9]);std1.x x1 x2 x3 [1,] 0.13774798 0.103406512 0.19521746 [2,] 1.72088817 2.344698037 1.35836580 [3,] 0.89085539 0.018444199 2.11317155 [4,] -0.87828274 -0.211025441 -0.09165809 [5,] -1.10796789 0.211025441 0.23704337 [6,] -0.83580771 1.792466805 0.01676315 [7,] 0.05553747 -0.012494458 0.48063857 [8,] -0.81424291 0.508417334 0.54860018 [9,] -1.06819604 0.005592757 -0.67642761 [10,] 3.16508751 1.474465006 2.08959214 [11,] 0.50222831 -0.143959952 0.36186002 [12,] 1.67061878 1.836923275 1.01410633 [13,] -0.15760133 -0.541688290 -0.38673134 [14,] 0.94771907 -0.378665365 1.42443224 [15,] -0.39006239 -0.500658871 -0.92213834 [16,] -0.40457226 1.037944352 0.39487120 [17,] -0.97817905 0.156383013 -0.62847545 [18,] -0.07080875 0.125396758 -0.35706976 [19,] -0.32791357 -0.012780045 -0.30144878 [20,] 1.86953864 -0.979113305 2.23419786 [21,] -0.14098225 -1.730589490 -0.59475909 [22,] 0.03912446 -2.700825781 -0.52432936 [23,] 0.19381162 0.718133832 0.13426356 [24,] 0.07969636 -0.335732029 -1.09177792 [25,] -0.47509729 -0.523458281 -0.96669683 [26,] 0.05810731 -0.764541818 -1.16758464 [27,] 0.39046468 -0.667204042 -1.36468211 [28,] -0.54633751 -0.523505879 -0.47033911 [29,] -0.83637744 -0.382615994 -0.74795918 [30,] -0.86179700 -0.579433452 -1.30483006 [31,] -0.91356950 -0.122826469 -0.12035006 [32,] -0.91363011 0.777821642 -0.88586571 x4 x5 x6 [1,] 0.21387591 0.11507170 0.1925790 [2,] 2.52501220 2.99460313 1.7555223 [3,] 1.18114978 2.36500509 0.1161781 [4,] -0.12152612 0.76562569 -0.3657415 [5,] -0.54198196 -0.16984211 -0.3376082 [6,] -0.03037963 0.21205951 -0.1833739 [7,] -0.77630643 0.98617134 -0.3289654 [8,] -0.97084570 0.86866315 -0.5856415 [9,] -1.28571540 0.26248930 -0.7914106 [10,] 2.39316660 0.87984288 3.0836620 [11,] 0.85671457 0.06802970 0.1050407 [12,] 0.60514782 0.88928032 1.9887165 [13,] -0.61545968 -0.58499254 -0.5574760 [14,] 0.47951676 -0.83680272 0.5941224 [15,] 0.12614132 -1.40062909 -0.8124301 [16,] 0.57474535 0.16100056 0.1543061 [17,] -0.10696461 -0.23600096 -0.6106686 [18,] -0.10075008 -0.72592494 -0.5387420 [19,] 0.22228381 -0.03282988 -0.4037568 [20,] 1.74545380 0.37365747 2.7816636 [21,] -0.46101024 -0.64485977 -0.4906997 [22,] -0.28456623 -0.83017232 0.2641677 [23,] 0.85342453 0.35908994 -0.1914695 [24,] -0.02849089 -0.79145463 -0.2620925 [25,] -0.62837616 -1.55254763 -0.5515854 [26,] -1.74297103 -0.21107645 -0.3267926 [27,] -1.79981573 -1.94800044 -0.5977929 [28,] -0.32666665 0.01484128 -0.5971009 [29,] -0.37492068 -0.53751497 -0.6056149 [30,] -0.49951598 -0.30041752 -0.7282555 [31,] -0.52309465 -0.14196537 -0.6095259 [32,] -0.55727459 -0.37039974 -0.5592142 x7 x8 [1,] 0.196917512 0.05270804 [2,] 2.379697737 