On Jun 22, 2010, at 11:10 AM, phani kishan wrote:
Hey!
On Tue, Jun 22, 2010 at 8:11 PM, David Winsemius <dwinsem...@comcast.net
> wrote:
On Jun 22, 2010, at 10:32 AM, Henrique Dallazuanna wrote:
Try this:
lapply(DF, auto.arima)
I am getting the following error:
"Error in if (PVAL == 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"
My objective is to come up with an exponential smoothing model which
best fits all the time-series. Simple operations like mean etc. can
be applied using lapply but not statistical functions.
It is possible that do.call may be more helpful than lapply in some
instances. But you have not described your desired instance in very
much detail.
Is it necessary that I write a function myself?
Perhaps it will be as simple as writing a wrapper that sets up the
parameters and possibly deal with NA's. Tell us how would you do it
for one vector, offer a modest subset of the data with the head
function and dput(), then we can tell you how it can be done for n
vectors.
If so, how do I loop though the columns in the dataframe?
lapply # since data.frames are lists
# Or:
?do.call
Thanks a lot
Phani
I cannot comment on the relative merits of auto.arima or the
forecast function, but I did notice that earlier today that Gabor
Grothendieck (who, I suspect, could comment knowledgeably) mentioned
the latter's use in a similar context:
https://stat.ethz.ch/pipermail/r-help/2010-June/243252.html
On Tue, Jun 22, 2010 at 11:26 AM, phani kishan
<phanikis...@gmail.com>wrote:
Hey,
I have a list of 30 odd time-series (products) in columns of a data-
frame.
I want to apply time-series forecasting functions across all the
columns of
the data-frame in order to determine which is the best model to use.
How do I go about this?
Phani
--
David Winsemius, MD
West Hartford, CT
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