astsa (version 2.4)

lap.xts: LA Pollution-Mortality Study: Sampled Daily

Description

Original data from a study of the effects of pollution and weather on mortality, LA, 1970-1979. These are 3652 daily observations for the 10 year period. The data set is an 'xts' object indexed by Date.

Format

The format is: An xts object on 1970-01-01 / 1979-12-31 containing:
Data: double [3652, 11]
Columns: Tmort, Rmort, Cmort, Temp, Rhumid, CO, SO2, NO2, HC, Ozone, Part
Index: Date [3652] (TZ: "UTC")

Details

columns are time series	with names
(1) Total Mortality	Tmort
(2) Respiratory Mortality	Rmort
(3) Cardiovascular Mortality	Cmort
(4) Temperature	Temp
(5) Relative Humidity	Rhumid
(6) Carbon Monoxide	CO
(7) Sulfur Dioxide	SO2
(8) Nitrogen Dioxide	NO2
(9) Hydrocarbons	HC
(10) Ozone	Ozone
(11) Particulates	Part

References

You can find demonstrations of astsa capabilities at FUN WITH ASTSA.

The most recent version of the package can be found at https://github.com/nickpoison/astsa/.

In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.

The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.

See Also

lap

Examples

# if 'xts' is not installed you can still do this
tsplot(timex(lap.xts), lap.xts, ncolm=3, col=astsa.col(4,wheel=TRUE,num=11), scale=.9)

# differencing (on its own) loses an obs 
dCmort = diff(log(lap.xts[,'Cmort']))
tsplot(timex(lap.xts)[-1], dCmort, col=4, gg=TRUE)

# classic IMA(1,1)
acf2(dCmort, col=2:7, lwd=4)

if (FALSE) {

library(xts)   # assumes package has been installed

plot(lap.xts$Cmort, col=4)   

lapw = apply.weekly(lap.xts, FUN=colMeans)  # get weekly averages

plot(lapw[,c('Cmort', 'Temp', 'Part')], col=astsa.col(2:4, .7), main=NA) 
addLegend(col=2:4, lty=1, lwd=2, ncol=3, bty="white")

sarima(lapw$Cmort, 0,1,1, no.constant=TRUE)  # fit ARIMA(0,1,1) to weekly Cmort
}