plot.window（…）中出错：最终值'；ylim'；在R中使用boxcox规范化时需要
这里是数据示例plot.window（…）中出错：最终值'；ylim'；在R中使用boxcox规范化时需要,r,R,这里是数据示例 dt=structure(list(Latitude = c(28.11957, 28.11581, 28.11144, 28.1137, 28.02281, 28.06032, 28.10983, 28.11073, 28.13138, 28.04587), Longitude = c(77.40836, 77.41864, 77.38658, 77.38574, 77.26007, 77.27687, 77.45602, 77.
dt=structure(list(Latitude = c(28.11957, 28.11581, 28.11144, 28.1137,
28.02281, 28.06032, 28.10983, 28.11073, 28.13138, 28.04587),
Longitude = c(77.40836, 77.41864, 77.38658, 77.38574, 77.26007,
77.27687, 77.45602, 77.45677, 77.41773, 77.38187), yield.x = c(4245L,
4592L, 3976L, 4005L, 3220L, 3397L, 4083L, 3840L, 4775L, 3324L
), ORYZA.Minimal = c(582.08, 310.27, 676.17, 565.53, 378.1,
800.02, 521.53, 667.02, 582.08, 815.03), ORYZA.Intensive = c(1391.7,
1275, 1413.9, 2742.1, 1595.9, 1945.4, 1945.4, 1945.4, 1637.9,
1637.9), IR36.Minimal = c(556.19, 252.01, 686.89, 489.67,
399, 717.98, 429.77, 588.65, 556.19, 773.37), IR36.Intensive = c(1582,
1479.4, 1552.1, 1421.1, 1601, 2073.8, 1464, 1533.7, 1582,
1824.6), WOFOST.Intensive = c(3131L, 3185L, 3131L, 3173L,
3140L, 3028L, 3155L, 3138L, 3131L, 3010L), IR72.Intensive = c(2937L,
3051L, 2937L, 3010L, 2965L, 2841L, 2969L, 2948L, 2937L, 2857L
), tmina1 = c(28.13171, 28.16585, 28.17805, 28.17805, 28.35366,
28.35122, 28.1561, 28.1561, 28.16098, 28.23902), tmaxa1 = c(34.37805,
34.39512, 34.41707, 34.41707, 34.5878, 34.56098, 34.43659,
34.43659, 34.38537, 34.49512), hmina1 = c(60.24634, 60.25366,
60.09024, 60.09024, 59.32683, 59.50732, 60.26098, 60.26098,
60.27317, 59.75854), rainss1 = c(6.05, 6.02, 6.12, 6.12,
8.62, 7.23, 5.28, 5.28, 6.07, 9.02), tmina2 = c(26.95854,
26.98537, 26.98049, 26.98049, 27.00976, 27.03659, 26.97805,
26.97805, 26.98293, 27), tmaxa2 = c(32.97561, 33.00488, 33.01463,
33.01463, 33.13902, 33.12439, 33.01951, 33.01951, 32.99756,
33.09268), hmina2 = c(63.60732, 63.63171, 63.43171, 63.43171,
62.86829, 63.00976, 63.76341, 63.76341, 63.65122, 63.20488
), rainss2 = c(27.94, 27.91, 29.49, 29.49, 32.2, 28.28, 25.71,
25.71, 27.53, 36.17), tmina3 = c(21.66429, 21.66429, 21.70238,
21.70238, 21.85952, 21.87381, 21.59048, 21.59048, 21.66905,
21.69524), tmaxa3 = c(33.64286, 33.66429, 33.67857, 33.67857,
33.97381, 33.88333, 33.70238, 33.70238, 33.65238, 33.80476
), hmina3 = c(30.24048, 30.27143, 29.99762, 29.99762, 28.57143,
28.93095, 30.3881, 30.3881, 30.30952, 29.44524), max_ndvi = c(0.883131206,
0.85527879, 0.858201087, 0.862457752, 0.876478314, 0.743324697,
