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") else warning("p-value greater than printed p-value") :
missing value where TRUE/FALSE needed
Any idea what this could mean?
On Sun, Jun 23, 2013 at 4:54 PM, Rui Barradas <ruipbarra...@sapo.pt> wrote:
> 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$ID == i, 3]))
>>
>>
>> Hope this helps,
>>
>> Rui Barradas
>>
>> Em 23-06-2013 19:12, Olga Musayev escreveu:
>>
>>> Short question: Is it possible to use statistical tests, like the
>>> Augmented
>>> Dickey-Fuller test, in functions with for-loops? If not, are there any
>>> alternative ways to scale measures?
>>>
>>> Detailed explanation: I am working with time-series, and I want to flag
>>> curves that are not stationary and which display pulses, trends, or level
>>> shifts.
>>>
>>> df
>>>>
>>>
>>> DATE ID VALUE2012-03-06 1 5.672012-03-07 1
>>> 3.452012-03-08 1 4.562012-03-09 1 20.302012-03-10 1
>>> 5.102012-03-06 2 5.672012-03-07 2 3.452012-03-08 2
>>> 4.562012-03-09 2 5.282012-03-10 2 5.102012-03-06 3
>>> 5.672012-03-07 3 7.802012-03-08 3 8.792012-03-09 3
>>> 9.432012-03-10 3 10.99
>>>
>>> You can see, object 2 is stationary, but 3 exhibits a trend and 1 has a
>>> pulse at 3/09.
>>>
>>> What I want, in pseudo-code:
>>>
>>> flag<- list()
>>> for (i in 1:length(obsv)) {
>>> if adf.test(i) FAIL {
>>> append(flag, i)
>>> }}
>>>
>>> What I have so far:
>>>
>>> library(tseries)
>>>> adf.test(df[which(df$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!
>>>
>>> [[alternative HTML version deleted]]
>>>
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