大致相等的频率合并

我想以这样的方式对频率进行合并，使得每个bin的频率大致相同。到目前为止，我设法通过平均分割范围来分割频率，但是我还没有找到自动计算分档大小的方法。大致相等的频率合并

数据：

> dput(sumsq) 
structure(list(difftim = c(-170L, -17L, 71L, -487L, -301L, -107L, 
-202L, -71L, 404L, 99L, -252L, -18L, 253L, -505L, 421L, 597L, 
-454L, 420L, 66L, -202L, 84L, -657L, 0L, -253L, -35L, 21L, -101L, 
-152L, -33L, 150L, -253L, -185L, -137L, -100L, -370L, -67L, -218L, 
-68L, -71L, 1332L, -151L, -67L, 438L, 34L, 201L, -35L, -83L, 
-17L, 269L, -380L, -336L, -125L, 67L, -59L, 170L, 130L, 486L, 
-118L, -16L, -33L, -73L, -50L, -404L, 302L, -51L, 302L, 17L, 
-17L, 18L, -28L, 388L, 337L, 405L, -34L, -152L, 386L, 152L, 119L, 
51L, -17L, 138L, 269L, 219L, 17L, -50L, 302L, 137L, 101L, 49L, 
33L, 152L, -34L, 1186L, -33L, 57L, 69L, -338L, 251L, -18L, 67L, 
-54L, -437L, 134L, -336L, 322L, -118L, 17L, -824L, 118L, 438L, 
1L, 84L, 135L, 52L, -118L, 50L, 87L, -185L, 90L, 73L, -136L, 
16L, -37L, -135L, -117L, 235L, 303L, -286L, 286L, 50L, -521L, 
151L, 420L, -87L, -184L, -67L, -168L, -33L, -135L, -118L, 1379L, 
256L, -34L, -84L, -17L, 135L, 254L, 811L, 168L, 403L, -443L, 
-253L, 152L, -185L, -16L, 601L, -825L, -220L, -51L, -219L, 488L, 
-51L, 19L, -365L, -249L, 274L, -503L, 37L, 14L, -84L, -320L, 
-201L, 118L, 85L, -270L, -121L, 152L, -25L, 17L, -18L, -203L, 
-33L, -236L, -371L, 252L, -438L, 219L, 1852L, 3082L, 1026L, -51L, 
201L, 371L, 140L, 50L, -499L, 433L, -350L, 57L, -472L, 17L, 185L, 
-51L, -118L, 174L, -49L, 185L, -118L, -185L, -337L, -121L, 166L, 
-50L, -83L, 250L, 83L, -149L, 83L, -555L, -51L, -100L, -102L, 
148L, 116L, 382L, -100L, -174L, 84L, 657L, -232L, -17L, -100L, 
-68L, -34L, 135L, 84L, 236L, 153L, -252L, 0L, -169L, 51L, 168L, 
163L, 135L, -51L, -303L, -202L, 33L, 101L, -219L, -34L, 51L, 
168L, 179L, 404L, 135L, 67L, -233L, 371L, -202L, -112L, -319L, 
16L, 115L, -220L, -370L, 682L, -236L, 504L, 8L, -151L, 34L, -45L, 
-283L, -69L, 167L, -82L, -102L, -51L, -202L, 534L, -17L, 39L, 
55L, 79L, 55L, -35L, -902L, -124L, -1L, -101L, 25L, 214L, 459L, 
-367L, -186L, -104L, 50L, 261L, -474L, -168L, 16L, 17L, -209L, 
-556L, -236L, -17L, -235L, -67L, -17L, -49L, 504L, -319L, 1702L, 
286L, 538L, 657L, 66L, -50L, 269L, 151L, -434L, -404L, -725L, 
68L, -572L, -86L, -455L, -236L, -421L, -152L, -370L, 135L, 118L, 
-169L, -269L, -286L, -118L, -539L, -68L, -20L, -121L, 785L, -169L, 
400L, -253L, -1L, 0L, 84L, -126L, -151L, -50L, -101L, -151L, 
