Short question: Is it possible to use statistical tests, like the Augmented
Dickey-Fuller test, in functions with for-loops? If not, are there any
alternative ways to scale measures?
Detailed explanation: I am working with time-series, and I want to flag
curves that are not stationary and which display pulses, trends, or level
shifts.
>df
DATE ID VALUE2012-03-06 1 5.672012-03-07 1
3.452012-03-08 1 4.562012-03-09 1 20.302012-03-10 1
5.102012-03-06 2 5.672012-03-07 2 3.452012-03-08 2
4.562012-03-09 2 5.282012-03-10 2 5.102012-03-06 3
5.672012-03-07 3 7.802012-03-08 3 8.792012-03-09 3
9.432012-03-10 3 10.99
You can see, object 2 is stationary, but 3 exhibits a trend and 1 has a
pulse at 3/09.
What I want, in pseudo-code:
flag<- list()
for (i in 1:length(obsv)) {
if adf.test(i) FAIL {
append(flag, i)
}}
What I have so far:
>library(tseries)
>adf.test(df[which(df$ID==1), 3])
Augmented Dickey-Fuller Test
data: dataDickey-Fuller = 11.1451, Lag order = 16, p-value = 0.01null
hypothesis: non-stationary
>adf.test(df[which(df$ID==2), 3])
Augmented Dickey-Fuller Test
data: dataDickey-Fuller = 11.1451, Lag order = 16, p-value = 0.99
alternative hypothesis: stationary
>adf.test(df[which(df$ID==3), 3])Augmented Dickey-Fuller Test
data: dataDickey-Fuller = 11.1451, Lag order = 16, p-value = 0.04null
hypothesis: non-stationary
How can I use this output in a for-loop? Thank you in advance!
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