Question

我的目标：我每5行计算一次xts列的曲线下面积。

我的目标：我想知道R中是否有一个函数可以更快地执行它（向量化它），而不是从myxts5的列中生成一个向量，然后循环计算。

谢谢你的帮助。

我已经包含了我的代码，输出和数据集：

#create an xts object
library(xts)
myxts5<-xts(dat5[,2:7],order.by=as.POSIXct(dat5[,1]))
colnames(myxts5)<-c("Open","High","Low","Close","Volume","RSI_10")

#make the data reproducible
dput(myxts5,file='so4.txt')

#my method to calculate the entire area under the RSI
library(MESS)

y1<-as.vector(myxts5[11:nrow(myxts5),"RSI_10"]) #remove the first 10 NAs
x1<-1:length(y1)

AUC_RSI10<-NA
for(i in 1:(length(y1)-4))
{
  AUC_RSI10[i]<-auc(x1,y1,from=i,to=i+4,type="spline") 

}

#output the result
dput(AUC_RSI10,file='so5.txt')

这是我的输出：

c(212.42031469304, 212.798819901101, 209.986805467201, 204.085562604063, 
197.984367949833, 190.26277666882, 186.077746687353, 175.748595915665, 
153.079882459862, 130.009179454897, 102.067828644029, 93.1546072252816, 
106.253169598421, 126.695279601823, 156.84779427326, 171.19433284721, 
177.724612560769, 176.407343545939, 180.185730377015, 185.540559407078, 
181.492038514392, 189.691635230233, 184.490969841544, 188.753967108042, 
207.705900307578, 214.048826298562, 218.30216750124, 212.29968278624, 
199.551248663318, 188.776300102749, 182.52612752936, 181.256931078184, 
186.756042540598, 192.301043062924, 192.527605116982, 194.728277897872, 
189.022862111989, 184.468524998165, 185.39881870675, 196.154701535433, 
206.890484681265, 207.327144844833, 212.703798768185, 198.013450923081, 
181.641656558781, 176.54287628256, 164.743975467614, 162.034403175174, 
162.412236153867, 162.309298857098, 166.286729859961, 176.547050171716, 
192.181072883591, 205.515617818054, 218.07534078917, 218.140824515946, 
205.641441980933, 201.576989476802, 196.867549251374, 190.783357752807, 
197.903201932701, 202.879574501878, 206.106624821193, 220.374961483055, 
227.474013530533, 223.343713145521, 229.246256446896, 243.286328522227, 
262.117443779325, 263.840756142047, 240.806125975743, 215.212025274733, 
187.537410808637, 181.697615578155, 189.007912680354, 184.797339348203, 
181.36323163363, 180.650062530952, 179.317002557559, 185.586705150491, 
186.662076288898, 188.57947064012, 203.125520580371, 203.930450146523, 
203.495197679754, 202.028430483861, 194.680742214661, 193.31866654904, 
191.777680494373, 191.023982678201, 188.354726404744, 188.21830097328, 
195.985546556535, 203.55439816918, 208.861239277293, 213.888773137499, 
214.806015837923, 214.330645702585, 213.514904083631, 226.469183651925, 
236.327655260894, 237.566101775243, 252.190727914684, 243.354777400258, 
231.51372836124, 229.798793976413, 221.107200373181, 217.996862355506, 
211.122748333001, 210.582316625607, 208.01205675632, 209.26953269887, 
225.397380909908, 230.483506508238, 227.694726952225, 224.967853799347, 
218.724867445519, 216.905971862255, 221.52922790949, 225.631736902592, 
230.891924036962, 241.177062846485, 249.246022464813, 254.246246799188, 
256.317954002037, 247.643366520204, 238.622153789957, 227.473438159439, 
214.560573157924, 212.790548936815, 208.420352726885, 208.640044465686, 
214.931993510179, 212.72755394672, 219.410868549475, 233.687119760904, 
246.692220779985, 264.374607939455, 269.70827693888, 268.536133748541, 
272.85747899862, 274.53970011536, 283.600460826417, 295.875094542883, 
302.745632060615, 309.701548644739, 319.260316467242, 326.643036235032, 
332.342520658862, 338.413750490865, 343.136490698001, 348.603529563524, 
352.398026008696, 357.650124476616, 358.743285419066, 344.637128752378, 
324.347614348694, 296.899987957071, 260.178562036282, 235.165894610707, 
218.512708871952, 201.615881861806, 204.662806854938, 214.207761875216, 
221.261680991827, 236.578968494141, 248.278613344013, 258.043801399754, 
274.96482029892, 286.236076785085, 285.602545656925, 285.686696242866, 
283.210913624938, 287.152216650458, 295.150088828741, 299.214294408208, 
304.574243471004, 307.788231278327, 313.280310293549, 319.207810872882, 
318.414126071909, 305.570556389567, 290.312583077288, 286.828481019533, 
284.595391529875, 289.607238027879, 298.301134488251, 292.448822019436, 
286.489056604544, 279.206214787399)

