Rui-- thanks so much for the help!
I'm getting this error though, which is leaving me stumped:
test-lapply(ids, function(i) {
if(!any(is.na(df[df$ID==i,3]))) {adf.test(df[df$ID==i, 3])} else {NA}
})
Error in if (interpol == min(tablep)) warning(p-value smaller than
printed p-value)
Hello,
From the help page for ?adf.test: The p-values are interpolated from
Table 4.2, p. 103 of Banerjee et al. (1993).
I believe it's a problem with your data. Putting a print statement in
the code for adf.test() gave me the following:
Call:
lm(formula = yt ~ xt1 + 1 + tt + yt1)
That makes a lot of sense. Thank you, Rui!
On Mon, Jun 24, 2013 at 3:41 PM, Rui Barradas ruipbarra...@sapo.pt wrote:
Hello,
From the help page for ?adf.test: The p-values are interpolated from
Table 4.2, p. 103 of Banerjee et al. (1993).
I believe it's a problem with your data. Putting a
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
Hello,
Sorry, I forgot to Cc the list.
Rui Barradas
Em 23-06-2013 21:44, Rui Barradas escreveu:
Hello,
See if the following does what you want.
lapply(seq_len(obsv), function(i) adf.test(df[df$ID == i, 3]))
Hope this helps,
Rui Barradas
Em 23-06-2013 19:12, Olga Musayev escreveu:
Short
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