-163L, -50L, 0L, -100L, -169L, 497L, -34L, 170L, 154L, -505L, 
-338L, -330L, 67L, 306L, -51L, -16L, -17L, 118L, -270L, -168L, 
-85L, 977L, 1127L, 34L, -166L, -489L, 0L, -404L, -84L, -455L, 
286L, -51L, -403L, 454L, -387L, 371L, -50L, -202L, -219L, -34L, 
84L, -303L, 28L, 33L, -10L, -370L, 35L, -151L, 59L, 214L, -101L, 
0L, 185L, 252L, 67L, -67L, 362L, -50L, 151L, -16L, -151L, -150L, 
50L, -1227L, -320L, -112L, -60L, 85L, 135L, 99L, 20L, -807L, 
-50L, 925L, 1177L, -15L, -67L, 2606L, 17L, 539L, 34L, -34L, 34L, 
606L, 101L, -85L, -50L, 17L, 444L, -199L, 142L, -17L, -16L, -504L, 
134L, -617L, 235L, 202L, -101L, -554L, -17L, -303L, 17L, -118L, 
-185L, 0L, -67L, -50L, -118L, 590L, -537L, -80L, -119L, -286L, 
354L, -488L, -235L, -336L, -101L, -185L, 169L, -370L, 218L, -101L, 
16L, 336L, 0L, 387L, 168L, 219L, 118L, 202L, 236L, -185L, -134L, 
-874L, -50L, 118L, 17L, 218L, -67L, 51L, -101L, 34L, 34L, -100L, 
16L, 67L, 0L, 0L, -152L, 151L, 51L, 67L, -185L, -337L, 68L, 118L, 
84L, 101L, -167L, -404L, 403L, -387L, -127L, 202L, -214L, 84L, 
-1L, 33L, 269L, 0L, 112L, 101L, -33L, 50L, -219L, 17L, 17L, -253L, 
252L, 51L, 51L, 252L, -17L, -134L, 153L, 135L, 151L, 40L, 294L, 
-67L, -61L, -118L, 38L, 0L, -50L, 152L, -218L, -168L, 67L, -67L, 
68L, 39L, -1L, 134L, -168L, -135L, 17L, -82L, -17L, 16L, 135L, 
51L, 134L, -101L, 101L, 152L, 16L, 118L, 100L, 202L, 218L, -168L, 
269L, -118L, 201L, -403L, -420L, 0L, 0L, -370L, -201L, 67L, 319L, 
320L, -67L, -335L, 285L, 269L, 151L, 185L, -204L, -403L, -84L, 
-111L, 1L, 369L, -67L, -50L, 150L, 0L, 319L, -168L, 16L, -117L, 
-39L, -67L, -202L, 249L, -101L, -331L, -49L, -67L, 285L, -168L, 
85L, -17L, 118L, 115L, -102L, -84L, 184L, 33L, -151L, -269L, 
-118L, -150L, -50L, 67L, -17L, 180L, -5L, -135L, 154L, -51L, 
73L, -117L, -123L, -82L, -83L, 959L, -168L, -19L, 134L, -100L, 
-135L, -252L, -16L, 202L, -36L, -18L, -39L, -172L, -84L, -286L, 
135L, 32L, -138L, -1429L, -84L, 117L, 51L, 134L, -134L, -134L, 
151L, -34L, 0L, 689L, 386L, 134L, 117L, -100L, -17L, -69L, -34L, 
118L, 50L, -117L, 286L, 68L, 95L, 98L, 204L, -16L, 17L, -168L, 
-135L, 151L, -68L, 200L, 169L, 100L, 375L, -152L, -33L, 1L, 17L, 
50L, 0L, -17L, 16L, -119L, 202L, 66L, -17L, 0L, 134L, 33L, 14L, 
252L, 67L, 84L, -122L, -84L, -68L, 12L, -1397L, 51L, -101L, -68L, 
-17L, 186L, 118L, 33L, 85L, -18L, 16L, 98L, 135L, 0L, -17L, 340L, 
