如何聚集密謀目的的月度系列（DFM）到每年（TS）？

Question

我學習從書Cowpertwait和梅特卡夫，「採用時間序列R」的時間序列。這裏是電子書鏈接：http://unalmed.edu.co/~ndgirald/Archivos%20Lectura/Archivos%20curso%20Series%20EIO/notas%20series%20en%20r%20couperwait.pdf

我跟着第20頁的書碼12列的數據幀轉換成一個時間序列，聚集月份全球氣溫到每年，然後繪製它，但它不工作

這是我得到的錯誤：Error in plotts(x = x, y = y, plot.type = plot.type, xy.labels = xy.labels, : cannot plot more than 10 series as "multiple"

下面是書中的代碼，我也包括從他們的網站數據集中

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) 

這是我不明白： dim(global)是150 12，但 dim(global.ts)是1800 12不是應該相同的尺寸？然後 dim(global.annual)爲150 12.難道不應該是150 1，由於平均是對所有的月份。

另外，我假設每個列是每個月份全球氣溫，因爲有150行，這相當於從1856年數字到2005年。

有誰知道如何解決的代碼？ Thaks

這裏是集：

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0.032 0.062 0.147 0.150 0.053 0.032 0.124 0.053 0.124 0.197 0.096 -0.187 
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0.036 0.037 0.003 0.161 -0.100 0.004 -0.018 0.301 0.107 0.134 -0.013 -0.189 
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-0.239 -0.379 -0.323 -0.326 -0.323 -0.261 -0.203 -0.242 -0.268 -0.195 -0.295 -0.248 
-0.154 -0.103 -0.115 -0.066 0.013 0.050 -0.019 0.061 0.027 -0.002 0.083 0.168 
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 
-0.020 0.158 -0.296 -0.128 -0.133 0.009 0.006 0.009 0.042 -0.006 -0.118 0.136 
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 
-0.172 -0.269 -0.110 -0.094 0.019 -0.125 -0.112 -0.052 -0.096 0.071 -0.107 -0.134 
-0.272 -0.227 0.015 -0.192 -0.209 -0.095 -0.068 -0.055 -0.053 -0.013 -0.050 -0.093 
-0.178 -0.106 0.028 0.104 0.079 0.025 0.076 0.051 0.027 0.039 0.130 0.169 
0.068 0.172 -0.040 0.043 -0.042 -0.013 -0.067 -0.079 -0.051 -0.096 -0.070 -0.215 
-0.078 -0.309 -0.286 -0.236 -0.232 -0.246 -0.152 -0.129 -0.126 -0.148 -0.126 -0.206 
-0.350 -0.286 -0.154 -0.065 -0.038 0.022 0.013 0.069 -0.002 0.052 0.016 0.173 
0.187 0.263 0.237 0.137 0.129 0.128 0.074 0.028 -0.023 0.011 -0.071 -0.070 
-0.301 -0.314 -0.194 -0.169 -0.146 -0.111 -0.065 -0.073 -0.140 -0.172 -0.174 -0.218 
-0.077 -0.111 -0.092 -0.091 -0.038 -0.082 -0.082 -0.120 -0.066 -0.220 -0.264 -0.281 
-0.197 -0.278 -0.391 -0.153 -0.258 -0.148 -0.156 -0.165 -0.129 -0.294 -0.166 -0.092 
-0.112 0.088 0.140 0.122 0.060 0.123 0.051 0.019 0.067 0.017 0.140 -0.014 
0.065 0.027 0.027 -0.030 -0.114 -0.130 -0.031 -0.120 -0.045 -0.092 0.011 -0.041 
0.014 -0.121 0.006 -0.042 -0.023 0.078 0.036 0.114 0.128 0.159 0.138 0.334 
0.129 0.148 0.047 0.138 0.203 0.126 0.067 0.056 0.040 0.032 0.162 0.056 
0.304 0.202 0.227 0.159 0.067 0.077 0.042 0.090 0.072 0.035 0.074 0.254 
-0.007 -0.025 -0.104 0.030 0.051 -0.021 -0.002 -0.011 0.077 0.039 -0.012 0.209 
0.373 0.334 0.247 0.188 0.173 0.198 0.207 0.235 0.211 0.114 0.276 0.113 
0.130 0.047 0.106 0.037 0.115 0.018 0.061 0.095 0.036 0.012 -0.078 -0.211 
0.086 -0.093 0.010 -0.015 0.052 -0.033 -0.018 0.047 0.005 0.029 -0.016 0.086 
0.198 0.185 0.120 0.109 0.102 0.116 0.055 0.053 0.076 0.077 -0.029 0.084 
0.189 0.339 0.070 0.182 0.199 0.219 0.330 0.270 0.330 0.264 0.252 0.418 
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 

Answer 1

你的問題是在這裏，在這裏您將ts到data.frame從read.table產生的，而不是一個值的vector：

global <- read.table("Chapter01global.txt",header=F) 
global.ts = ts(global, st=c(1856,1), end=c(2005,12), fr=12) 

比較他們的代碼：

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) 

你的代碼產生：

str(global) 
#'data.frame': 150 obs. of 12 variables: ... 

他們的代碼會導致然後可以變成一個適當的ts物體的向量：

str(global) 
#num [1:1800] -0.384 -0.457 -0.673 -0.344 -...