R Dataset / Package DAAG / bomsoi

On this R-data 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. You can load the bomsoi data set in R by issuing the following command at the console data("bomsoi"). This will 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 the library() command. 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 Data

Description

The 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.

Usage

bomsoi

Format

This data frame contains the following columns:

Year: a numeric vector
Jan: average January SOI values for each year
Feb: average February SOI values for each year
Mar: average March SOI values for each year
Apr: average April SOI values for each year
May: average May SOI values for each year
Jun: average June SOI values for each year
Jul: average July SOI values for each year
Aug: average August SOI values for each year
Sep: average September SOI values for each year
Oct: average October SOI values for each year
Nov: average November SOI values for each year
Dec: average December SOI values for each year
SOI: a numeric vector consisting of average annual SOI values
avrain: a numeric vector consisting of a weighted average annual rainfall at a large number of Australian sites
NTrain: Northern Territory rain
northRain: north rain
seRain: southeast rain
eastRain: east rain
southRain: south rain
swRain: southwest rain

Source

Australian Bureau of Meteorology web pages:

http://www.bom.gov.au/climate/change/rain02.txt and http://www.bom.gov.au/climate/current/soihtm1.shtml

References

Nicholls, 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.

Attachments: csv, json

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