以下是数据集：

    structure(c(3, 3, 3.24, 3.25, 3.35, 3.3, 3.19, 3.15, 3.2, 3.24, 
3.24, 3.239, 3.24, 3.24, 3.24, 3.24, 3.24, 3.23, 3.19, 3.19, 
3.18, 3.25, 3.05, 2.95, 2.95, 2.94, 2.96, 3, 3.05, 3, 3, 3.1, 
3.15, 3.15, 2.99, 3.04, 3, 3.2, 3.18, 3.18, 3.15, 3.02, 3, 2.99, 
2.99, 3, 3.1, 3.05, 2.99, 3, 3, 3, 3, 3.1, 3.2, 2.98, 2.85, 3, 
2.9, 2.88, 2.85, 2.86, 2.86, 2.9, 2.9, 3, 3, 3, 3, 2.9, 2.91, 
3, 3, 2.92, 3, 3, 3, 3.05, 3, 3, 3, 3.15, 3.3, 3.3, 2.9, 2.95, 
2.99, 2.95, 2.91, 2.9, 2.9, 2.92, 2.99, 3, 2.95, 3.1, 3.25, 2.95, 
2.92, 2.92, 2.95, 2.92, 2.9, 2.93, 2.93, 2.91, 3, 2.94, 2.99, 
3.05, 2.99, 2.92, 3, 3, 3.2, 3.15, 3.21, 3.24, 3.05, 3.1, 3.1, 
3.15, 3.01, 3.01, 3.15, 3.15, 3.25, 3.2, 3.23, 3.22, 3.19, 3.24, 
3.24, 3.2, 3.24, 3.4, 3.35, 3.35, 3.35, 3.33, 3.3, 3.3, 3.26, 
3.28, 3.27, 3.3, 3.3, 3.3, 3.39, 3.39, 3.5, 3.4, 3.45, 3.45, 
3.54, 3.59, 3.55, 3.65, 3.68, 3.65, 3.72, 3.79, 3.84, 3.88, 3.9, 
3.95, 3.95, 3.95, 4.05, 4.04, 3.97, 3.95, 3.89, 3.86, 3.8, 3.81, 
3.95, 3.9, 3.95, 3.97, 4.05, 4.1, 4.13, 4.3, 4.2, 4.23, 4.29, 
4.45, 4.48, 4.49, 4.57, 4.68, 4.7, 4.71, 4.77, 4.67, 4.65, 4.74, 
5.16, 4.96, 5.26, 5.16, 5.27, 5.19, 3.2, 3.2, 3.25, 3.4, 3.35, 
3.3, 3.19, 3.2, 3.2, 3.24, 3.24, 3.24, 3.24, 3.24, 3.24, 3.24, 
3.24, 3.24, 3.19, 3.19, 3.18, 3.25, 3.05, 2.95, 2.95, 2.99, 2.96, 
3.1, 3.05, 3, 3, 3.1, 3.15, 3.15, 2.99, 3.04, 3, 3.2, 3.18, 3.18, 
3.15, 3.05, 3, 3.03, 2.99, 3, 3.1, 3.05, 2.99, 3, 3, 3, 3, 3.2, 
3.2, 3.05, 3.05, 3, 2.9, 2.88, 2.85, 2.86, 2.86, 2.9, 2.9, 3, 
3, 3, 3, 2.92, 2.92, 3, 3, 2.92, 3, 3, 3, 3.05, 3, 3, 3.15, 3.29, 
3.49, 3.3, 2.9, 2.99, 2.99, 2.95, 2.91, 2.9, 2.95, 2.92, 2.99, 
3, 2.95, 3.1, 3.25, 2.95, 2.95, 2.95, 2.99, 2.95, 2.9, 2.93, 
2.93, 2.95, 3, 2.99, 2.99, 3.05, 2.99, 2.99, 3, 3.2, 3.24, 3.2, 
3.25, 3.24, 3.1, 3.1, 3.23, 3.15, 3.01, 3.3, 3.15, 3.25, 3.28, 
3.25, 3.23, 3.22, 3.29, 3.24, 3.24, 3.24, 3.4, 3.4, 3.4, 3.35, 
3.35, 3.33, 3.3, 3.3, 3.28, 3.31, 3.31, 3.3, 3.3, 3.3, 3.39, 
3.54, 3.5, 3.47, 3.47, 3.54, 3.6, 3.59, 3.69, 3.75, 3.69, 3.75, 
3.8, 3.84, 3.89, 3.93, 3.95, 3.95, 3.95, 4.1, 4.15, 4.1, 3.97, 
3.95, 3.92, 3.86, 3.85, 3.81, 3.95, 4, 3.97, 3.99, 4.1, 4.19, 
4.33, 4.33, 4.31, 4.3, 4.46, 4.49, 4.54, 4.65, 4.72, 4.72, 4.79, 
4.79, 4.77, 4.7, 4.71, 5.17, 5.3, 5.39, 5.39, 5.39, 5.3, 5.19, 
3, 3, 3.24, 3.25, 3.25, 3.1, 3.19, 3.15, 3.2, 3.24, 3.24, 3.239, 
3.24, 3.24, 3.24, 3.24, 3.2, 3.2, 3.19, 3.18, 3.18, 3.05, 2.6, 
2.95, 2.85, 2.94, 2.95, 3, 3, 3, 3, 3, 3.05, 2.82, 2.99, 3.04, 
2.95, 3.2, 3.18, 3.15, 3.02, 3, 3, 2.99, 2.95, 3, 3.1, 2.99, 
2.99, 3, 2.98, 3, 3, 3.1, 2.98, 2.8, 2.85, 2.85, 2.89, 2.76, 
2.85, 2.86, 2.85, 2.88, 2.9, 3, 3, 3, 3, 2.9, 2.91, 3, 2.92, 
2.92, 3, 3, 3, 3.05, 3, 2.85, 3, 3.15, 3.26, 2.8, 2.9, 2.95, 
