In addition, we could create a function to.df which converts a zoo
object to a data frame assuming that any column that only contains
1:nlevels is a factor with the indicated level names. Use to.df just
before plotting:
library(zoo)
set.seed(1)
f <- zoo(factor(sample(3, 10, replace = TRUE)))
x <-
Thank you.
I will try to get the book, althoug I am not sure if I with my tiny
knowledge of mathematics will be able to digest it.
Meanwhile I tried to make 7 min average and then to reanalyze by spectrum,
but the output was not very convincing.
Regards
Petr Pikal
[EMAIL PROTECTED]
Rolf Tur
On Thu, 16 Aug 2007, Felix Andrews wrote:
list(...),
I am working with environmental time series (eg rainfall, stream flow)
that have attached quality codes for each data point. The quality
codes have just a few factor levels, like "good", "suspect", "poor",
"imputed". I use the quality codes i
list(...),
I am working with environmental time series (eg rainfall, stream flow)
that have attached quality codes for each data point. The quality
codes have just a few factor levels, like "good", "suspect", "poor",
"imputed". I use the quality codes in plots and summaries. They are
carried throu
On 16/08/2007, at 12:26 AM, Petr PIKAL wrote:
> Dear all
>
> Please help me with analysis of some periodic data.
>
> I have an output from measurement each minute and this output is
> modulated
> by rotation of the equipment (approx 6.5 min/revolution). I can easily
> spot this frequency from
>
Dear all
Please help me with analysis of some periodic data.
I have an output from measurement each minute and this output is modulated
by rotation of the equipment (approx 6.5 min/revolution). I can easily
spot this frequency from
spectrum(mydata, some suitable span)
However from other anal
On Mon, 16 Jul 2007, livia wrote:
>
> Hi all, I have got some time series data. Data[[1]] is the data in the format
> "1975-12-05 1975-12-12 1975-12-19...", data[[2]] is the time series data. I
> would like to generate the time series format as
> 1975-12-05 1.5
> 1975-12-12 2.3etc.
>
> I am thin
Check out the zoo package:
Lines <- "1975-12-05 1.5
1975-12-12 2.3
"
library(zoo)
# replace next line with z <- read.zoo("myfile.dat")
z <- read.zoo(textConnection(Lines))
plot(z)
z
vignette("zoo") # gives more info
On 7/16/07, livia <[EMAIL PROTECTED]> wrote:
>
> Hi all, I have got some time
Hi all, I have got some time series data. Data[[1]] is the data in the format
"1975-12-05 1975-12-12 1975-12-19...", data[[2]] is the time series data. I
would like to generate the time series format as
1975-12-05 1.5
1975-12-12 2.3etc.
I am thinking about cbind(data[[1]],data[[2]]), but it re
D)'; r-help@stat.math.ethz.ch
Subject: Re: [R] Time series\optimization question not R question
Your approach obviously won't give you the same result as when the
likelihood is optimized jointly with A and \beta. However, you can maximize
the likelihood over \beta for different values of A, w
22, 2007 10:29 AM
To: r-help@stat.math.ethz.ch
Subject: [R] Time series\optimization question not R question
This is a time series\optimization rather than an R question : Suppose I
have an ARMA(1,1) with
restrictions such that the coefficient on the lagged epsilon_term is
related to the coefficient
This is a time series\optimization rather than an R question : Suppose I
have an ARMA(1,1) with
restrictions such that the coefficient on the lagged epsilon_term is
related to the coefficient on
The lagged z term as below.
z_t =[A + beta]*z_t-1 + epsilon_t - A*epsilon_t-1
So, if I don't have a fa
Can you give us a little more information? Are you
expecting N times to be record and want to know if you
only have N-x times or are you expecting N entries but
want to know if some are NA? Etc, etc...
Also are you actually recording the times or is the
sampling setup just taking a sample every
Jessica,
> I am working with a data file which is the record of precipitation
> measurement normaly done every 10 minutes. I would like to check if there
> are missing times in my data file.
>
> Is there a function existing able to check for that in R ?
I'd use max(diff(time))==min(diff(time)).
Dear all,
I am working with a data file which is the record of precipitation
measurement normaly done every 10 minutes. I would like to check if there
are missing times in my data file.
Is there a function existing able to check for that in R ?
