[R] Scaling Statistical

[Rui Barradas]

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)

Residuals:
ALL 3 residuals are 0: no residual degrees of freedom!

Coefficients: (1 not defined because of singularities)
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  -24.319         NA      NA       NA
xt1           -3.115         NA      NA       NA
tt            18.087         NA      NA       NA
yt1               NA         NA      NA       NA

Residual standard error: NaN on 0 degrees of freedom
Multiple R-squared:      1,     Adjusted R-squared:    NaN
F-statistic:   NaN on 2 and 0 DF,  p-value: NA



And the test statistic becomes NaN (Not a Number). It's computed as 
coefficients[2, 1]/coefficients[2, 2] or -3.115/NA.
So when it tries to interpolate the p-value, the variable 'interpol' is 
equal to NaN and the if test fails.

With a standard error like NA, maybe you don't have enough data points 
to run the tests. (Only 3 residuals, all zero, like seen above.)


Rui Barradas

Em 24-06-2013 20:12, Olga Musayev escreveu:
> 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 <http://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 <ruipbarradas at sapo.pt
> <mailto:ruipbarradas at 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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