2.99, 2.95, 2.86, 2.9, 2.89, 2.92, 2.9, 3, 2.85, 3.1, 3.05, 2.9, 
2.9, 2.9, 2.95, 2.85, 2.9, 2.93, 2.93, 2.91, 3, 2.92, 2.99, 2.99, 
2.99, 2.92, 3, 3, 3.01, 3.15, 3.02, 3.05, 3.05, 3.1, 3.1, 3, 
3.01, 3.01, 3.1, 3.15, 3.25, 3.18, 3.16, 3.19, 3.13, 3.24, 3.24, 
3.2, 3.24, 3.4, 3.35, 3.35, 3.33, 3.3, 3.3, 3.22, 3.22, 3.28, 
3.25, 3.3, 3.3, 3.3, 3.39, 3.35, 3.35, 3.38, 3.41, 3.43, 3.45, 
3.45, 3.55, 3.6, 3.65, 3.65, 3.67, 3.78, 3.8, 3.86, 3.9, 3.9, 
3.85, 3.93, 4.02, 3.97, 3.93, 3.82, 3.8, 3.8, 3.8, 3.8, 3.95, 
3.85, 3.95, 3.97, 4.05, 4.06, 4.13, 4.16, 4.2, 4.22, 4.29, 4.35, 
4.43, 4.49, 4.51, 4.65, 4.66, 4.71, 4.68, 4.6, 4.6, 4.74, 5, 
4.91, 5.16, 5.14, 5.17, 5.19, 3.2, 3.2, 3.25, 3.4, 3.25, 3.1, 
3.19, 3.2, 3.2, 3.24, 3.24, 3.24, 3.24, 3.24, 3.24, 3.24, 3.2, 
3.2, 3.19, 3.19, 3.18, 3.05, 2.95, 2.95, 2.85, 2.99, 2.95, 3.1, 
3, 3, 3, 3, 3.15, 2.85, 2.99, 3.04, 2.97, 3.2, 3.18, 3.15, 3.02, 
3, 3, 2.99, 2.98, 3, 3.1, 2.99, 2.99, 3, 2.98, 3, 3, 3.2, 2.98, 
3.05, 3.015, 2.85, 2.9, 2.86, 2.85, 2.86, 2.86, 2.88, 2.9, 3, 
3, 3, 3, 2.92, 2.92, 3, 2.92, 2.92, 3, 3, 3, 3.05, 3, 3, 3.15, 
3.29, 3.26, 2.8, 2.9, 2.99, 2.99, 2.95, 2.9, 2.9, 2.95, 2.92, 
2.9, 3, 2.85, 3.1, 3.05, 2.92, 2.92, 2.95, 2.99, 2.9, 2.9, 2.93, 
2.93, 2.95, 3, 2.99, 2.99, 2.99, 2.99, 2.99, 3, 3.2, 3.01, 3.2, 
3.24, 3.05, 3.1, 3.1, 3.23, 3, 3.01, 3.2, 3.14, 3.25, 3.28, 3.2, 
3.16, 3.19, 3.2, 3.24, 3.24, 3.24, 3.31, 3.4, 3.35, 3.35, 3.33, 
3.3, 3.3, 3.22, 3.28, 3.31, 3.25, 3.3, 3.3, 3.3, 3.39, 3.42, 
3.47, 3.47, 3.41, 3.54, 3.58, 3.59, 3.6, 3.69, 3.69, 3.74, 3.8, 
3.8, 3.85, 3.86, 3.95, 3.95, 3.95, 4.1, 4.05, 3.97, 3.96, 3.89, 
3.8, 3.85, 3.81, 3.8, 3.95, 3.91, 3.97, 3.99, 4.1, 4.1, 4.33, 
4.2, 4.25, 4.3, 4.45, 4.48, 4.43, 4.57, 4.68, 4.72, 4.7, 4.76, 
4.7, 4.6, 4.71, 5.16, 5, 5.29, 5.16, 5.3, 5.19, 5.19, 8042, 7247, 
1500, 2200, 1600, 1983, 1360, 400, 1500, 4800, 400, 1900, 450, 
500, 1600, 25, 9190, 2685, 100, 2450, 2100, 14824, 12366, 400, 
550, 7701, 4000, 2000, 2200, 1000, 400, 192, 1800, 9825, 776, 
500, 5050, 2100, 1000, 526, 5262, 13470, 150, 5000, 825, 550, 
1000, 1000, 835, 1000, 800, 1500, 2000, 6000, 23716, 2510, 5073, 
4667, 5332, 6600, 11000, 1000, 4500, 5400, 500, 1000, 14260, 
600, 230, 4500, 1500, 1200, 5372, 500, 6128, 1000, 3500, 500, 
782, 1318, 2650, 9856, 15240, 9280, 100, 3500, 1000, 500, 9900, 
2000, 1073, 3000, 1650, 3000, 5500, 1000, 7120, 3300, 8200, 3600, 
630, 12800, 3800, 1500, 5000, 12000, 1433, 8450, 1500, 1420, 
1000, 1088, 510, 3390, 15482, 850, 9900, 3240, 4860, 661, 4405, 
21850, 675, 12475, 10030, 4747, 6449, 2771, 2400, 1900, 26130, 
75, 1000, 20360, 18704, 1100, 6750, 910, 440, 4745, 1115, 12415, 
1892, 1000, 4000, 100, 3200, 4000, 1000, 14715, 18365, 8100, 
13375, 21693, 34433, 14629, 32341, 26850, 16369, 14310, 12150, 
