I was a lttle shocked to see your report of 3.2M hits for the correct
speeling versus 1.8M for incorrect. This is a ratio of just 1.8! Google
normally has much higher power than this, as the arbiter of spelling
disputes.
A quick google reveals however that your search is for
A typo in previous post: for latter, read former. Apologies for the
confusion.
**
This is a commercial communication from Commerzbank AG.\ \ T...{{dropped}}
__
[EMAIL PROTECTED]
Although this thread might be considered closed by some, I'd like to make a
late contribution, since I contributed the extractIts() function in the
'its' package.
See ?itsSubset, which gives details of:
extractIts(x,weekday=FALSE,find=c(all,last,first),period=c(week,mon
th),partials=TRUE,select)
If you use priceIts() in package 'its' (Irregular Time Series), you get
similar functionality, and the labelling etc in plot() recognizes the
calendar. You can also do further calendar-based extractions, etc.
- Giles
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL
I get different results from match.call(), according to whether a function
is dispatched via S3 or S4. Specifically, when I use S4 dispatch in the
following example, the match.call() result is of length 1 less than I
expect. I need to add an extra comma to get the same results as in the S3
If you wish to 'skip' (i.e. not interpolate) weekends in its, you could use
the following:
prices - priceIts(instrument=ongc.ns)
plot(union(prices,newIts(start=start(prices),end=end(prices))),interp=none
)
- Giles
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED]
1. I want to extend the 'array' class, and prefer to use S4 in the belief
that this is the best structure for new projects (as the documentation
says). I actually wish to do something similar to the excellent Oarray by
Jonathan Rougier, but as this class is S3, I can't see how to extend it by
S4.
You could take a look at the irregular time-series (its) package
on CRAN. Your series is certainly irregular, and possibly a little
er... sparse. Anyway, the following might get you started:
require(its)
mydates - c(28.8.1962,27.6.1977,19.7.1989,26.6.1995,26.7.1999)
data -
You might wish to have a look at the 'its' package for irregular
time-series on CRAN. If your prices are in an its called price,
then the following will get you on your way. Since it is not efficient
either in storage or computation, I offer it because it might be
convenient for display,
The following is an example of a dataframe containing times,
plus some numeric data.
foo - c(12:39:26,12:40:22,12:41:19)
bar - data.frame(foo,1:3,11:13)
Note that the times are of class 'factor' (their class changes
in this case, as they go into the dataframe).
To convert this dataframe to an
I'm not sure what a calendar time series is, but it may be helpful
to consider it as an irregular time series, depending on what
analysis or display you are wanting to do. As you say, there are
packages (including 'its') for this purpose.
- Giles
-Original Message-
From: Brian
Well, I'm not sure I understand the question exactly,
but you might want to have a look at the package 'its',
as Achim said.
A practical example might look like:
You have a .csv file as follows (I have chosen the date
format at random).
,x,y
Monday 08-Sep-2003,1,11
Monday 15-Sep-2003,2,22
You may find the irregular time-series (its) package on CRAN
helpful.
If your raw data were in a csv file thus:
x
april 26 2002 15:00:00 1.1
april 26 2002 15:15:00 1.2
april 26 2002 15:30:00 1.3
april 26 2002 15:45:00 1.4
Then you could read it in thus:
Financial data, as you point out, is generally irregular, which is
in essence what prompted the devlopment of the irregular time-series
(its) package, which is posted on CRAN (v0.1.2 posted today, incidentally).
In the its class, the time-stamps of the rows of a matrix are represented
using the
One thing you could do is to use the 'its' (irregular time-series)
package on CRAN.
e.g. using a trivial dataset
require(its)
its.format(%Y-%m-%d) #defines text format of dates in dimnames
df - data.frame(1:3,(1:3)^2)
dimnames(df) - list(c(2003-01-03,2003-01-06,2003-01-07),letters[1:2])
The way to control number of tickmarks in plot is via par(lab)
par(lab=c(5,5,7)) #the default
plot(rnorm(20),rnorm(20))
par(lab=3*c(5,5,7))
plot(rnorm(20),rnorm(20))
However this does not work for axis.POSIXct, which the function
called by the plot method for 'its'. I am not sure why this is.
One solution is to use the Irregular Time-Series (its) package
on CRAN.
LeafDig - cbind(Leafminers,Diglyphus)
dimnames(LeafDig)[[1]] -
c(05/02/03,05/09/03,05/16/03,05/23/03,05/30/03,06/07/03,06/14/0
3)
require(its)
its.format(%m/%d/%y)
plot(its(LeafDig),format=%d %b %y)
You can select date
Jan
The function readcsvIts reads a csv file into a matrix. To convert
the matrix into an 'its', apply the its function to the matrix.
So for your example
its.format(%m/%d/%Y)
test - its(readcsvIts(filename))
is.its(test)
TRUE
The its documentation for readcsvIts states incorrectly that an
You might wish to have a look at the package 'its' for handling irregular
time-series. If your data is in a .csv file, the following would enable
you to handle the data in its irregular form.
its.format(%m/%d/%Y)
readcsvIts(filename)
- Giles
-Original Message-
From: Jan Verbesselt
to raise, and the documentation
provided does not answer your questions, I suggest you contact
me off-list.
Regards
Giles Heywood
-Original Message-
From: Fan [mailto:[EMAIL PROTECTED]
Sent: 17 August 2003 17:12
To: Heywood, Giles
Subject: Re: [R] New package: irregular time-series
I have uploaded to CRAN a new package named 'its' (Irregular Time-Series).
It
implements irregular time-series as an S4 class, extending the matrix class,
and records the time-stamp of each row in the matrix using POSIX. Print,
plot,
extraction, append, and related functionality are available.
A solution is at hand using the 'irts' (irreglar time-series)
class from package tseries.
If your raw data is in a csv file, you could proceed as follows:
mydata - read.csv(filename,header=TRUE)
basedate - as.POSIXct(strptime(2003-01-01 00:00:00,format=%Y-%m-%d %X))
rawdates -
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