UKDriverDeaths(datasets)
UKDriverDeaths()所属R语言包：datasets
Road Casualties in Great Britain 1969–84
在1969年至1984年英国伟大的交通意外伤亡
译者：生物统计家园网 机器人LoveR
描述----------Description----------
UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.
UKDriverDeaths是给在大不列颠死亡或重伤1969年1月至1984年12月的汽车司机，每月总计时间序列。强制佩戴安全带是1983年1月31日推出。
Seatbelts is more information on the same problem.
Seatbelts是对同一问题的更多信息。
用法----------Usage----------
UKDriverDeaths
Seatbelts
格式----------Format----------
Seatbelts is a multiple time series, with columns
Seatbelts是一个多的时间序列，列
DriversKilled car drivers killed.
DriversKilled车司机死亡。
drivers same as UKDriverDeaths.
driversUKDriverDeaths的。
front front-seat passengers killed or seriously injured.
front前排乘客死亡或重伤。
rear rear-seat passengers killed or seriously injured.
rear后座乘客死亡或重伤。
kms distance driven.
kms距离驱动。
PetrolPrice petrol price.
PetrolPrice汽油价格。
VanKilled number of van ("light goods vehicle")
VanKilled数面包车（轻型货车）
law 0/1: was the law in effect that month?
law0/1：生效法律吗？
源----------Source----------
Harvey, A.C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, pp. 519–523.
哈维，AC（1989）预测，结构性时间序列模型和卡尔曼滤波。剑桥大学出版社，第519-523页。
Durbin, J. and Koopman, S. J. (2001) Time Series Analysis by State Space Methods. Oxford University Press. http://www.ssfpack.com/dkbook/
德宾，研究和考夫曼，SJ（2001）状态空间法的时间序列分析。牛津大学出版社。 http://www.ssfpack.com/dkbook/~~V
参考文献----------References----------
legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series B, 149, 187–227.
举例----------Examples----------
require(stats); require(graphics)
## work with pre-seatbelt period to identify a model, use logs[＃使用安全带预期，以确定一个模型，使用日志]
work <- window(log10(UKDriverDeaths), end = 1982+11/12)
par(mfrow = c(3,1))
plot(work); acf(work); pacf(work)
par(mfrow = c(1,1))
(fit <- arima(work, c(1,0,0), seasonal = list(order= c(1,0,0))))
z <- predict(fit, n.ahead = 24)
ts.plot(log10(UKDriverDeaths), z$pred, z$pred+2*z$se, z$pred-2*z$se,
lty = c(1,3,2,2), col = c("black", "red", "blue", "blue"))
## now see the effect of the explanatory variables[＃现在看到的解释变量的影响]
X <- Seatbelts[, c("kms", "PetrolPrice", "law")]
X[, 1] <- log10(X[, 1]) - 4
arima(log10(Seatbelts[, "drivers"]), c(1,0,0),
seasonal = list(order= c(1,0,0)), xreg = X)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-16 20:13:34