16731, 21046, 11623, 9380, 8760, 10850, 21129, 20312, 14727, 
7393, 4879, 16760, 6538, 15113, 1200, 1500, 23771, 9575, 8150, 
7774, 20177, 26701, 18467, 14485, 16184, 16318, 10731, 15647, 
33617, 9598, 10061, 31855, 13082, 8919, 9716, 17170, 120717, 
99373, 48754, 16061, 40634, 71196, 1550, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, 53.125, 53.125, 53.125, 53.125, 53.125, 53.125, 
47.5346930503198, 47.5346930503198, 46.0394165349133, 46.0394165349133, 
44.318305280762, 28.7784405800004, 22.1424718838612, 22.1424718838612, 
17.2358309419688, 38.4516614314786, 35.5580201252693, 50.9409073335208, 
43.2868577214891, 43.2868577214891, 43.2868577214891, 43.2868577214891, 
57.7874913716085, 36.8498892395159, 46.8375224558739, 49.9770019079572, 
45.7722513840718, 58.5146700229585, 57.2156823579705, 55.1742932408986, 
47.0855992829417, 45.9344690510984, 45.9344690510985, 45.2515723462439, 
44.5162256558408, 46.449998432825, 55.1367545352793, 46.0137841205261, 
46.0137841205261, 46.9980428383782, 45.1680669242794, 47.441920921326, 
47.441920921326, 65.2386620558215, 46.142307043277, 51.193099386028, 
48.6580081770167, 38.6361289882013, 42.6156590089499, 40.2929460812456, 
39.692011854992, 40.6750999191946, 40.6750999191946, 42.9705189551315, 
45.3212400820865, 55.5094323592483, 55.5094323592483, 55.5094323592483, 
55.5094323592483, 45.2327792238372, 45.2327792238372, 55.4216085337574, 
45.9278679630622, 45.9278679630622, 55.3669503461937, 55.3669503461936, 
55.3669503461936, 61.1772096913823, 53.4465567305418, 53.4465567305418, 
68.2882384971981, 76.1676317292872, 71.9132908833502, 36.8483156083071, 
43.5024785084454, 48.8880404068233, 48.8880404068233, 46.4581004766477, 
43.4580365276188, 43.4580365276188, 47.6329025895786, 45.3982034143251, 
43.8735164139756, 52.6990228922181, 41.7559573740936, 57.9325988310753, 
54.5648674809673, 46.7190334585342, 46.7190334585342, 48.8158219705131, 
51.6355364603515, 45.3849436005241, 45.3849436005241, 47.9765292549334, 
47.9765292549334, 49.9319385680271, 54.6652697441737, 53.5404685492423, 
53.5404685492423, 53.5404685492423, 53.5404685492423, 53.5404685492423, 
55.1048795718158, 74.3203906677712, 51.1923456182049, 63.7325679700046, 
65.7886989290019, 50.6372228137231, 53.7516204585925, 53.7516204585925, 
61.540372521279, 46.233933197312, 46.8722919081336, 57.5199212894259, 
53.7407977117634, 59.2011388495268, 60.6100798191561, 54.9839832912961, 
52.2875592667179, 54.1608587608694, 54.8178785295968, 57.5237409247848, 
57.5237409247849, 57.5237409247849, 62.8627359282454, 68.5160644996568, 
62.6307841423264, 62.6307841423264, 60.0822193137844, 56.2662526029152, 
56.2662526029152, 46.5358654620614, 53.2702015941206, 56.3263815242224, 