-35L, -34L, -34L, 336L, 85L, 101L, -98L, -1L, 251L, 101L, 151L, 
-519L, -201L, -68L, -319L, -169L, -421L, -269L, -342L, 236L, 
-543L, -1785L, -87L, 100L, 304L, 118L, 0L, -16L, 639L, 101L, 
67L, -160L, 276L, 135L, -51L, 85L, 15L, -219L, -336L, 926L, 236L, 
-320L, 200L, -33L, -50L, -611L, -353L, -67L, -17L, -160L, 17L, 
-269L, -286L, -151L, 135L, 183L, -387L, -101L, -169L, -219L, 
17L, -137L, -33L, -101L, -80L, 269L, -17L, 252L, -67L, -66L, 
118L, -324L, -100L, -135L, 235L, -17L, 68L, -488L, 387L, 205L, 
-117L, -455L, 0L, 455L, -125L, -370L, -34L, 185L, -34L, -1L, 
-101L, -123L, -729L, 504L, 0L, 123L, 352L, 218L, 134L, 588L, 
537L, -68L, 202L, 135L, 101L, -285L, 23L, -201L, 363L, 65L, 202L, 
-211L, 34L, 0L, -134L, -219L, 17L, 420L, 50L, -1L, 319L, -48L, 
151L, 17L, 152L, 945L, 46L, -33L, 232L, 16L, 15L, -304L, -168L, 
151L, -55L, 51L, 0L, 33L, 50L, -183L, 806L, -422L, -185L, -102L, 
-151L, 66L, -148L, 353L, -337L, -14L, 67L, -185L, -17L, -118L, 
-134L, 134L, 18L, -168L, 6L, 588L, 235L, -232L, 253L, 67L, -51L, 
-151L, -118L, -135L, 212L, -131L, -135L, -18L, 102L, 56L, -100L, 
84L, 84L, 150L, 50L, 17L, -35L, 136L, 34L, -147L, -35L, 51L, 
-51L, 0L, -118L, 16L, -118L, -67L, -67L, -123L, -84L, 168L, -17L, 
-353L, 51L, -84L, 17L, -101L, -17L, 202L, -220L, -1L, -159L, 
304L, -792L, -219L, 92L, -185L, 31L, -252L, 572L, -421L, 33L, 
1128L, -270L, 0L, 235L, -639L, -54L, 135L, 34L, 236L, -152L, 
124L, 33L, -17L, -67L, 202L, 723L, -34L, 82L, 201L, 1L, -169L, 
110L, -348L, -11L, 606L, -17L, -394L, -118L, 673L, 35L, 51L, 
-842L, 118L, 68L, -13L, -33L, 84L, -67L, -320L, 17L, 168L, -16L, 
84L, -268L, -201L, -487L, -68L, 17L, -34L, 336L, -56L, 67L, 101L, 
1L, 0L, -101L, 68L, -16L, 16L, -101L, 67L, 33L, 0L, 34L, 51L, 
-18L, 52L, 353L, -153L, 117L, -17L, 605L, 252L, 219L, 438L, -31L, 
-219L, -101L, 135L, 34L, 51L, 34L, 1497L, -487L, -85L, -137L, 
-67L, -135L, -51L, 202L, 51L, -1110L, -67L, -1043L, -308L, -185L, 
135L, -202L, -354L, -253L, 84L, -185L, -50L, -354L, 0L, -33L, 
7L, -252L, -134L, -284L, 34L, -219L, -219L, -303L, -287L, -135L, 
102L, -35L, 860L, -34L, 118L, 68L, -61L, -264L, -168L, 47L, 236L, 
-287L, -103L, 17L, -95L, -607L, 438L, -388L, 17L, -287L, -68L, 
-253L, 34L, -113L, -101L, 168L, 1885L, 168L, 708L, 135L, 387L, 
118L, 556L, 219L, 68L, 51L, 68L, 202L, -370L, -572L, 487L, 0L, 
-253L, 405L, 118L, -269L, 741L, -185L, -152L, -84L, -167L, 169L, 
