如何确定R中向量值的百分位数？
百分位数可帮助我们确定数据集中处于某个百分比的值。例如，如果我们有一个大小为100的向量，其中包含任何值，并且假设向量的十分位数为25，则意味着该向量中有百分之十的值小于25，或者可以说，是向量中大于25的百分之九十的值。我们可以使用R中的分位数函数找到向量值的百分位数。
例子
> x1<-rpois(100,5) > x1
输出结果
[1] 7 1 7 6 6 5 3 1 5 5 4 5 6 2 4 7 7 3 4 7 7 4 3 6 2 [26] 1 14 1 4 8 6 4 6 7 4 5 6 6 6 10 4 2 5 3 5 4 10 4 3 6 [51] 3 9 5 3 6 10 6 4 4 7 3 3 8 4 4 7 3 1 4 5 4 4 7 5 5 [76] 9 7 3 4 10 5 6 4 7 5 8 3 4 2 3 10 4 6 7 2 3 4 2 2 11
> quantile(x1,probs=seq(0,1,by=0.01))
输出结果
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 1.00 1.00 1.00 1.00 1.00 1.95 2.00 2.00 2.00 2.00 2.00 2.00 2.88 13% 14% 15% 16% 17% 18% 19% 20% 21% 22% 23% 24% 25% 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 26% 27% 28% 29% 30% 31% 32% 33% 34% 35% 36% 37% 38% 3.74 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 39% 40% 41% 42% 43% 44% 45% 46% 47% 48% 49% 50% 51% 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.52 5.00 5.00 5.00 52% 53% 54% 55% 56% 57% 58% 59% 60% 61% 62% 63% 64% 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.39 6.00 6.00 6.00 65% 66% 67% 68% 69% 70% 71% 72% 73% 74% 75% 76% 77% 6.00 6.00 6.00 6.00 6.00 6.00 6.00 6.00 6.00 6.00 6.25 7.00 7.00 78% 79% 80% 81% 82% 83% 84% 85% 86% 87% 88% 89% 90% 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.12 8.00 8.00 91% 92% 93% 94% 95% 96% 97% 98% 99% 100% 8.09 9.00 9.07 10.00 10.00 10.00 10.00 10.02 11.03 14.00
> quantile(x1,probs=seq(0,1,by=0.02))
输出结果
0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 22% 24% 1.00 1.00 1.00 2.00 2.00 2.00 2.88 3.00 3.00 3.00 3.00 3.00 3.00 26% 28% 30% 32% 34% 36% 38% 40% 42% 44% 46% 48% 50% 3.74 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.52 5.00 52% 54% 56% 58% 60% 62% 64% 66% 68% 70% 72% 74% 76% 5.00 5.00 5.00 5.00 5.00 6.00 6.00 6.00 6.00 6.00 6.00 6.00 7.00 78% 80% 82% 84% 86% 88% 90% 92% 94% 96% 98% 100% 7.00 7.00 7.00 7.00 7.00 7.12 8.00 9.00 10.00 10.00 10.02 14.00
> quantile(x1,probs=seq(0,1,by=0.05))
输出结果
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 1.00 1.95 2.00 3.00 3.00 3.00 4.00 4.00 4.00 4.00 5.00 5.00 5.00 65% 70% 75% 80% 85% 90% 95% 100% 6.00 6.00 6.25 7.00 7.00 8.00 10.00 14.00
> quantile(x1,probs=seq(0,1,by=0.10))
输出结果
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 2 3 4 4 5 5 6 7 8 14
> quantile(x1,probs=seq(0,1,by=0.25))
输出结果
0% 25% 50% 75% 100% 1.00 3.00 5.00 6.25 14.00
示例
> x2<-sample(1:1000,100) > x2
输出结果
[1] 315 259 494 865 760 289 48 331 100 108 301 10 170 280 348 402 209 468 [19] 827 649 309 395 991 8 626 261 541 306 326 74 282 585 267 887 262 736 [37] 204 723 219 696 352 667 119 452 856 924 579 622 936 646 36 55 490 240 [55] 891 632 862 304 989 665 422 612 105 793 388 463 180 278 373 241 24 679 [73] 559 703 37 686 566 303 719 912 19 712 671 378 549 615 244 994 188 464 [91] 393 139 299 371 670 189 311 905 418 569
> quantile(x2,probs=seq(0,1,by=0.01))
输出结果
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 8.00 9.98 18.82 23.85 35.52 36.95 47.34 54.51 72.48 97.66 104.50 11% 12% 13% 14% 15% 16% 17% 18% 19% 20% 21% 107.67 117.68 136.40 165.66 178.50 186.72 188.83 201.30 208.05 217.00 235.59 22% 23% 24% 25% 26% 27% 28% 29% 30% 31% 32% 240.78 243.31 255.40 260.50 261.74 265.65 274.92 279.42 281.40 286.83 295.80 33% 34% 35% 36% 37% 38% 39% 40% 41% 42% 43% 300.34 302.32 303.65 305.28 307.89 310.24 313.44 321.60 328.95 340.86 350.28 44% 45% 46% 47% 48% 49% 50% 51% 52% 53% 54% 362.64 372.10 375.70 383.30 390.60 394.02 398.50 409.84 419.92 436.10 457.06 55% 56% 57% 58% 59% 60% 61% 62% 63% 64% 65% 463.45 465.76 477.46 491.68 513.27 544.20 552.90 561.66 567.11 572.60 581.10 66% 67% 68% 69% 70% 71% 72% 73% 74% 75% 76% 594.18 612.99 617.24 623.24 627.80 636.06 646.84 653.32 665.52 667.75 670.24 77% 78% 79% 80% 81% 82% 83% 84% 85% 86% 87% 672.84 680.54 688.10 697.40 704.71 713.26 719.68 725.08 739.60 764.62 797.42 88% 89% 90% 91% 92% 93% 94% 95% 96% 97% 98% 830.48 856.66 862.30 866.98 887.32 891.98 905.42 912.60 924.48 937.59 989.04 99% 100% 991.03 994.00
> quantile(x2,probs=seq(0,1,by=0.05))
输出结果
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 8.00 36.95 104.50 178.50 217.00 260.50 281.40 303.65 321.60 372.10 398.50 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 463.45 544.20 581.10 627.80 667.75 697.40 739.60 862.30 912.60 994.00
> quantile(x2,probs=seq(0,1,by=0.25))
输出结果
0% 25% 50% 75% 100% 8.00 260.50 398.50 667.75 994.00