2.51243056 [3,] 0.839811267 0.94523785 [4,] -0.701399606 -0.71850377 [5,] -0.372389151 -0.89618410 [6,] 0.022944879 0.98340247 [7,] -0.374686161 0.41042765 [8,] -0.488895149 0.36097372 [9,] -0.612105933 -0.34351319 [10,] 2.937829757 3.47404424 [11,] 0.962794419 0.21842506 [12,] 1.912763219 0.97792631 [13,] -0.132465431 -0.35505525 [14,] 0.398019694 0.20671450 [15,] -0.539325898 -0.47890071 [16,] -0.088615305 -0.21739302 [17,] -0.615541101 -0.43180574 [18,] -0.235313533 -0.91514003 [19,] 0.106030417 -0.30012516 [20,] 1.574502643 0.86478043 [21,] -0.380687358 -0.62355558 [22,] -0.819830126 -1.18448285 [23,] 0.006927891 -0.73661722 [24,] -0.418412124 -0.50653426 [25,] -0.409824204 -0.78548142 [26,] -1.093650262 -1.49300128 [27,] -1.640980127 -0.13297591 [28,] -0.006750700 -0.15673401 [29,] -0.362849317 0.01858735 [30,] -0.579699468 -0.16903431 [31,] -0.766998898 -0.41512451 [32,] -0.697819582 -0.16549587 attr(,"scaled:center") x1 x2 x3 x4 3514.3941 1020.2750 937.9866 566.6962 x5 x6 x7 x8 675.3134 1237.7553 1234.0022 351.4438 attr(,"scaled:scale") x1 x2 x3 x4 x5 824.9554 210.0932 226.8928 164.1314 206.6239 x6 x7 x8 621.3277 483.2369 118.6963
执行结果分析：
32组数据，8个变量x1,...,x8
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207x1 x2 x3 [1,] 0.13774798 0.103406512 0.19521746 [2,] 1.72088817 2.344698037 1.35836580 [3,] 0.89085539 0.018444199 2.11317155 [4,] -0.87828274 -0.211025441 -0.09165809 [5,] -1.10796789 0.211025441 0.23704337 [6,] -0.83580771 1.792466805 0.01676315 [7,] 0.05553747 -0.012494458 0.48063857 [8,] -0.81424291 0.508417334 0.54860018 [9,] -1.06819604 0.005592757 -0.67642761 [10,] 3.16508751 1.474465006 2.08959214 [11,] 0.50222831 -0.143959952 0.36186002 [12,] 1.67061878 1.836923275 1.01410633 [13,] -0.15760133 -0.541688290 -0.38673134 [14,] 0.94771907 -0.378665365 1.42443224 [15,] -0.39006239 -0.500658871 -0.92213834 [16,] -0.40457226 1.037944352 0.39487120 [17,] -0.97817905 0.156383013 -0.62847545 [18,] -0.07080875 0.125396758 -0.35706976 [19,] -0.32791357 -0.012780045 -0.30144878 [20,] 1.86953864 -0.979113305 2.23419786 [21,] -0.14098225 -1.730589490 -0.59475909 [22,] 0.03912446 -2.700825781 -0.52432936 [23,] 0.19381162 0.718133832 0.13426356 [24,] 0.07969636 -0.335732029 -1.09177792 [25,] -0.47509729 -0.523458281 -0.96669683 [26,] 0.05810731 -0.764541818 -1.16758464 [27,] 0.39046468 -0.667204042 -1.36468211 [28,] -0.54633751 -0.523505879 -0.47033911 [29,] -0.83637744 -0.382615994 -0.74795918 [30,] -0.86179700 -0.579433452 -1.30483006 [31,] -0.91356950 -0.122826469 -0.12035006 [32,] -0.91363011 0.777821642 -0.88586571 x4 x5 x6 [1,] 0.21387591 0.11507170 0.1925790 [2,] 2.52501220 2.99460313 1.7555223 [3,] 