102L, 573L, -151L, 235L, 820L, 169L, -186L, 539L, -58L, 4L, 100L, 
101L, -573L, -118L, -17L, -34L, -252L, 84L, 1L, 101L, -286L, 
269L, 270L, 370L, -168L, 152L, -83L, 34L, -50L, -84L, -51L, 84L, 
118L, 51L, 253L, 67L, -117L, 87L, 52L, -319L, -1L, 51L, 149L, 
68L, 219L, 589L, -67L, -219L, 689L, 17L, -17L, -219L, -202L, 
555L, 806L, -151L, 353L, 673L, -252L, 151L, 438L, 403L, 50L, 
-235L, -252L, 84L, -235L, -336L, 182L, 1059L, 84L, -505L, 169L, 
134L, -168L, 404L), ratperc = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0, 0, 0, 0, 0, 0, 75.6, 0, 89.6, 
24.8, -100, -100, 75.6, 100, 100, -100, -100, -100, -100, -100, 
-100, 75.6, 98.4, 98.4, -51.2, -51.2, 0.8, 0.8, 0.4, 0.4, 0.4, 
0.4, 75.2, -100, -100, -100, 1.2, -0.4, -0.4, -0.4, -0.4, 100, 
100, -1.6, 0, 0, 0, 0, -100, 0.4, 100, 0.4, 0.4, 100, -0.4, -78.4, 
0.4, 100, 100, 100, 100, -100, 23.6, 61.2, 61.2, 69.2, 75.6, 
75.6, 75.6, 75.6, 75.6, 98, 98, 98, -75.2, -75.2, 47.2, 47.2, 
47.2, 47.2, 76.8, 97.6, -71.6, -71.6, -71.6, -71.6, 24, 52, 52, 
52, 75.2, 75.2, -77.6, 25.2, 47.2, 76.4, 76.4, 76.4, 76.4, 76.4, 
76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, -73.2, 
-73.2, -73.2, -73.2, 0.8, 0.8, 75.2, 75.2, 75.2, 75.2, 75.2, 
75.2, 0.4, 0.4, 0.4, 0.4, 0.4, -100, -100, -100, -100, -100, 
73.2, 2, -0.8, -0.8, -0.8, -100, -0.4, -0.4, 50.4, 50.4, 50.4, 
50.4, 50.4, 50.4, -76.4, 99.6, 99.6, -76.4, 100, 100, 50.4, 1.2, 
28, -1.2, 93.6, 41.2, 1.6, 24.8, -1.6, 0, 0, 24.8, -24, 26, 50.8, 
2, 28, 36.4, 24, -43.6, 33.6, 61.2, 81.2, 86.8, 34, -51.6, -2, 
28.4, 2, 82, 41.6, 25.6, 82, 0.8, 92, 1.2, 86.4, 54, 96, 0.4, 
-54.4, 1.2, -93.2, -49.2, -98.4, -2, -77.2, 93.2, 23.6, 78.8, 
42.4, 0.4, 2.8, 70.8, 24.4, 2.4, 62, 92.8, 16.4, -61.2, 24.4, 
-77.2, -0.4, 74.8, 3.6, 82, 82, 18, 54, 9.2, 55.2, 96.4, 96.4, 
90, 90, -84.4, -84.4, -2.8, -2, -90.4, 2.4, 34.8, 24, -1.6, -16.8, 
2.8, 2.4, -83.2, 22.4, 22.4, -1.6, -1.6, 60, -2.4, 2.4, 2, 0.8, 
-22.8, 2, -1.6, 25.2, 2, 2, -52.8, -1.2, -1.2, 3.2, -74.4, 3.2, 
3.2, -78.4, 0.4, -2.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, -79.2, -0.8, 
-0.8, -0.8, -0.8, -0.8, -3.2, 41.2, -0.8, -0.8, -0.8, -0.8, -83.2, 
-1.6, -1.6, 0.4, 0.4, 0.4, -90, -1.6, -1.6, -1.6, -1.6, -1.6, 
-1.6, -1.6, -1.6, -1.6, -1.6, -1.6, 77.6, -79.6, 80.8, -81.6, 
-93.2, -100, 8.4, 75.6, 82.8, 67.2, -27.2, 78.8, 65.6, 84.8, 
