R 按间隔计算每行的平均值
我需要计算区间每一行的平均值。下面是一个基本的例子，也许任何人都有更好的想法：R 按间隔计算每行的平均值,r,cut,R,Cut,我需要计算区间每一行的平均值。下面是一个基本的例子，也许任何人都有更好的想法： M_1_mb <- (15 : -15)#creating a vector value --> small M_31 <- cut(M_31_mb,128)# getting 128 groups from the small vector #M_1_mb <- (1500 : -1500)#creating a vector value #M_1 <- cut(M_1
M_1_mb <- (15 : -15)#creating a vector value --> small
M_31 <- cut(M_31_mb,128)# getting 128 groups from the small vector
#M_1_mb <- (1500 : -1500)#creating a vector value
#M_1 <- cut(M_1_mb,128)# getting 128 groups from the vector
M_31_mb <- (15 : -15) # smaller vector
M_31 <- table(cut2(M_31_mb,g=128,levels.mean = TRUE))
aggregate(M_1_mb,by=list(M_1),mean)
Group.1 x
1 (-1.5e+03,-1.48e+03] -1488.5
2 (-1.48e+03,-1.45e+03] -1465.0
3 (-1.45e+03,-1.43e+03] -1441.5
4 (-1.43e+03,-1.41e+03] -1418.0
5 (-1.41e+03,-1.38e+03] -1394.5
6 (-1.38e+03,-1.36e+03] -1371.0
7 (-1.36e+03,-1.34e+03] -1347.5
8 (-1.34e+03,-1.31e+03] -1324.0
9 (-1.31e+03,-1.29e+03] -1301.0
10 (-1.29e+03,-1.27e+03] -1277.5
11 (-1.27e+03,-1.24e+03] -1254.0
12 (-1.24e+03,-1.22e+03] -1230.5
13 (-1.22e+03,-1.2e+03] -1207.0
14 (-1.2e+03,-1.17e+03] -1183.5
15 (-1.17e+03,-1.15e+03] -1160.0
16 (-1.15e+03,-1.12e+03] -1136.5
17 (-1.12e+03,-1.1e+03] -1113.0
18 (-1.1e+03,-1.08e+03] -1090.0
19 (-1.08e+03,-1.05e+03] -1066.5
20 (-1.05e+03,-1.03e+03] -1043.0
21 (-1.03e+03,-1.01e+03] -1019.5
22 (-1.01e+03,-984] -996.0
23 (-984,-961] -972.5
24 (-961,-938] -949.0
25 (-938,-914] -926.0
26 (-914,-891] -902.5
27 (-891,-867] -879.0
28 (-867,-844] -855.5
29 (-844,-820] -832.0
30 (-820,-797] -808.5
31 (-797,-773] -785.0
32 (-773,-750] -761.5
33 (-750,-727] -738.0
34 (-727,-703] -715.0
35 (-703,-680] -691.5
36 (-680,-656] -668.0
37 (-656,-633] -644.5
38 (-633,-609] -621.0
39 (-609,-586] -597.5
40 (-586,-562] -574.0
41 (-562,-539] -551.0
42 (-539,-516] -527.5
43 (-516,-492] -504.0
44 (-492,-469] -480.5
45 (-469,-445] -457.0
46 (-445,-422] -433.5
47 (-422,-398] -410.0
48 (-398,-375] -386.5
49 (-375,-352] -363.0
50 (-352,-328] -340.0
51 (-328,-305] -316.5
52 (-305,-281] -293.0
53 (-281,-258] -269.5
54 (-258,-234] -246.0
55 (-234,-211] -222.5
56 (-211,-188] -199.0
57 (-188,-164] -176.0
58 (-164,-141] -152.5
59 (-141,-117] -129.0
60 (-117,-93.8] -105.5
61 (-93.8,-70.3] -82.0
62 (-70.3,-46.9] -58.5
63 (-46.9,-23.4] -35.0
64 (-23.4,0] -11.5
65 (0,23.4] 12.0
66 (23.4,46.9] 35.0
67 (46.9,70.3] 58.5
68 (70.3,93.8] 82.0
69 (93.8,117] 105.5
70 (117,141] 129.0
71 (141,164] 152.5
