filter() on zoo objects
Hi Costas: I don't think you are using loess correctly - it requires a formula, see ?loess. with quantmod and xts/zoo:
getSymbols("^DJI",from='1990-01-01')
[1] "DJI"
loess(dailyReturn(DJI,type="log") ~ as.numeric(time(DJI)))
Call: loess(formula = dailyReturn(DJI,type="log") ~ as.numeric(time(DJI))) Number of Observations: 4595 Equivalent Number of Parameters: 4.34 Residual Standard Error: 0.009854
You may need to call Delt(Cl(DJI)) in place of dailyReturn if you don't have the most recent R-forge version of quantmod. Additionally filter works too:
filter(DJI,2)
[,1] [,2] [,3] [,4] [,5] [,6] [1,] 5506.40 5623.30 5465.02 5620.30 324140000 5620.30 [2,] 5620.30 5668.08 5572.52 5619.46 384660000 5619.46 [3,] 5619.46 5642.92 5532.84 5592.16 354000000 5592.16 [4,] 5592.16 5620.30 5516.22 5546.50 317060000 5546.50 [5,] NA NA NA NA NA NA [6,] NA NA NA NA NA NA [7,] 5546.50 5607.94 5506.82 5588.74 280220000 5588.74 [8,] 5588.74 5621.58 5520.06 5532.00 310420000 5532.00 [9,] 5532.00 5545.64 5450.94 5501.28 351980000 5501.28 [10,] 5501.28 5566.54 5496.58 5521.34 308780000 5521.34 ...
as.xts(filter(DJI,2))
1990-01-02 5506.40 5623.30 5465.02 5620.30 324140000 5620.30 1990-01-03 5620.30 5668.08 5572.52 5619.46 384660000 5619.46 1990-01-04 5619.46 5642.92 5532.84 5592.16 354000000 5592.16 1990-01-05 5592.16 5620.30 5516.22 5546.50 317060000 5546.50 1990-01-06 NA NA NA NA NA NA 1990-01-07 NA NA NA NA NA NA 1990-01-08 5546.50 5607.94 5506.82 5588.74 280220000 5588.74 1990-01-09 5588.74 5621.58 5520.06 5532.00 310420000 5532.00 1990-01-10 5532.00 5545.64 5450.94 5501.28 351980000 5501.28 1990-01-11 5501.28 5566.54 5496.58 5521.34 308780000 5521.34 ... My opinion on the conversion is that it is painful and a waste of development resources. All of the time-series objects in R exist for a reason, and many people are accustomed to one class or another. As a developer you had to choose which to support and which to add to your WISHLIST. Forcing the user to adopt a particular class such as 'ts' or 'timeSeries' is at best unappealing, and most likely will simply force the user to look elsewhere for a solution. The general problem with conversion/reconversion is what we have been addressing with the `xts` package. It has yet to be fully sorted out, and certainly has yet to see wide adoption by other package developers, but the core concept is simple, and it _is_ implemented so far in a few functions: Basically, functions can call `as.xts` on any incoming time-series-like object, handle it with all the tools available to zoo/xts objects, and then simply call `reclass` to convert the object back to its original class to return to the user the class of object he used in the original call. There are some minor things to keep track of if very different objects are returned, but for the most part it is that simple. If the object need not be returned then the process truly only requires a call to 'as.xts' at the beginning. The rest of the function can now handle the data as a zoo/xts object. The majority of functions seem to only need this approach. At present you can see 'reclass' in the `to.period` function in `xts`, as well as the `periodReturn` function that is in the quantmod version on R-forge. 'as.xts' is used in many functions in both packages to allow for maximum time-series-like class support - including chartSeries from quantmod. The issue that this approach addresses is the simplified and uniform handling of all of R's disparate time-series-like objects. `xts` attempts to handle the conversion and reconversion without having the package/function author write methods for each class of object. It saves time, and makes for a far less error-filled final product. In reality it also means that new functions will *just work* with almost any object that is passed in. I am working on putting together a vignette to address developing functions, as well as modifying current ones, with xts. I will make it known to the list when it is complete. Jeff On Thu, Mar 27, 2008 at 5:17 AM, Vorlow Constantinos
<CVorlow at eurobank.gr> wrote:
Dear all,
Am I right in understanding you cannot directly apply functions such as
loess() (filter() as well ?) to zoo objects and you need to use the
rollapply/rollmean functions instead?
For example:
library("tseries")
DJ<- get.hist.quote("^DJI", start = "1990-01-01", quote = "Close")
DJret<-diff(log(DJ))
# the
loess(DJret, time(DJret))
does not work(?). What am I understanding wrong? I found a reply by
Achim on a similar issue.
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/88599.html
Is Achim's answer all there is to it?
I.e., my problem is a little bit more general:
Say I want to produce fits and then forecasts on a time series (zoo
mostly) using a non-zoo routine which "understands" ts or timeSeries
objects (or simple vectors). Do I always have to "translate" zoo
objects to vectors or to ts/simeSeries ones, run the routines and then
put back the zoo attribute with the appropriate dates so as to produce
correct time-series plots (with dates etc, especially correct dates
alligned to the forecast period - which could be postsample)?
Is there an easier way to shift between zoo-ts/timeSeries objects and
produce plots statistical analysis that will have the dates correctly
aligned on (to account for example for irregularly sampled sequences
such as stock prices with non-trading days etc. etc.)?
Thanks in advance,
Costas
P Think before you print.
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