73.6, 46.8, -62.4, 57.2, 74, 13.6, -0.8, 32.8, -27.2, 6.4, -67.2, 
79.2, -64, 58, -40.4, 64, 8, 60, 76.8, -24.8, -52.4, 56.8, 75.6, 
38.4, -50.4, -72.8, -83.6, 24, 34.8, 54.4, -54, 67.6, 78.4, -41.6, 
-64.4, -83.6, -93.6, 76.8, -2.4, -19.2, -54, -38, 5.2, 52.4, 
64.8, 42.4, 77.6, -46.4, -74.8, -60.4, -83.2, -56.4, -34.8, -16.8, 
21.2, 40, 59.2, 0.4, -17.6, 24.4, -14.4, 35.2, -26.8, 42, 44, 
-1.2, -35.6, 10.8, -19.6, -35.2, 22.4, -18.4, 27.6, -9.6, 43.2, 
-31.2, 45.2, 23.6, -16.4, 28.8, 40.4, 25.6, -8, 15.6, 11.2, -17.2, 
15.6, -17.6, 18, 24, -9.6, -34.8, 12.4, -17.2, 36.4, -9.2, -35.2, 
-19.6, 10.4, -15.6, -30.4, 30.8, 16.4, -14.8, -26.4, -34.4, 52.8, 
34.4, 55.6, 21.2, 41.2, 52, 36.8, 50, 15.6, 36, 53.6, -22.8, 
14.8, 25.2, -13.2, -18.8, 32, 20.8, -6.8, -16.4, -27.6, 14.4, 
26.8, 38, -28.4, 19.6, -23.6, 18.4, -19.6, 11.6, 0, 0, 0, -26, 
-52.4, -24.4, 2, 19.6, -10.8, 3.6, 3.6, -25.2, 28.4, 12, -11.2, 
3.2, 37.2, 26, 0.8, 47.6, -17.2, 2.4, -12, -52.4, 0.8, 28.4, 
-12, 36.4, 2.4, 50.4, -16, 24.4, -2.4, -2.4, 15.2, -1.6, -1.6, 
-1.6, 24.4, -36, 33.2, 1.2, 1.2, -48.8, -22.4, -1.2, -100, -1.6, 
-1.6, -26.4, 28, -47.6, 86, -1.6, -1.6, -1.6, -1.6, -1.6, 41.6, 
-16, 29.6, -14.8, 3.2, 3.2, 100, 0.8, 0.8, 0.8, 0.8, 25.6, 24.8, 
-28, 0.8, -39.2, -97.6, -97.6, -50, 0, 0, 49.6, 0.8, 54, 25.6, 
-1.2, -1.2, -90.8, 4.4, 4.4, 41.6, -40.8, -6, -6, 51.6, -8.4, 
0, 0, 0, -60, 2.8, -52.4, 1.6, 1.6, 1.6, 18.8, 24.4, -0.4, -0.4, 
-0.4, -0.4, -51.6, -0.4, -0.4, -0.4, 26, 0, 18, -42.4, -1.6, 
-0.4, 60.4, -2.8, -2.8, -2.8, 76, 2.8, 2.8, -29.2, -23.2, 23.6, 
-26.8, 0.4, 0.4, -40.8, -3.6, -47.6, 27.6, -2.4, -2.4, -76, -2, 
-2, -2, -30.8, 26.8, -4.4, -4.4, -4.4, 3.6, -0.8, -0.8, 67.2, 
-1.2, -48.8, 63.2, -42, 50, 30.8, 57.6, -48.8, -48.8, 41.6, -39.2, 
-39.2, -35.6, 40, -44, -39.6, -39.6, -50.8, 0, -48.8, 40, -53.2, 
52, -47.2, -47.2, -46, 26.4, -29.2, 0, -46.8, -46.8, 34.8, -43.6, 
0, 39.2, 0.4, -48.4, 0, -23.6, 29.2, 29.2, -53.2, -53.2, 19.2, 
46.4, 46.4, -2, 36, 2, -25.2, -50, -1.6, -2, 35.2, -32.8, 31.2, 
-43.2, 46, -28.8, -0.4, -50.4, 0.8, -43.6, 0.4, 27.6, -37.6, 
-37.6, 37.6, -50, 40.8, -0.8, -50.4, -49.6, 45.6, 45.6, -48.8, 
-0.8, -54, -54, 43.2, -48.8, 46.4, -42.8, 54, -54.4, 34.8, 0.4, 
0.4, 0.4, 0.8, -50.4, -50.8, -50.8, 51.6, -68.8, 0.8, 52, -42, 
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2245L, 2246L, 2247L, 2248L, 2249L, 2250L), class = "data.frame")

