Hello, All:
What documentation do you recommend for someone trying to learn how to analyze time series in R beyond ch. 14 in Venables and Ripley (2002) Modern Applied Statistics with S, 4th ed. (Springer), and a not-quite random walk through the documentation on commands like arima and provided with packages like dse? I've installed dse and its, and I've made progress using arima, acf, etc. I tried working through "dse1-guide.pdf", but far I have not successfully used that package.
In particular, what's the preferred way to keep track of dates with time series? I tried assigning a "Date" object somehow to a "ts" object, so far without success. Two of my attempts are as follows:
> tst2 <- ts(1:11, frequency=365, + start=c(2005, 11)) # This seemed to work, but the time does not seem to have class "Date"
> tst3 <- ts(1:11, frequency=365,
+ start=as.Date("21/01/2005", "%d/%m/%Y"))
Error in Math.difftime((end - start) * frequency + 1.01) :
floor not defined for difftime objectsAny suggestions would be greatly appreciated. I'd gladly rtfm ("read the f****** manual"), but I don't know which fm to r.
Thanks,
Spencer Graves ############################
It looks to me like what you want is "intervention analysis" in
the time series literature. Have you considered the arima function,
especially the example in the documentation using the xreg argument?
Also, have you looked at ch. 14 in Venables and Ripley (2002) Modern
Applied Statistics with S, 4th ed. (Springer)?There are other time series packages available, e.g, dse, fSeries, its, GeneTS, msm, pastecs, splancs, tseries, urca, uroot, but I haven't used them and so can't comment further on them.
hope this helps. spencer graves
Kum-Hoe Hwang wrote:
Howdy
What I 'd like to analyze with a large data on building permits is to find time series effect of urban policy on buildings as well as cross-sectional effects in any. In 1990 the specialZone urban policy was introduced. I guess that the effects of this specialZone policy would be different from countys. There are counties that do not welcome this specialZone forced to design it.
One of the important aims is to find 1) time series effect using Dummy variable, 2) cross-sectional effects using specialZones variable below.
The data has items like year(1970-2000), floorSpace, county, specialZones agianst permitting large buildings. specialZones have been designed after 1990. (Dummy = 1 after 1990, Dummy =0 before 1990)
I have tried three methods, such as
lm(floorSpace ~ county, specialZones, Dummy), glm(floorSpace ~ county, specialZones, Dummy),
aov(floorSpace ~ county, specialZones, Dummy).
What I am focusing on is best method among lm, glm, aov or others not siginificant results.
I have wasted too much time for this. I welcome your comments.
Thanks a lot,
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