我正在尝试使用cor（）以相关的顺序返回最相关的元素。我写了这个函数，使用cor（）来完成它并且它完美地工作，但只有当我在一个大输入上运行它时。当我尝试在一个小输入上运行它时，我得到缺少值，其中需要TRUE / FALSE 错误，我不明白为什么？
以下是我输入数据的示例：
这可以直接复制到R（通过write.table打印）：
"Col2" "Col3" "Col4" "Col5" "Col6"
"Market Capitalization" NA NA 17082.69 17879.8 16266.11
"Cash & Equivalents" NA NA 747 132 394
"Preferred & Other" NA NA 0 0 0
"Total Debt" NA NA 12379 11982 11309
"Enterprise Value" NA NA 28714.69 29729.8 27181.11
"Total Revenue" 2896.75 3461.25 2818 3184 2901
"Growth % YoY" -0.15 0.68 1.7 3.44 -0.48
"Gross Profit" NA NA 1874 2080 1981
"Margin %" NA NA 66.5 65.33 68.29
"EBITDA" 758 1074 641 777 699
"Margin %1" 26.17 31.03 22.75 24.4 24.1
"Net Income Before XO" 214.5 410 172 192 207
"Margin %2" 7.4 11.85 6.1 6.03 7.14
"Adjusted EPS" 0.7 1.42 0.59 1.07 0.69
"Growth % YoY1" 0.72 -1.67 -3.28 5.94 -6.76
"Cash from Operations" 375.79 812.21 991 -84 961
"Capital Expenditures" NA NA -660 -676 -608
"Free Cash Flow" NA NA 331 -760 353
"Adjusted Price" 2094.66 3689.2 3805.62 3588.42 3582.4
这是我写的mycor（）函数
mycor<-function(dataset, relative.to=19, neg.cor=0){
#This takes the dataset (as a matrix) and computes the best correleted value
#and returns the row (variable ID) that is the most strongly correlated
#to the variable row referenced by relative.to. Use neg.cor = 1 for neg correlation
if(neg.cor == 0){
best.cor <- -1.0 #Have to get better correlation then this
best.cor.row <- integer() #The row with the best correlation
all.cor <- numeric() #The correlation for everything else
index <- 1 #The index for the all.cor array
for(i in 1:nrow(dataset)){
if(i != relative.to){ #No self correlation
temp.cor <- cor(dataset[i,], dataset[relative.to,], use = "na.or.complete")
all.cor[index] <- temp.cor
index <- index+1 #I wish the ++ opperator worked in R...
cat(best.cor)
pause()
if(temp.cor > best.cor){ #This remembers the best seen cor value
best.cor <- temp.cor
best.cor.row <- i
} #End inner if
} #End outter if
} #End for loop
}else{
best.cor <- 1.0 #Have to get better correlation then this
best.cor.row <- integer() #The row with the best correlation
all.cor <- numeric() #The correlation for everything else
index <- 1 #The index for the all.cor array
for(i in 1:nrow(dataset)){
if(i != relative.to){ #No self correlation
temp.cor <- cor(dataset[i,], dataset[relative.to,], use = "na.or.complete")
all.cor[index] <- temp.cor
index <- index+1 #I wish the ++ opperator worked in R...