49.17892489837, 54.5222862433549, 54.5222862433549, 54.5222862433549, 
63.8953027289306, 66.4558554034754, 70.3499117941237, 70.3499117941237, 
60.026469332036, 70.4615938291669, 72.8818396542941, 73.4853107348059, 
74.125093473292, 79.1548876794736, 79.1548876794736, 81.6071301038229, 
84.1009697961463, 84.1009697961463, 86.0472783823051, 86.4167923755497, 
89.2608680025719, 89.2608680025719, 89.2608680025719, 92.7374320331654, 
82.8087109204784, 69.567643114619, 68.0562327567284, 58.2185653891826, 
48.2539781801277, 53.1986881679786, 49.033768692775, 47.9901183825881, 
61.6089181051025, 57.1731265162146, 61.7616734481797, 63.221137287655, 
70.1771943992812, 70.1771943992812, 79.9607457795924, 66.3010390829917, 
68.5938260901332, 70.8011706594854, 76.343417368729, 77.3008696102273, 
71.911133672881, 76.9180575079281, 80.0263157494416, 81.0569834689864, 
78.7980499036461, 80.600192632844, 73.6449272697599, 63.4978113677881, 
68.7589068199776, 81.1248114717251, 70.1544736530449, 76.542694603108, 
69.1685415119339, 72.3553875493797, 66.3666023398198, 66.3666023398197
), .Dim = c(204L, 6L), .Dimnames = list(NULL, c("Open", "High", 
"Low", "Close", "Volume", "RSI_10")), index = structure(c(1391794931, 
1391796453, 1391798428, 1391799253, 1391800289, 1391801398, 1391801991, 
1391802911, 1391804252, 1391805482, 1391806437, 1391807621, 1391809180, 
1391810164, 1391811085, 1391811953, 1391812974, 1391813874, 1391814356, 
1391815389, 1391816676, 1392054295, 1392055042, 1392055641, 1392056480, 
1392057860, 1392058660, 1392061125, 1392061818, 1392062871, 1392064406, 
1392069454, 1392071161, 1392072828, 1392073926, 1392074435, 1392075588, 
1392139818, 1392141787, 1392143229, 1392144209, 1392144981, 1392145281, 
1392146999, 1392147895, 1392150775, 1392153349, 1392157752, 1392157949, 
1392161101, 1392162668, 1392223583, 1392225001, 1392226078, 1392227071, 
1392227853, 1392228856, 1392229625, 1392232022, 1392232952, 1392234128, 
1392235169, 1392235962, 1392237413, 1392238825, 1392247565, 1392248355, 
1392248933, 1392312896, 1392314399, 1392314919, 1392318896, 1392319091, 
1392324415, 1392328718, 1392328923, 1392330638, 1392332327, 1392333020, 
1392334866, 1392399762, 1392400798, 1392401577, 1392402571, 1392403101, 
1392405234, 1392405314, 1392407097, 1392407384, 1392412536, 1392417358, 
1392418366, 1392419602, 1392421312, 1392421904, 1392744561, 1392744882, 
1392746369, 1392747095, 1392748082, 1392748953, 1392749704, 1392750668, 
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1393024498, 1393025388, 1393026292, 1393027175, 1393027473), tzone = "", tclass = c("POSIXct", 
"POSIXt")), .indexCLASS = c("POSIXct", "POSIXt"), tclass = c("POSIXct", 
"POSIXt"), .indexTZ = "", tzone = "", class = c("xts", "zoo"))

