我正在从Cowpertwait和Metcalfe的书中学习时间序列，＆＃34;与R＆＃34;一起介绍时间序列。这是电子书链接：http://unalmed.edu.co/~ndgirald/Archivos%20Lectura/Archivos%20curso%20Series%20EIO/notas%20series%20en%20r%20couperwait.pdf
我按照第20页的图书代码将12列的数据框转换为时间序列，将月份全球温度汇总到每年，然后绘制它，但它不起作用
以下是我收到的错误：Error in plotts(x = x, y = y, plot.type = plot.type, xy.labels = xy.labels, : cannot plot more than 10 series as "multiple"
以下是本书的代码，我还包括了他们网站上的数据集
global<-read.table("Chapter01global.txt",header=F)
global.ts = ts(global, st=c(1856,1), end=c(2005,12), fr=12)
global.annual = aggregate(global.ts, FUN=mean)
plot(global.ts); plot(global.annual)
这是我不明白的：
dim(global)是150 12，但是
dim(global.ts)是1800 12.难道它不是同一个维度吗？然后
dim(global.annual)是150 12.不应该是150 1，因为平均值是在所有月份内接受的。
另外，我假设每个列都是每个月的全球温度，因为有150行，相当于1856年到2005年的年数。
有谁知道如何修复代码？ Thaks
这是数据集：
-0.384 -0.457 -0.673 -0.344 -0.311 -0.071 -0.246 -0.235 -0.380 -0.418 -0.670 -0.386
-0.437 -0.150 -0.528 -0.692 -0.629 -0.363 -0.375 -0.328 -0.495 -0.646 -0.754 -0.137
-0.452 -1.031 -0.643 -0.328 -0.311 -0.263 -0.248 -0.274 -0.203 -0.121 -0.913 -0.197
-0.249 -0.041 -0.082 -0.172 -0.085 -0.278 -0.220 -0.132 -0.436 -0.234 -0.288 -0.486
-0.070 -0.526 -0.599 -0.420 -0.273 -0.063 -0.182 -0.256 -0.213 -0.326 -0.696 -0.813
-0.858 -0.415 -0.431 -0.443 -0.735 -0.169 -0.227 -0.131 -0.377 -0.375 -0.434 -0.209
-0.711 -0.817 -0.435 -0.232 -0.194 -0.322 -0.466 -0.623 -0.345 -0.382 -0.932 -0.768
0.263 -0.063 -0.379 -0.187 -0.320 -0.365 -0.510 -0.359 -0.291 -0.431 -0.362 -0.276
-0.834 -0.604 -0.516 -0.482 -0.399 -0.200 -0.138 -0.332 -0.394 -0.711 -0.507 -0.587
-0.125 -0.615 -0.597 -0.135 -0.152 -0.227 -0.153 -0.267 0.002 -0.358 -0.200 -0.324
0.095 -0.173 -0.396 -0.119 -0.533 0.078 -0.089 -0.282 -0.224 -0.364 -0.294 -0.368
-0.306 0.087 -0.665 -0.235 -0.452 -0.316 -0.267 -0.218 -0.073 -0.177 -0.317 -0.540
-0.718 -0.382 0.004 -0.281 -0.029 -0.113 0.067 -0.135 -0.234 -0.247 -0.517 -0.098
-0.159 0.241 -0.602 -0.232 -0.210 -0.372 -0.295 -0.228 -0.252 -0.581 -0.489 -0.487
-0.072 -0.462 -0.426 -0.246 -0.260 -0.242 -0.110 -0.216 -0.248 -0.352 -0.133 -0.729
-0.349 -0.528 -0.058 -0.157 -0.215 -0.216 -0.129 -0.218 -0.437 -0.505 -0.613 -0.643
-0.378 -0.385 -0.469 -0.163 -0.109 -0.117 -0.206 -0.146 -0.171 -0.269 -0.359 -0.385
-0.090 -0.397 -0.365 -0.414 -0.433 -0.187 -0.138 -0.183 -0.302 -0.541 -0.508 -0.397
-0.033 -0.407 -0.569 -0.573 -0.470 -0.300 -0.111 -0.342 -0.248 -0.476 -0.564 -0.425