if(temp.cor < best.cor){ #This remembers the worst seen cor value
best.cor <- temp.cor
best.cor.row <- i
} #End inner if
} #End outter if
} #End for loop
} #End else
return(list(all.cor = all.cor, best.cor.row = best.cor.row))
)
当我尝试运行它时，我得到： if（temp.cor＆gt; best.cor）{：缺少值需要TRUE / FALSE 时出错。关于这个奇怪的部分是mycor功能完美运行，当我给它一个相同数据集的更大块时没有错误。
这是同一数据集的较大块。
这也可以复制到R（通过write.table打印）：
"Col2" "Col3" "Col4" "Col5" "Col6" "Col7" "Col8" "Col9" "Col10" "Col11" "Col12" "Col13" "Col14" "Col15" "Col16" "Col17" "Col18" "Col19" "Col20" "Col21" "Col22" "Col23" "Col24" "Col25" "Col26" "Col27" "Col28" "Col29" "Col30" "Col31" "Col32" "Col33" "Col34" "Col35" "Col36" "Col37" "Col38" "Col39" "Col40" "Col41" "Col42" "Col43" "Col44" "Col45" "Col46" "Col47" "Col48" "Col49" "Col50" "Col51" "Col52" "Col53" "Col54" "Col55" "Col56" "Col57" "Col58" "Col59" "Col60" "Col61" "Col62" "Col63" "Col64" "Col65" "Col66" "Col67" "Col68" "Col69" "Col70" "Col71" "Col72" "Col73" "Col74" "Col75" "Col76" "Col77" "Col78" "Col79" "Col80" "Col81" "Col82" "Col83" "Col84" "Col85" "Col86" "Col87" "Col88" "Col89" "Col90" "Col91" "Col92" "Col93" "Col94" "Col95" "Col96" "Col97" "Col98" "Col99" "Col100" "Col101" "Col102" "Col103" "Col104" "Col105" "Col106" "Col107" "Col108" "Col109" "Col110" "Col111"
"Market Capitalization" NA NA 17082.69 17879.8 16266.11 17540.1 18214.39 17110.13 18167.87 16700.24 15592.71 14824.06 14455.42 13685.56 12168.31 12550.1 12771.45 11273.2 10284.48 10863.21 10655.99 11750.74 10671.37 10818.32 13288.42 12558.8 12221.79 13213.51 12375.92 11854.12 10942.65 10689.79 11364.1 11887.9 11426.1 10249.34 10609.99 10167.51 9600.1 10001.68 9713.38 9184.3 9730.33 8249.64 9160.61 8586.38 8894.55 8908.81 11887.9 11426.1 10249.34 10609.99 10167.51 9600.1 10001.68 9713.38 9184.3 9730.33 8249.64 9160.61 8586.38 8894.55 8908.81 8566.69 8641.04 8444.84 7867.83 8163.04 7238.2 6279.55 6173.33 7376.47 9048.75 10095.35 10351.52 12311.04 12006.02 10785.58 11009.16 9655.09 7990.1 6918.52 7050.24 6844.2 6520.75 6873.11 7489.61 7459.85 7136.58 6930.38 6401.43 6048.8 5843.01 6224.43 6840.76 7529.23 8452.46 8247.48 8132.72 7632.03 7339.11 6549.2 6165.26 6535.8 5793.52 5621.57 5877.31 5391.98 4792.51 5362.35
"Cash & Equivalents" NA NA 747 132 394 69 1381 769 648 398 492 516 338 198 178 87 260 75 311 651 74 68 1757 144 210 192 186 157 94 234 63 177 81 119 818 477 26 70 487 55 49 49 60 62 117.86 83.4 59.2 108.34 119 818 477 26 70 487 55 49 49 60 62 117.86 83.4 59.2 108.34 271.35 432.14 41.63 59.57 94.83 72.81 37.66 73.6 485.05 188.94 291.14 57.5 102.29 153.82 105.01 198.26 183.46 269.87 12.23 94.9 106.88 117.28 57.37 103.23 342.29 429.89 48.49 111.39 245.22 360.74 80.65 205.1 36.76 203.96 143.32 74.33 282.45 349.66 384.84 238.24 317.86 315.65 291.01 185.21 353.33 160.33 160.31