0.88056463, 0.853658557, 0.790722787, 0.833333313), ndvi_av1 = c(0.813338554,
0.71207198, 0.733572155, 0.729932564, 0.876478314, 0.635059396,
0.689225515, 0.734376109, 0.656024903, 0.660404734), ndvi_av1_1 = c(0.7724209,
0.728266976, 0.78009241, 0.752792495, 0.772244539, 0.664578617,
0.789137627, 0.746921837, 0.639280979, 0.741114633), ndvi_sum_50 = c(136.6598759,
145.6536001, 146.1629442, 96.96318125, 28.6404264, 30.98428436,
51.02991654, 46.34770326, 103.7962121, 116.3206422), ndvi_sum_75 = c(43.98806781,
157.6088821, 99.82658933, 100.1613262, 23.16733616, 20.07433534,
32.67057598, 30.53274579, 28.02520227, 39.78411955), arvi_max = c(0.887292802,
0.859690845, 0.866099894, 0.879408419, 0.886393666, 0.770778656,
0.888767719, 0.869518697, 0.790722787, 0.820143878), lat1 = c(0.0926100000000005,
0.0963700000000003, 0.100739999999998, 0.0984799999999986,
0.18937, 0.151859999999999, 0.102350000000001, 0.10145, 0.0808,
0.166309999999999), lon1 = c(0.0484100000000041, 0.0381300000000095,
0.0701900000000109, 0.0710300000000075, 0.196700000000007,
0.179900000000004, 0.000750000000010687, 0, 0.03904, 0.0748999999999995
)), row.names = c(NA, 10L), class = "data.frame")
library(MASS)
bc <- boxcox(yield.x ~ .,data=dt)
Latitude Longitude yield.x ORYZA.Minimal ORYZA.Intensive
1 0.15 0.13 0.08 0.17 0.17
2 0.10 0.02 0.10 0.20 0.04
3 0.04 0.10 0.10 0.20 0.12
4 0.01 0.08 0.12 0.14 0.08
5 0.07 0.02 0.08 0.11 0.08
6 0.07 0.11 0.19 0.15 0.02
7 0.12 0.08 0.20 0.13 0.14
IR36.Minimal IR36.Intensive WOFOST.Intensive IR72.Intensive
1 0.13 0.09 0.15 0.08
2 0.02 0.08 0.14 0.02
3 0.19 0.14 0.02 0.19
4 0.17 0.13 0.05 0.17
5 0.12 0.15 0.18 0.15
6 0.09 0.06 0.15 0.10
7 0.13 0.04 0.11 0.01
tmina1 tmaxa1 hmina1 rainss1 tmina2 tmaxa2 hmina2 rainss2 tmina3
1 0.20 0.08 0.19 0.20 0.02 0.19 0.10 0.12 0.01
2 0.15 0.19 0.01 0.20 0.08 0.10 0.14 0.04 0.11
3 0.08 0.04 0.05 0.09 0.18 0.08 0.09 0.19 0.15
4 0.12 0.13 0.12 0.13 0.13 0.19 0.06 0.04 0.13
5 0.19 0.09 0.03 0.18 0.04 0.07 0.14 0.14 0.08
6 0.19 0.15 0.19 0.18 0.15 0.10 0.18 0.02 0.01
7 0.10 0.15 0.17 0.11 0.19 0.10 0.18 0.08 0.19
tmaxa3 hmina3 max_ndvi ndvi_av1 ndvi_av1_1 ndvi_sum_50
1 0.03 0.18 0.10 0.20 0.06 0.08
2 0.12 0.08 0.05 0.14 0.05 0.01
3 0.16 0.10 0.04 0.03 0.08 0.04
4 0.14 0.12 0.12 0.15 0.08 0.13
5 0.14 0.12 0.19 0.04 0.06 0.06
6 0.05 0.10 0.06 0.05 0.04 0.19
7 0.20 0.08 0.12 0.19 0.04 0.09
ndvi_sum_75 arvi_max
1 0.09 0.03
2 0.10 0.05
3 0.11 0.08
4 0.01 0.08
5 0.19 0.14
6 0.09 0.19
7 0.14 0.03
使用
scalescale(dt)
您是否需要缩放（dt）？我不知道这个命令。非常感谢。
scale(dt)