-0.665 -0.673 -0.561 -0.457 -0.099 -0.162 -0.330 -0.233 -0.390 -0.463 -0.533 -0.493
-0.320 -0.361 -0.451 -0.407 -0.548 -0.387 -0.272 -0.239 -0.456 -0.465 -0.739 -0.812
-0.401 -0.085 -0.310 -0.442 -0.530 -0.155 -0.136 -0.165 -0.121 -0.157 -0.186 0.142
0.009 0.204 0.341 0.159 -0.125 -0.066 -0.134 -0.116 -0.113 -0.169 -0.274 -0.430
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-0.108 -0.264 -0.300 -0.217 -0.334 -0.382 -0.280 -0.112 -0.274 -0.434 -0.557 -0.280
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-0.480 -0.382 -0.437 -0.327 -0.317 -0.043 -0.146 -0.241 -0.335 -0.435 -0.348 -0.315
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-0.551 -0.481 -0.343 -0.426 -0.386 -0.432 -0.268 -0.301 -0.241 -0.309 -0.311 -0.150
-0.340 -0.490 -0.339 -0.140 -0.061 -0.236 -0.138 -0.134 -0.202 -0.341 -0.362 -0.290
-0.439 -0.483 -0.363 -0.426 -0.262 -0.302 -0.151 -0.278 -0.259 -0.465 -0.365 -0.357
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-0.347 -0.308 -0.334 -0.315 -0.454 -0.398 -0.406 -0.406 -0.466 -0.472 -0.596 -0.423
-0.583 -0.555 -0.422 -0.364 -0.256 -0.285 -0.312 -0.258 -0.167 -0.319 -0.537 -0.163
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-0.531 -0.531 -0.426 -0.604 -0.661 -0.556 -0.478 -0.459 -0.382 -0.363 -0.595 -0.483
-0.432 -0.465 -0.635 -0.515 -0.523 -0.379 -0.387 -0.446 -0.386 -0.516 -0.571 -0.490
-0.538 -0.514 -0.635 -0.572 -0.529 -0.444 -0.493 -0.285 -0.276 -0.291 -0.287 -0.516
-0.317 -0.435 -0.441 -0.404 -0.447 -0.460 -0.331 -0.386 -0.372 -0.468 -0.585 -0.640
-0.520 -0.612 -0.643 -0.666 -0.500 -0.466 -0.383 -0.372 -0.346 -0.409 -0.352 -0.273
-0.354 -0.260 -0.457 -0.347 -0.354 -0.251 -0.414 -0.507 -0.494 -0.573 -0.461 -0.399
-0.379 -0.464 -0.602 -0.471 -0.520 -0.516 -0.370 -0.302 -0.346 -0.394 -0.230 -0.133
-0.063 -0.194 -0.330 -0.368 -0.259 -0.198 -0.183 -0.270 -0.299 -0.140 -0.328 -0.360
-0.190 -0.004 -0.408 -0.050 -0.218 -0.104 -0.032 -0.159 -0.085 -0.265 -0.169 -0.248
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0.106 0.069 -0.121 0.021 -0.007 0.008 0.003 0.018 0.018 -0.056 -0.218 -0.086
0.051 0.122 0.107 0.112 0.021 0.030 -0.039 0.015 0.047 0.056 -0.071 0.068
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0.275 0.174 0.065 0.046 0.045 0.001 0.049 0.017 -0.034 0.013 0.041 0.078
0.110 0.056 0.088 0.062 -0.010 0.053 0.033 -0.011 0.032 -0.061 -0.138 -0.055
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0.028 0.134 0.055 0.069 0.077 0.066 -0.023 0.007 -0.023 -0.079 -0.049 -0.092