1.18114978 2.36500509 0.1161781 [4,] -0.12152612 0.76562569 -0.3657415 [5,] -0.54198196 -0.16984211 -0.3376082 [6,] -0.03037963 0.21205951 -0.1833739 [7,] -0.77630643 0.98617134 -0.3289654 [8,] -0.97084570 0.86866315 -0.5856415 [9,] -1.28571540 0.26248930 -0.7914106 [10,] 2.39316660 0.87984288 3.0836620 [11,] 0.85671457 0.06802970 0.1050407 [12,] 0.60514782 0.88928032 1.9887165 [13,] -0.61545968 -0.58499254 -0.5574760 [14,] 0.47951676 -0.83680272 0.5941224 [15,] 0.12614132 -1.40062909 -0.8124301 [16,] 0.57474535 0.16100056 0.1543061 [17,] -0.10696461 -0.23600096 -0.6106686 [18,] -0.10075008 -0.72592494 -0.5387420 [19,] 0.22228381 -0.03282988 -0.4037568 [20,] 1.74545380 0.37365747 2.7816636 [21,] -0.46101024 -0.64485977 -0.4906997 [22,] -0.28456623 -0.83017232 0.2641677 [23,] 0.85342453 0.35908994 -0.1914695 [24,] -0.02849089 -0.79145463 -0.2620925 [25,] -0.62837616 -1.55254763 -0.5515854 [26,] -1.74297103 -0.21107645 -0.3267926 [27,] -1.79981573 -1.94800044 -0.5977929 [28,] -0.32666665 0.01484128 -0.5971009 [29,] -0.37492068 -0.53751497 -0.6056149 [30,] -0.49951598 -0.30041752 -0.7282555 [31,] -0.52309465 -0.14196537 -0.6095259 [32,] -0.55727459 -0.37039974 -0.5592142 x7 x8 [1,] 0.196917512 0.05270804 [2,] 2.379697737 2.51243056 [3,] 0.839811267 0.94523785 [4,] -0.701399606 -0.71850377 [5,] -0.372389151 -0.89618410 [6,] 0.022944879 0.98340247 [7,] -0.374686161 0.41042765 [8,] -0.488895149 0.36097372 [9,] -0.612105933 -0.34351319 [10,] 2.937829757 3.47404424 [11,] 0.962794419 0.21842506 [12,] 1.912763219 0.97792631 [13,] -0.132465431 -0.35505525 [14,] 0.398019694 0.20671450 [15,] -0.539325898 -0.47890071 [16,] -0.088615305 -0.21739302 [17,] -0.615541101 -0.43180574 [18,] -0.235313533 -0.91514003 [19,] 0.106030417 -0.30012516 [20,] 1.574502643 0.86478043 [21,] -0.380687358 -0.62355558 [22,] -0.819830126 -1.18448285 [23,] 0.006927891 -0.73661722 [24,] -0.418412124 -0.50653426 [25,] -0.409824204 -0.78548142 [26,] -1.093650262 -1.49300128 [27,] -1.640980127 -0.13297591 [28,] -0.006750700 -0.15673401 [29,] -0.362849317 0.01858735 [30,] -0.579699468 -0.16903431 [31,] -0.766998898 -0.41512451 [32,] -0.697819582 -0.16549587
以上是数据标准化的结果。
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14attr(,"scaled:center") x1 x2 x3 x4 3514.3941 1020.2750 937.9866 566.6962 x5 x6 x7 x8 675.3134 1237.7553 1234.0022 351.4438
数据中心化，数据为每个数据集的平均值。
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11attr(,"scaled:scale") x1 x2 x3 x4 x5 824.9554 210.0932 226.8928 164.1314 206.6239 x6 x7 x8 621.3277 483.2369 118.6963
数据标准化，数据为数据集的标准差。
最后
以上就是怡然河马最近收集整理的关于R语言的scale函数的全部内容，更多相关R语言内容请搜索靠谱客的其他文章。
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