做迄今为止分级的方法...

timevec = seq(min(sums$difftim), max(sums$difftim), by=round((max(sums$difftim) - min(sums$difftim))/8)) 
#cut 
sumstab = xtabs(~ratperc +cut(difftim, timevec), dat=sumsid1) 
#to dataframe 
sumstab = data.frame(sumstab) 
#rename 
colnames(sumstab) = c("rating", "range", "freq") 

#convert rating to numeric 
sumstab$rating = as.numeric(as.character(sumstab$rating)) 

#prepare data for summary 
sumstab$fresum = sumstab$rating * sumstab$freq 

sumstab2 = ddply(sumstab, .(range), summarise, meanrat = sum(fresum)/sum(freq), no_obs = sum(freq))

我认为最简单的方法是找到一个需要计算向量的新方法timevec，我用于cut的数据。但是如果有人知道更好的方法来完成整个事情，请让我知道。谢谢！

来源

2012-07-30 unixsnob

不切'（）'做呢？ – Andrie 2012-07-30 13:32:34

除了'cut（）'，格子包中还有辅助函数'equal.count（）'，所以试试'help（“equal.count”，package =“lattice”）'。 – 2012-07-30 13:35:21

也许'剪切'只是不会削减它;） – 2012-07-30 13:48:06

GGPLOT2的cut_number()是一个方便的便利功能，用于查找限定Ñ近似相等地间隔人口断点，然后使用它们区间上的数据。听起来像它可能会做你想要的。

require(ggplot2) 
cut_number(sumsq$difftim, n = 10)

检查其输出：

data.frame(table(cut_number(sumsq$difftim, 10))) 
#    Var1 Freq 
# 1 [-1.78e+03,-287] 121 
# 2  (-287,-168] 128 
# 3  (-168,-101] 124 
# 4  (-101,-50] 121 
# 5   (-50,-3] 106 
# 6   (-3,34] 130 
# 7   (34,84.3] 110 
# 8  (84.3,152] 124 
# 9   (152,302] 118 
# 10 (302,3.08e+03] 118

来源

2012-07-30 13:55:48

谢谢，这正是我想要的！我已经在使用ggplot2进行绘图，所以它适合我的其他代码。 – unixsnob 2012-07-30 14:52:25

是否有任何方法将间隔转换回矢量？所以我可以在以后使用它。 – unixsnob 2012-07-30 15:19:26

快速查看'cut_number（）'的代码表明它使用未导出的函数'breaks（）'计算间隔。你也可以这样使用它：'ggplot2 ::: breaks（sumsq $ difftim，“n”，n = 10）''。 – 2012-07-30 15:32:40

尝试使用classInt包 - 它实现了几种产生分类间隔的方法，包括相同的数字。

来源

2012-07-30 13:44:16 Spacedman

大致相等的频率合并

回答

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