R Dataset / Package DAAG / bomsoi
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| Attachment | Size |
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| dataset-65930.csv | 12.63 KB |
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
| Documentation |
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On this Picostat.com statistics page, you will find information about the bomsoi data set which pertains to Southern Oscillation Index Data. The bomsoi data set is found in the DAAG R package. Try to load the bomsoi data set in R by issuing the following command at the console data("bomsoi"). This may load the data into a variable called bomsoi. If R says the bomsoi data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data with library("DAAG") followed by data("bomsoi"). 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 bomsoi 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 bomsoi R data set. The size of this file is about 12,928 bytes. Southern Oscillation Index DataDescriptionThe Southern Oscillation Index (SOI) is the difference in barometric pressure at sea level between Tahiti and Darwin. Annual SOI and Australian rainfall data, for the years 1900-2001, are given. Australia's annual mean rainfall is an area-weighted average of the total annual precipitation at approximately 370 rainfall stations around the country. Usagebomsoi FormatThis data frame contains the following columns:
SourceAustralian Bureau of Meteorology web pages: http://www.bom.gov.au/climate/change/rain02.txt and http://www.bom.gov.au/climate/current/soihtm1.shtml ReferencesNicholls, N., Lavery, B., Frederiksen, C.\ and Drosdowsky, W. 1996. Recent apparent changes in relationships between the El Nino – southern oscillation and Australian rainfall and temperature. Geophysical Research Letters 23: 3357-3360. Examples
plot(ts(bomsoi[, 15:14], start=1900),
panel=function(y,...)panel.smooth(1900:2005, y,...))
pause()# Check for skewness by comparing the normal probability plots for
# different a, e.g.
par(mfrow = c(2,3))
for (a in c(50, 100, 150, 200, 250, 300))
qqnorm(log(bomsoi[, "avrain"] - a))
# a = 250 leads to a nearly linear plotpause()par(mfrow = c(1,1))
plot(bomsoi$SOI, log(bomsoi$avrain - 250), xlab = "SOI",
ylab = "log(avrain = 250)")
lines(lowess(bomsoi$SOI)$y, lowess(log(bomsoi$avrain - 250))$y, lwd=2)
# NB: separate lowess fits against time
lines(lowess(bomsoi$SOI, log(bomsoi$avrain - 250)))
pause()xbomsoi <-
with(bomsoi, data.frame(SOI=SOI, cuberootRain=avrain^0.33))
xbomsoi$trendSOI <- lowess(xbomsoi$SOI)$y
xbomsoi$trendRain <- lowess(xbomsoi$cuberootRain)$y
rainpos <- pretty(bomsoi$avrain, 5)
with(xbomsoi,
{plot(cuberootRain ~ SOI, xlab = "SOI",
ylab = "Rainfall (cube root scale)", yaxt="n")
axis(2, at = rainpos^0.33, labels=paste(rainpos))
## Relative changes in the two trend curves
lines(lowess(cuberootRain ~ SOI))
lines(lowess(trendRain ~ trendSOI), lwd=2)
})
pause()xbomsoi$detrendRain <-
with(xbomsoi, cuberootRain - trendRain + mean(trendRain))
xbomsoi$detrendSOI <-
with(xbomsoi, SOI - trendSOI + mean(trendSOI))
oldpar <- par(mfrow=c(1,2), pty="s")
plot(cuberootRain ~ SOI, data = xbomsoi,
ylab = "Rainfall (cube root scale)", yaxt="n")
axis(2, at = rainpos^0.33, labels=paste(rainpos))
with(xbomsoi, lines(lowess(cuberootRain ~ SOI)))
plot(detrendRain ~ detrendSOI, data = xbomsoi,
xlab="Detrended SOI", ylab = "Detrended rainfall", yaxt="n")
axis(2, at = rainpos^0.33, labels=paste(rainpos))
with(xbomsoi, lines(lowess(detrendRain ~ detrendSOI)))
pause()par(oldpar)
attach(xbomsoi)
xbomsoi.ma0 <- arima(detrendRain, xreg=detrendSOI, order=c(0,0,0))
# ordinary regression modelxbomsoi.ma12 <- arima(detrendRain, xreg=detrendSOI,
order=c(0,0,12))
# regression with MA(12) errors -- all 12 MA parameters are estimated
xbomsoi.ma12
pause()xbomsoi.ma12s <- arima(detrendRain, xreg=detrendSOI,
seasonal=list(order=c(0,0,1), period=12))
# regression with seasonal MA(1) (lag 12) errors -- only 1 MA parameter
# is estimated
xbomsoi.ma12s
pause()xbomsoi.maSel <- arima(x = detrendRain, order = c(0, 0, 12),
xreg = detrendSOI, fixed = c(0, 0, 0,
NA, rep(0, 4), NA, 0, NA, NA, NA, NA),
transform.pars=FALSE)
# error term is MA(12) with fixed 0's at lags 1, 2, 3, 5, 6, 7, 8, 10
# NA's are used to designate coefficients that still need to be estimated
# transform.pars is set to FALSE, so that MA coefficients are not
# transformed (see help(arima))detach(xbomsoi)
pause()Box.test(resid(lm(detrendRain ~ detrendSOI, data = xbomsoi)),
type="Ljung-Box", lag=20)pause()attach(xbomsoi)
xbomsoi2.maSel <- arima(x = detrendRain, order = c(0, 0, 12),
xreg = poly(detrendSOI,2), fixed = c(0,
0, 0, NA, rep(0, 4), NA, 0, rep(NA,5)),
transform.pars=FALSE)
xbomsoi2.maSel
qqnorm(resid(xbomsoi.maSel, type="normalized"))
detach(xbomsoi)
-- Dataset imported from https://www.r-project.org. |
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