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}
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}
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\examples{
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\examples{
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require(stats); require(graphics)
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require(stats); require(graphics)
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## work with pre-seatbelt period to identify a model, use logs
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## work with pre-seatbelt period to identify a model, use logs
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work <- window(log10(UKDriverDeaths), end = 1982+11/12)
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work <- window(log10(UKDriverDeaths), end = 1982+11/12)
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par(mfrow = c(3,1))
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par(mfrow = c(3, 1))
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plot(work); acf(work); pacf(work)
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plot(work); acf(work); pacf(work)
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par(mfrow = c(1,1))
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par(mfrow = c(1, 1))
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(fit <- arima(work, c(1,0,0), seasonal = list(order= c(1,0,0))))
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(fit <- arima(work, c(1, 0, 0), seasonal = list(order = c(1, 0, 0))))
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z <- predict(fit, n.ahead = 24)
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z <- predict(fit, n.ahead = 24)
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ts.plot(log10(UKDriverDeaths), z$pred, z$pred+2*z$se, z$pred-2*z$se,
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ts.plot(log10(UKDriverDeaths), z$pred, z$pred+2*z$se, z$pred-2*z$se,
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        lty = c(1,3,2,2), col = c("black", "red", "blue", "blue"))
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        lty = c(1, 3, 2, 2), col = c("black", "red", "blue", "blue"))
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## now see the effect of the explanatory variables
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## now see the effect of the explanatory variables
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X <- Seatbelts[, c("kms", "PetrolPrice", "law")]
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X <- Seatbelts[, c("kms", "PetrolPrice", "law")]
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X[, 1] <- log10(X[, 1]) - 4
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X[, 1] <- log10(X[, 1]) - 4
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arima(log10(Seatbelts[, "drivers"]), c(1,0,0),
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arima(log10(Seatbelts[, "drivers"]), c(1, 0, 0),
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      seasonal = list(order= c(1,0,0)), xreg = X)
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      seasonal = list(order = c(1, 0, 0)), xreg = X)
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}
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}
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\keyword{datasets}
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\keyword{datasets}