72 (164,188] 176.0
73 (188,211] 199.0
74 (211,234] 222.5
75 (234,258] 246.0
76 (258,281] 269.5
77 (281,305] 293.0
78 (305,328] 316.5
79 (328,352] 340.0
80 (352,375] 363.5
81 (375,398] 387.0
82 (398,422] 410.0
83 (422,445] 433.5
84 (445,469] 457.0
85 (469,492] 480.5
86 (492,516] 504.0
87 (516,539] 527.5
88 (539,562] 551.0
89 (562,586] 574.0
90 (586,609] 597.5
91 (609,633] 621.0
92 (633,656] 644.5
93 (656,680] 668.0
94 (680,703] 691.5
95 (703,727] 715.0
96 (727,750] 738.5
97 (750,773] 762.0
98 (773,797] 785.0
99 (797,820] 808.5
100 (820,844] 832.0
101 (844,867] 855.5
102 (867,891] 879.0
103 (891,914] 902.5
104 (914,938] 926.0
105 (938,961] 949.0
106 (961,984] 972.5
107 (984,1.01e+03] 996.0
108 (1.01e+03,1.03e+03] 1019.5
109 (1.03e+03,1.05e+03] 1043.0
110 (1.05e+03,1.08e+03] 1066.5
111 (1.08e+03,1.1e+03] 1090.0
112 (1.1e+03,1.12e+03] 1113.5
113 (1.12e+03,1.15e+03] 1137.0
114 (1.15e+03,1.17e+03] 1160.0
115 (1.17e+03,1.2e+03] 1183.5
116 (1.2e+03,1.22e+03] 1207.0
117 (1.22e+03,1.24e+03] 1230.5
118 (1.24e+03,1.27e+03] 1254.0
119 (1.27e+03,1.29e+03] 1277.5
120 (1.29e+03,1.31e+03] 1301.0
121 (1.31e+03,1.34e+03] 1324.0
122 (1.34e+03,1.36e+03] 1347.5
123 (1.36e+03,1.38e+03] 1371.0
124 (1.38e+03,1.41e+03] 1394.5
125 (1.41e+03,1.43e+03] 1418.0
126 (1.43e+03,1.45e+03] 1441.5
127 (1.45e+03,1.48e+03] 1465.0
128 (1.48e+03,1.5e+03] 1488.5
tapplyM_1_mb，M_1，FUN=mean？有很多NAs…请提供一个代表您实际问题的可复制示例。如果您的基础数据包含NAs，则应注意情况就是这样。无法对这些元素进行装箱，因此您必须在执行tapply功能之前删除它们或输入它们的值。我的数据没有任何NAs，我提供了不包含任何NAs的可复制向量示例。NAs出现在使用docendo discimusanyNAtapplyM_1_mb，cutM_1_mb，128提供的tapply函数后的平均值位置，mean返回FALSE。这并不完全是我需要的，在cut之后，我得到了128个组/间隔，因为我希望得到每个创建间隔的平均值，我应该得到128个平均值，而不是31，因为Aggregate我经常从Aggregate获得128个平均值，但是有一个问题，为什么这个代码不适用于较小的向量：M_31_mb，因为其他128-31=97组是空的，没有点，平均值是0实际上我刚刚检查了它，有点级别SM_31
Group.1 x
1 (-1.5e+03,-1.48e+03] -1488.5
2 (-1.48e+03,-1.45e+03] -1465.0
3 (-1.45e+03,-1.43e+03] -1441.5
4 (-1.43e+03,-1.41e+03] -1418.0
5 (-1.41e+03,-1.38e+03] -1394.5
6 (-1.38e+03,-1.36e+03] -1371.0
7 (-1.36e+03,-1.34e+03] -1347.5
8 (-1.34e+03,-1.31e+03] -1324.0
9 (-1.31e+03,-1.29e+03] -1301.0
10 (-1.29e+03,-1.27e+03] -1277.5
11 (-1.27e+03,-1.24e+03] -1254.0
12 (-1.24e+03,-1.22e+03] -1230.5
13 (-1.22e+03,-1.2e+03] -1207.0
14 (-1.2e+03,-1.17e+03] -1183.5
15 (-1.17e+03,-1.15e+03] -1160.0
16 (-1.15e+03,-1.12e+03] -1136.5
17 (-1.12e+03,-1.1e+03] -1113.0
18 (-1.1e+03,-1.08e+03] -1090.0
19 (-1.08e+03,-1.05e+03] -1066.5
20 (-1.05e+03,-1.03e+03] -1043.0
21 (-1.03e+03,-1.01e+03] -1019.5