"Preferred & Other" NA NA 0 0 0 0 0 0 213 213 213 213 213 213 213 213 213 213 213 213 213 213 213 257 256 255 255 254 254 254 255 255 255 254 255 255 252 252 253 254 255 221 222 221 221.47 221.13 221.2 220.79 254 255 255 252 252 253 254 255 221 222 221 221.47 221.13 221.2 220.79 222.09 212.56 249.61 212.56 249.61 212.56 212.56 212.56 249.61 212.56 212.56 212.56 249.61 318.02 318.02 318.02 318.02 322.34 322.42 322.54 322.65 322.74 322.77 322.84 639.92 639.98 640.13 640.24 640.31 640.39 640.47 640.54 640.73 640.89 640.95 641.09 641.25 645.87 634.99 635.05 635.18 637.51 637.73 638.05 638.15 640.53 640.77
"Total Debt" NA NA 12379 11982 11309 11111 11873 11073 10675 10676 10678 11144 10683 11526 11020 11027 10599 10773 10366 10699 10094 9751 9480 9363 9282 9213 8653 8943 8815 8968 8487 8162 8205 7687 7868 7498 7219 7245 7336 7432 7094 6968 6682 7000 6841.23 6584.25 6374.14 6264.74 7687 7868 7498 7219 7245 7336 7432 7094 6968 6682 7000 6841.23 6584.25 6374.14 6264.74 6234.03 6249.6 6448.51 6100.6 6011.55 5693.56 5536.13 5276.01 5449.52 4792.08 4881.68 4471.08 4312.4 4410.61 4480.08 4437.33 4758.17 4432.04 4532.28 4466.59 4387.54 4313.86 4316.43 4316.66 4146.02 4175.36 4082.33 4085.09 4089.16 4116.98 3970.11 3972.46 3827.89 3850.12 3927.94 3722.68 3709.36 3804.58 3658.69 3885.52 3667.45 3734.29 3737 3615.16 3492.38 3374.62 3229.81
"Enterprise Value" NA NA 28714.69 29729.8 27181.11 28582.1 28706.39 27414.13 28407.87 27191.24 25991.71 25665.06 25013.42 25226.56 23223.31 23703.1 23323.45 22184.2 20552.48 21124.21 20888.99 21646.74 18607.37 20294.32 22616.42 21834.8 20943.79 22253.51 21350.92 20842.12 19621.65 18929.79 19743.1 19709.9 18731.1 17525.34 18054.99 17594.51 16702.1 17632.68 17013.38 16324.3 16574.33 15408.64 16105.45 15308.35 15430.68 15286 19709.9 18731.1 17525.34 18054.99 17594.51 16702.1 17632.68 17013.38 16324.3 16574.33 15408.64 16105.45 15308.35 15430.68 15286 14751.46 14671.06 15101.34 14121.44 14329.37 13071.51 11990.59 11588.31 12590.55 13864.46 14898.46 14977.66 16770.77 16580.82 15478.67 15566.25 14547.82 12474.62 11760.98 11744.46 11447.51 11040.07 11454.93 12025.88 11903.5 11522.02 11604.35 11015.38 10533.05 10239.65 10754.35 11248.66 11961.09 12739.51 12673.05 12422.15 11700.18 11439.9 10458.04 10447.58 10520.58 9849.67 9705.29 9945.31 9169.17 8647.34 9072.61
"Total Revenue" 2896.75 3461.25 2818 3184 2901 3438 2771 3078 2915 3629 2993 3349 3140 3707 3017 3462 3273 3489 2845 3423 2998 3858 3149 3577 3228 3579 2957 3357 2649 3441 2555 3317 3107 3337 2395 2800 2181 2734 2164 2685 2279 2801 2176 2570 2057.03 2539.49 1848 2056 3337 2395 2800 2181 2734 2164 2685 2279 2801 2176 2570 2057.03 2539.49 1848 2056 1942.6 2627.56 2112.22 2886.26 2250.13 2820.78 2041.89 2318.59 1963.38 2346.24 1479.08 1776.59 1617.34 2061.62 1561.04 1853.05 1720.06 2011.03 1504.01 1886.15 1632.3 1920.34 1539.73 1867.36 1528.38 1879.88 1459.85 1668.79 1461.25 1821.99 1392.09 1697.76 1483.61 1799.69 1396.01 1586.08 1478.81 1717.88 1280.11 1456.11 1342.73 1720.3 1330.65 1479.39 1367.21 1613.83 1263.27