Answer 1

首先，我们为整个数据集生成样条线。其次，我们使用此函数同时插入中点（因此进行矢量化）。使用这些和初始函数值，我们为每个区间形成Simpson规则，得到样条的精确积分。最后，我们使用快速滚动功能为所有组合间隔添加这些部分和。

spl <- splinefun(x1, y1) # cubic spline
n <- length(x1)
xmid <- (x1[-1] + x1[-n]) / 2 # midpoints
# Simpson's rule
sums <- diff(x1)/6 * (y1[-1] + 4*spl(xmid) + y1[-n])

library(RcppRoll)
areas <- roll_sum(sums, 4)
# check accuracy
matplot(cbind(areas,AUC_RSI10), t="l")

# comparing with piecewise linear integration of @BondedDust
points(rollapply(y1, FUN=sum, width=4, by=1), pch="+", cex=0.5)

enter image description here

计时结果：

microbenchmark(
+   loop = {
+ AUC_RSI10 <- vector("numeric", length(y1)-4)
+ for(i in 1:(length(y1)-4))
+ {
+   AUC_RSI10[i]<-auc(x1,y1,from=i,to=i+4,type="spline") 
+   
+ }
+ }, simpson = {
+   spl <- splinefun(x1, y1)
+   n <- length(x1)
+   xmid <- (x1[-1] + x1[-n]) / 2 
+   sums <- diff(x1)/6 * (y1[-1] + 4*spl(xmid) + y1[-n] )
+   areas <- roll_sum(sums, 4)
+ }
+ )

Unit: microseconds
    expr       min         lq    median         uq        max neval
    loop 56828.857 59390.3055 68435.338 75537.2380 217565.279   100
 simpson   283.399   307.3585   325.642   340.8675    603.664   100

Answer 2

我看一下NA值的位置，你会发现它们都在开头，所以简单地总结它们应该给AUC至少假设x值之间的差距只有1。

> rle(is.na(coredata(myxts5)[, "RSI_10"]))
Run Length Encoding
  lengths: int [1:2] 10 194
  values : logi [1:2] TRUE FALSE

> sum(coredata(myxts5)[, "RSI_10"], na.rm=TRUE)
[1] 10998.76

如果NAs分散得更多，可以使用pkg：zoo中的na.interp函数（我认为在加载'xts'时附加）。要从5个小组中获取它，请使用“动物园”中的rollapply：

> rollapply(coredata(myxts5)[, "RSI_10"], FUN=sum, width=5,by=5,  na.rm=TRUE)
 [1]   0.0000   0.0000 265.6250 240.2732 134.6175 211.5243 228.0486 266.6539 228.7223 240.6124
[11] 251.4329 221.3958 209.3340 267.2705 247.8771 278.8042 296.7200 231.1503 231.3596 254.7514
[21] 238.3585 265.2186 287.6986 287.6617 265.9073 281.2436 290.2518 310.1261 261.5776 293.9180
[31] 344.0697 387.5273 429.9269 423.6355 276.7612 291.7550 355.2100 373.2746 394.1265 360.0787

如何对曲线下面积的计算进行向量化

2 个答案: