R Dataset / Package datasets / UKDriverDeaths
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dataset-16110.csv | 3.52 KB |
Documentation |
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On this Picostat.com statistics page, you will find information about the UKDriverDeaths data set which pertains to Road Casualties in Great Britain 1969–84. The UKDriverDeaths data set is found in the datasets R package. Try to load the UKDriverDeaths data set in R by issuing the following command at the console data("UKDriverDeaths"). This may load the data into a variable called UKDriverDeaths. If R says the UKDriverDeaths data set is not found, you can try installing the package by issuing this command install.packages("datasets") and then attempt to reload the data with library("datasets") followed by data("UKDriverDeaths"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing UKDriverDeaths at the command-line which should display the entire dataset. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the UKDriverDeaths R data set. The size of this file is about 3,608 bytes. Road Casualties in Great Britain 1969–84Description
UsageUKDriverDeaths Seatbelts Format
SourceHarvey, A.C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, pp. 519–523. Durbin, J. and Koopman, S. J. (2001) Time Series Analysis by State Space Methods. Oxford University Press. http://www.ssfpack.com/dkbook/ ReferencesHarvey, A. C. and Durbin, J. (1986) The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series B, 149, 187–227. Examplesrequire(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) -- Dataset imported from https://www.r-project.org. |
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