22 (-1.01e+03,-984] -996.0
23 (-984,-961] -972.5
24 (-961,-938] -949.0
25 (-938,-914] -926.0
26 (-914,-891] -902.5
27 (-891,-867] -879.0
28 (-867,-844] -855.5
29 (-844,-820] -832.0
30 (-820,-797] -808.5
31 (-797,-773] -785.0
32 (-773,-750] -761.5
33 (-750,-727] -738.0
34 (-727,-703] -715.0
35 (-703,-680] -691.5
36 (-680,-656] -668.0
37 (-656,-633] -644.5
38 (-633,-609] -621.0
39 (-609,-586] -597.5
40 (-586,-562] -574.0
41 (-562,-539] -551.0
42 (-539,-516] -527.5
43 (-516,-492] -504.0
44 (-492,-469] -480.5
45 (-469,-445] -457.0
46 (-445,-422] -433.5
47 (-422,-398] -410.0
48 (-398,-375] -386.5
49 (-375,-352] -363.0
50 (-352,-328] -340.0
51 (-328,-305] -316.5
52 (-305,-281] -293.0
53 (-281,-258] -269.5
54 (-258,-234] -246.0
55 (-234,-211] -222.5
56 (-211,-188] -199.0
57 (-188,-164] -176.0
58 (-164,-141] -152.5
59 (-141,-117] -129.0
60 (-117,-93.8] -105.5
61 (-93.8,-70.3] -82.0
62 (-70.3,-46.9] -58.5
63 (-46.9,-23.4] -35.0
64 (-23.4,0] -11.5
65 (0,23.4] 12.0
66 (23.4,46.9] 35.0
67 (46.9,70.3] 58.5
68 (70.3,93.8] 82.0
69 (93.8,117] 105.5
70 (117,141] 129.0
71 (141,164] 152.5
72 (164,188] 176.0
73 (188,211] 199.0
74 (211,234] 222.5
75 (234,258] 246.0
76 (258,281] 269.5
77 (281,305] 293.0
78 (305,328] 316.5
79 (328,352] 340.0
80 (352,375] 363.5
81 (375,398] 387.0
82 (398,422] 410.0
83 (422,445] 433.5
84 (445,469] 457.0
85 (469,492] 480.5
86 (492,516] 504.0
87 (516,539] 527.5
88 (539,562] 551.0
89 (562,586] 574.0
90 (586,609] 597.5
91 (609,633] 621.0
92 (633,656] 644.5
93 (656,680] 668.0
94 (680,703] 691.5
95 (703,727] 715.0
96 (727,750] 738.5
97 (750,773] 762.0
98 (773,797] 785.0
99 (797,820] 808.5
100 (820,844] 832.0
101 (844,867] 855.5
102 (867,891] 879.0
103 (891,914] 902.5
104 (914,938] 926.0
105 (938,961] 949.0
106 (961,984] 972.5
107 (984,1.01e+03] 996.0
108 (1.01e+03,1.03e+03] 1019.5
109 (1.03e+03,1.05e+03] 1043.0
110 (1.05e+03,1.08e+03] 1066.5
111 (1.08e+03,1.1e+03] 1090.0
112 (1.1e+03,1.12e+03] 1113.5
113 (1.12e+03,1.15e+03] 1137.0
114 (1.15e+03,1.17e+03] 1160.0
115 (1.17e+03,1.2e+03] 1183.5
116 (1.2e+03,1.22e+03] 1207.0
117 (1.22e+03,1.24e+03] 1230.5
118 (1.24e+03,1.27e+03] 1254.0
119 (1.27e+03,1.29e+03] 1277.5
120 (1.29e+03,1.31e+03] 1301.0
121 (1.31e+03,1.34e+03] 1324.0
122 (1.34e+03,1.36e+03] 1347.5
123 (1.36e+03,1.38e+03] 1371.0
124 (1.38e+03,1.41e+03] 1394.5
125 (1.41e+03,1.43e+03] 1418.0
126 (1.43e+03,1.45e+03] 1441.5
127 (1.45e+03,1.48e+03] 1465.0
128 (1.48e+03,1.5e+03] 1488.5