| % File src/library/datasets/man/UKDriverDeaths.Rd |
| % Part of the R package, https://www.R-project.org |
| % Copyright 1995-2018 R Core Team |
| % Distributed under GPL 2 or later |
| |
| \name{UKDriverDeaths} |
| \docType{data} |
| \alias{UKDriverDeaths} |
| \alias{Seatbelts} |
| \title{ |
| Road Casualties in Great Britain 1969--84 |
| } |
| \description{ |
| \code{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. |
| |
| \code{Seatbelts} is more information on the same problem. |
| } |
| \usage{ |
| UKDriverDeaths |
| Seatbelts |
| } |
| \format{ |
| \code{Seatbelts} is a multiple time series, with columns |
| \describe{ |
| \item{\code{DriversKilled}}{car drivers killed.} |
| \item{\code{drivers}}{same as \code{UKDriverDeaths}.} |
| \item{\code{front}}{front-seat passengers killed or seriously injured.} |
| \item{\code{rear}}{rear-seat passengers killed or seriously injured.} |
| \item{\code{kms}}{distance driven.} |
| \item{\code{PetrolPrice}}{petrol price.} |
| \item{\code{VanKilled}}{number of van (\sQuote{light goods vehicle}) |
| drivers.} |
| \item{\code{law}}{0/1: was the law in effect that month?} |
| } |
| } |
| \source{ |
| Harvey, A.C. (1989). |
| \emph{Forecasting, Structural Time Series Models and the Kalman Filter}. |
| Cambridge University Press, pp.\sspace{}519--523. |
| |
| Durbin, J. and Koopman, S. J. (2001). |
| \emph{Time Series Analysis by State Space Methods}. |
| Oxford University Press. |
| \url{http://www.ssfpack.com/dkbook/} |
| } |
| \references{ |
| Harvey, A. C. and Durbin, J. (1986). |
| The effects of seat belt legislation on British road casualties: A |
| case study in structural time series modelling. |
| \emph{Journal of the Royal Statistical Society} series A, \bold{149}, |
| 187--227. |
| \doi{10.2307/2981553}. |
| } |
| \examples{ |
| require(stats); require(graphics) |
| ## work with pre-seatbelt period to identify a model, use logs |
| 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 |
| 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) |
| } |
| \keyword{datasets} |