"Growth % YoY" -0.15 0.68 1.7 3.44 -0.48 -5.26 -7.42 -8.09 -7.17 -2.1 -0.8 -3.26 -4.06 6.25 6.05 1.14 9.17 -9.56 -9.65 -4.31 -7.13 7.8 6.49 6.55 21.86 4.01 15.73 1.21 -14.74 3.12 6.68 18.46 42.46 22.06 10.67 4.28 -4.3 -2.39 -0.55 4.47 10.79 10.3 17.75 25 5.89 -3.35 -12.51 -28.77 22.06 10.67 4.28 -4.3 -2.39 -0.55 4.47 10.79 10.3 17.75 25 5.89 -3.35 -12.51 -28.77 -13.67 -6.85 3.44 24.48 14.6 20.23 38.05 30.51 21.4 13.81 -5.25 -4.13 -5.97 2.52 3.79 -1.75 5.38 4.72 -2.32 1.01 6.8 2.15 5.47 11.9 4.59 3.18 4.87 -1.71 -1.51 1.24 -0.28 7.04 0.32 4.76 9.05 8.93 10.13 -0.14 -3.8 -1.57 -1.79 6.6 5.33 -1.02 NA NA NA
"Gross Profit" NA NA 1874 2080 1981 2393 1934 1993 1846 2244 1794 2000 1942 2103 1723 1826 1700 1979 1558 1551 1459 1531 1420 1588 1478 1595 1317 1506 1273 1554 1202 1322 1179 1460 1097 1217 916 1285 980 1169 1066 1349 975 1157 1024.93 1317.57 980 1091 1460 1097 1217 916 1285 980 1169 1066 1349 975 1157 1024.93 1317.57 980 1091 1052.71 1368.8 1091.61 1236.41 991.8 1374.86 1043.29 1236.87 1129.87 1507.31 998.19 1190.69 1151.22 1475.08 1025.84 1170.8 1115.9 1438.56 981.96 1159.37 1094.25 1401.25 1001.2 1198.64 1079.65 1405.45 984.46 1196.22 1086.13 1415.37 998.06 1177.1 1086.53 1381.01 971.41 1118.91 1055.19 1331.37 947.22 1036.88 991.58 1301.1 921.48 994.97 967.89 1217.32 848.39
"Margin %" NA NA 66.5 65.33 68.29 69.6 69.79 64.75 63.33 61.84 59.94 59.72 61.85 56.73 57.11 52.74 51.94 56.72 54.76 45.31 48.67 39.68 45.09 44.39 45.79 44.57 44.54 44.86 48.06 45.16 47.05 39.86 37.95 43.75 45.8 43.46 42 47 45.29 43.54 46.77 48.16 44.81 45.02 49.83 51.88 53.03 53.06 43.75 45.8 43.46 42 47 45.29 43.54 46.77 48.16 44.81 45.02 49.83 51.88 53.03 53.06 54.19 52.09 51.68 42.84 44.08 48.74 51.09 53.35 57.55 64.24 67.49 67.02 71.18 71.55 65.72 63.18 64.88 71.53 65.29 61.47 67.04 72.97 65.02 64.19 70.64 74.76 67.44 71.68 74.33 77.68 71.7 69.33 73.24 76.74 69.58 70.55 71.35 77.5 74 71.21 73.85 75.63 69.25 67.26 70.79 75.43 67.16
"EBITDA" 758 1074 641 777 699 1091 711 794 684 978 617 844 708 916 640 696 625 885 569 611 567 586 520 702 596 715 510 694 547 670 467 564 423 717 411 533 274 624 367 497 458 669 334 485 388.44 693.3 384 487 717 411 533 274 624 367 497 458 669 334 485 388.44 693.3 384 487 445 695.27 439.32 538.75 377.16 666.39 492.65 526.86 446.87 748.34 331.51 492.91 430.87 760.5 313.33 474.78 434.79 751.92 280.96 463.41 390.79 712.97 313.14 490.27 368.26 711.24 307.36 506.85 383.64 721.41 317.3 474.34 363.04 678.27 279.09 400.41 320.03 637.82 281.47 340.21 297.39 610.07 247.48 300.27 305.15 561.67 203.06
"Margin %1" 26.17 31.03 22.75 24.4 24.1 31.73 25.66 25.8 23.46 26.95 20.61 25.2 22.55 24.71 21.21 20.1 19.1 25.37 20 17.85 18.91 15.19 16.51 19.63 18.46 19.98 17.25 20.67 20.65 19.47 18.28 17 13.61 21.49 17.16 19.04 12.56 22.82 16.96 18.51 20.1 23.88 15.35 18.87 18.88 27.3 20.78 23.69 21.49 17.16 19.04 12.56 22.82 16.96 18.51 20.1 23.88 15.35 18.87 18.88 27.3 20.78 23.69 22.91 26.46 20.8 18.67 16.76 23.62 24.13 22.72 22.76 31.9 22.41 27.74 26.64 36.89 20.07 25.62 25.28 37.39 18.68 24.57 23.94 37.13 20.34 26.25 24.09 37.83 21.05 30.37 26.25 39.59 22.79 27.94 24.47 37.69 19.99 25.25 21.64 37.13 21.99 23.36 22.15 35.46 18.6 20.3 22.32 34.8 16.07
"Net Income Before XO" 214.5 410 172 192 207 440 214 280 193 386 168 314 236 353 186 229 205 339 153 183 163 185 283 303 209 313 154 261 205 234 129 183 148 290 121 184 55 253 92 158 50 260 69 157 123.03 286.54 101 169 290 121 184 55 253 92 158 50 260 69 157 123.03 286.54 101 169 128.51 280.74 104.07 182.51 49.48 283.27 72.14 191.53 124.96 339.41 69.8 180.05 135.23 351.55 66.51 176.45 143.61 355.04 47.56 166.61 120.15 327.99 71.42 188.48 113.12 333.3 76.4 201.03 117.88 339.87 87.21 189.31 117.29 324.84 62.45 153.94 100.63 309.44 77.54 116.48 92.2 303.36 64.65 106.7 121.1 263.26 49.06
"Margin %2" 7.4 11.85 6.1 6.03 7.14 12.8 7.72 9.1 6.62 10.64 5.61 9.38 7.52 9.52 6.17 6.61 6.26 9.72 5.38 5.35 5.44 4.8 8.99 8.47 6.47 8.75 5.21 7.77 7.74 6.8 5.05 5.52 4.76 8.69 5.05 6.57 2.52 9.25 4.25 5.88 2.19 9.28 3.17 6.11 5.98 11.28 5.47 8.22 8.69 5.05 6.57 2.52 9.25 4.25 5.88 2.19 9.28 3.17 6.11 5.98 11.28 5.47 8.22 6.62 10.68 4.93 6.32 2.2 10.04 3.53 8.26 6.36 14.47 4.72 10.13 8.36 17.05 4.26 9.52 8.35 17.65 3.16 8.83 7.36 17.08 4.64 10.09 7.4 17.73 5.23 12.05 8.07 18.65 6.26 11.15 7.91 18.05 4.47 9.71 6.8 18.01 6.06 8 6.87 17.63 4.86 7.21 8.86 16.31 3.88
"Adjusted EPS" 0.7 1.42 0.59 1.07 0.69 1.44 0.61 1.01 0.74 1.33 0.57 0.99 0.69 1.32 0.51 0.93 0.67 1.16 0.48 0.78 0.72 0.98 0.42 0.87 0.71 1.2 0.58 1.03 0.78 0.92 0.51 0.86 0.59 1.17 0.48 0.75 0.49 1.08 0.38 0.69 0.65 1.16 0.29 0.72 0.56 1.33 0.46 0.78 1.17 0.48 0.75 0.49 1.08 0.38 0.69 0.65 1.16 0.29 0.72 0.56 1.33 0.46 0.78 0.59 1.3 0.48 0.84 0.52 1.4 0.33 0.88 0.57 1.5 0.3 0.76 0.56 1.49 0.26 0.73 0.59 1.49 0.18 0.69 0.49 1.38 0.28 0.78 0.44 1.38 0.29 0.82 0.47 1.41 0.33 0.77 0.46 1.35 0.23 0.62 0.39 1.3 0.3 0.47 0.36 1.29 0.24 0.43 0.49 1.11 0.18
"Growth % YoY1" 0.72 -1.67 -3.28 5.94 -6.76 8.27 7.02 2.02 7.25 0.76 11.76 6.45 2.99 13.79 6.25 19.23 -6.94 18.37 14.29 -10.34 1.41 -18.33 -27.59 -15.53 -8.97 30.43 13.73 19.77 32.2 -21.37 6.25 14.67 20.41 8.33 26.32 8.7 -24.62 -6.9 31.03 -4.17 16.07 -12.78 -36.96 -7.69 -5.08 2.31 -4.17 -7.14 8.33 26.32 8.7 -24.62 -6.9 31.03 -4.17 16.07 -12.78 -36.96 -7.69 -5.08 2.31 -4.17 -7.14 13.46 -7.14 45.45 -4.55 -8.77 -6.67 10 15.79 1.79 0.67 13.64 4.11 -5.08 -0.07 44.44 5.89 20.41 8.05 -34.72 -11.62 11.36 0 -3.45 -4.88 -6.38 -2.13 -12.12 6.49 2.17 4.44 43.48 24.19 17.95 3.85 -23.33 31.91 8.33 0.78 25 9.3 -26.53 16.22 33.33 -23.21 NA NA NA
"Cash from Operations" 375.79 812.21 991 -84 961 391 845 402 976 572 1227 362 1407 179 794 1 997 26 798 645 581 -1237 733 563 630 109 346 481 710 -162 224 593 177 581 -346 389 525 164 490 152 766 218 492 -58 735.49 285 369 146 581 -346 389 525 164 490 152 766 218 492 -58 735.49 285 369 146 490.18 387.73 254.59 141.41 215.82 279.84 489.5 199.17 -325.31 -66.66 280.22 256.65 718.82 438.66 302.05 244.37 -52.38 647.78 53.19 258.9 294.29 359.1 267.8 184.51 310.07 585.52 233.75 145.31 426.63 480.57 187.86 270.34 236.08 472.92 243.13 69.8 261.19 291.41 285.57 77.33 283.64 328.4 309.68 11.95 357.21 141.59 357.15
"Capital Expenditures" NA NA -660 -676 -608 -478 -635 -523 -542 -503 -629 -460 -599 -548 -551 -465 -719 -531 -595 -529 -785 -584 -608 -547 -638 -519 -485 -482 -583 -480 -537 -420 -619 -385 -426 -390 -431 -439 -308 -373 -448 -356 -404 -317 -593.69 -310 -392 -340 -385 -426 -390 -431 -439 -308 -373 -448 -356 -404 -317 -593.69 -310 -392 -340 -302.22 -394.08 -274.8 -228.02 -75.57 -274.36 -684.94 -207.41 -211.95 -218.98 -157.07 -127.56 -210.59 -156.81 -150.58 -127.3 -226.32 -145.55 -171.37 -140.37 -244.12 -167.92 -185.35 -142.94 -239.55 -165.98 -166.25 -147.38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"Free Cash Flow" NA NA 331 -760 353 -87 210 -121 434 69 598 -98 808 -369 243 -464 278 -505 203 116 -204 -1821 125 16 -8 -410 -139 -1 127 -642 -313 173 -442 196 -772 -1 94 -275 182 -221 318 -138 88 -375 141.79 -25 -23 -194 196 -772 -1 94 -275 182 -221 318 -138 88 -375 141.79 -25 -23 -194 187.96 -6.35 -20.21 -86.61 140.26 5.47 -195.45 -8.24 -537.26 -285.64 123.15 129.09 508.23 281.85 151.46 117.07 -278.7 502.23 -118.18 118.53 50.17 191.18 82.45 41.57 70.51 419.54 67.49 -2.08 426.63 480.57 187.86 270.34 236.08 472.92 243.13 69.8 261.19 291.41 285.57 77.33 283.64 328.4 309.68 11.95 357.21 141.59 357.15
"Adjusted Price" 2094.66 3689.2 3805.62 3588.42 3582.4 3885.75 3523.13 3554.9 3420.27 3141.36 2984.19 2838.81 2760.09 2517.44 2447.56 2403.89 2188.98 1960.8 1952.2 2033.87 2099.97 1993.98 2043.36 2296.42 2201.73 2277.15 2301.5 2203.47 2086.87 1938.95 2019.34 2002.47 2048.12 1881.97 1817.17 1807.02 1664.57 1659.78 1717.25 1585.27 1589.9 1506.13 1534.98 1531.24 1498.21 1528.96 1418.46 1431.1 1343.43 1244.04 1194.62 1076.93 1058.66 960.76 1112.69 1322.69 1414.59 1442.28 1545.6 1364.27 1305.46 1231.15 1022.23 869.37 796.9 820.22 762.84 715.9 756.11 816.37 731.97 705.73 657.84 628.55 571.47 624.67 651.89 676.63 759.77 742.27 734.39 657.44 619.61 569.84 524.2 510.26 475.43 449.8 441.27 409.34 383 413.34 441.72 435.71 419.07 385.87 356.85 346.15 326.97 318.45 323.72 314.18 313.22 300.88 329.3 315.1 312.34 279.11 163.47 NA
较大的块可以很好地工作，但我需要能够检查较小部分的相关性。我是R的新手，所以它可能很简单，但我已经阅读了这里的电路板和r手册，但找不到它。
答案 0 :(得分：3)
在上面的示例中，您的代码在第一个（较小的）数据集上失败，因为第3行仅包含0和NA，因此它的标准偏差为0，因此它与任何其他行的相关性将返回NA，因为计算相关性涉及将样本协方差除以每个向量的样本标准偏差。在大型示例中不会发生这种情况，因为第3行具有足够的变化以具有非零标准偏差。
然而，你的方法似乎有点复杂。如果要计算矩阵中单个行与所有其他行之间的相关性（按相关性排序），则可以在转置矩阵上使用cor()并对结果进行排序，例如：
mycor <- function(dataset, relative.to=19) {
mat <- t(dataset)
cors <- cor(mat, mat[, relative.to], use="na.or.complete")
cors[order(drop(cors)), ]
}
mycor(dataset)