0.031 0.087 0.016 -0.020 -0.067 -0.062 -0.022 -0.017 -0.004 0.033 0.024 0.027
-0.042 0.166 -0.123 -0.053 -0.040 -0.021 0.061 0.100 0.085 0.155 0.110 0.016
-0.023 -0.158 -0.277 -0.246 -0.175 -0.185 -0.172 -0.255 -0.283 -0.342 -0.297 -0.414
-0.195 -0.290 -0.244 -0.273 -0.155 -0.129 -0.217 -0.127 -0.100 -0.055 -0.148 -0.078
-0.097 -0.080 -0.066 -0.132 -0.120 0.017 0.005 -0.057 -0.018 -0.119 -0.108 -0.218
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0.129 0.148 0.047 0.138 0.203 0.126 0.067 0.056 0.040 0.032 0.162 0.056
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0.427 0.297 0.373 0.316 0.248 0.251 0.200 0.220 0.166 0.185 0.038 0.143
0.061 0.159 0.155 0.131 0.107 0.148 0.208 0.206 0.198 0.225 0.132 0.243
0.237 0.283 0.534 0.355 0.282 0.271 0.253 0.291 0.218 0.351 0.360 0.260
0.301 0.385 0.220 0.376 0.296 0.341 0.289 0.222 0.210 0.173 0.107 0.095
0.349 0.306 0.245 0.143 0.144 0.130 0.020 0.035 0.004 -0.021 -0.064 0.097
0.321 0.296 0.262 0.227 0.223 0.169 0.136 0.111 0.051 0.137 0.017 0.194
0.222 -0.032 0.223 0.243 0.244 0.224 0.185 0.224 0.239 0.327 0.361 0.331
0.462 0.558 0.373 0.319 0.281 0.369 0.387 0.415 0.323 0.350 0.361 0.280
0.163 0.343 0.217 0.164 0.256 0.274 0.281 0.228 0.189 0.165 0.166 0.278
0.254 0.348 0.324 0.295 0.299 0.431 0.422 0.444 0.517 0.538 0.488 0.572
0.512 0.824 0.593 0.660 0.613 0.639 0.704 0.670 0.475 0.452 0.345 0.469
0.404 0.607 0.283 0.357 0.296 0.328 0.340 0.287 0.324 0.280 0.197 0.383
0.203 0.429 0.388 0.469 0.304 0.267 0.250 0.365 0.304 0.219 0.123 0.172
0.343 0.307 0.505 0.432 0.456 0.415 0.447 0.502 0.407 0.390 0.523 0.348
0.641 0.680 0.620 0.445 0.429 0.449 0.488 0.395 0.439 0.395 0.423 0.301
0.545 0.430 0.393 0.397 0.450 0.440 0.454 0.518 0.521 0.573 0.429 0.573
0.508 0.619 0.527 0.469 0.295 0.358 0.364 0.436 0.452 0.494 0.586 0.385
0.502 0.355 0.512 0.553 0.494 0.516 0.537 0.510 0.526 0.514 0.493 0.305
答案 0 :(得分：1)
您的问题就在这里，您将ts应用于由data.frame而不是read.table值产生的vector：
global <- read.table("Chapter01global.txt",header=F)
global.ts = ts(global, st=c(1856,1), end=c(2005,12), fr=12)
比较他们的代码：
www = "http://web.address.that.doesnt.work.anymore.com"
global = scan(www)
#Read 1800 items
global.ts = ts(global, st=c(1856,1), end=c(2005,12), fr=12)
您的代码会产生：
str(global)
#'data.frame': 150 obs. of 12 variables: ...
他们的代码会生成一个向量，然后可以将其转换为正确的ts对象：
str(global)
#num [1:1800] -0.384 -0.457 -0.673 -0.344 -...