Thanks by advance,
Jessica
On Tue, 24 Apr 2007, Tomas Mikoviny wrote:
> Hi everybody,
>
> I work with data with following pattern
>
> > comm
>
> "Date" "Value"
> 1 4/10/2007 361.2
> 2 4/11/2007 370.1
> 3 4/12/2007 357.2
> 4 4/13/2007 362.3
> 5 4/16/2007 363.5
Hi everybody,
I work with data with following pattern
> comm
"Date" "Value"
1 4/10/2007 361.2
2 4/11/2007 370.1
3 4/12/2007 357.2
4 4/13/2007 362.3
5 4/16/2007 363.5
6 4/17/2007 368.7
7 4/18/2007
hello,
I have plots http://uosis.mif.vu.lt/~roka5178/time series.JPG
I must to do almost the same. I do not have any data.
but i know that this is time series data with parabolic changeable mean.
I can not generate tie series data with parabolic mean.
how can i do this?
and have http://uosis.mif.v
th
>suggestions for other readings), obviously geared to S and R, that is a
>good jumping-off place.
>
>Ben Fairbank
>
>
>-Original Message-
>From: [EMAIL PROTECTED]
>[mailto:[EMAIL PROTECTED] On Behalf Of lamack lamack
>Sent: Thursday, February 01, 2007 3:12 PM
is a
good jumping-off place.
Ben Fairbank
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of lamack lamack
Sent: Thursday, February 01, 2007 3:12 PM
To: R-help@stat.math.ethz.ch
Subject: [R] time series analysis
Does anyone know a good introductory book o
Does anyone know a good introductory book or tutorial about time series
analysis? (time
series for a beginner).
Thank you so much.
John Lamak
_
Descubra como mandar Torpedos SMS do seu Messenger para o celular dos seus
amigos. htt
Here's an example illustrating a way to get a second y axis that has
a different range:
x <- 1:10
y1 <- 2*x
y2 <- 100-3*x+rnorm(10)
par(mar=c(5.1,4.1,4.1,4.1))
plot(x,y1)
par(new=TRUE)
plot(x,y2,xaxt='n',yaxt='n',xlab='',ylab='',pch=3)
axis(4)
mtext('y2',side=4,line=2.5)
-Don
At 2:18 PM +0530
ot;r-help@stat.math.ethz.ch"
Subject:Re: [R] Time series plot
> Dear Gabor,
>
> Thank you very much for your letter. Actually I got partial solution
> from your suggestion. Still I am fighting with defining a secondary
> axis. More pecisely, suppose I have followin
Check out #2 in:
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/85801.html
and RSiteSearch("axis(4") to find additional examples.
On 1/4/07, Arun Kumar Saha <[EMAIL PROTECTED]> wrote:
> Dear Gabor,
>
> Thank you very much for your letter. Actually I got partial solution from
> your suggestion.
Dear Gabor,
Thank you very much for your letter. Actually I got partial solution from
your suggestion. Still I am fighting with defining a secondary axis. More
pecisely, suppose I have following two dataset:
x = c(1:10)
y = x*10
To plot x I can simply write plot(x, type='l'), here the"y-axis" ta
You can use read.zoo in the zoo package to read in the data
and then see:
https://www.stat.math.ethz.ch/pipermail/r-help/2006-December/122742.html
See ?axis for creating additional axes with classic graphics and
library(lattice)
?panel.axis
in lattice graphics. Search the archives for examples
Dear all R users,
Suppose I have a data set like this:
date price
1-Jan-02 4.8803747
2-Jan-02 4.8798430
3-Jan-02 4.8840133
4-Jan-02 4.8803747
5-Jan-02 4.8749683
6-Jan-02 4.8754263
7-Jan-02 4.8746628
8-Jan-02 4.8753500
9-Jan-02 4.8882416
10-Jan-02
First, I'd write down a model for how your stochastic process
relates to independent, normal observations with mean 0 and standard
deviation 1. You want a lognormal series, so I'd start by generating a
normal series and the compute 'exp' of that. If you'd like more help
from this listse
On 28 September 2006 at 22:00, David Kaplan wrote:
| Greetings,
|
| Are there R packages that perform time-series analyses - particularly
| estimation of ARIMA models along with unit-root tests? I know that
| FinMetrics in the S-Plus program will do it, but I'm looking for R
| packages, as we
Greetings,
Are there R packages that perform time-series analyses - particularly
estimation of ARIMA models along with unit-root tests? I know that
FinMetrics in the S-Plus program will do it, but I'm looking for R
packages, as well any reference material for estimating time-series'
models in
Hi everybody
I'm trying to simulate a stochastic process in R. I would like consider n
log normal time series. The first time serie has a growth rate lower than
the second and so on. the initial time of the first serie is lower than the
initial time of the second and so on. In the long run the ser
Gabor Grothendieck wrote:
> When the axis labelling does not work well you will have to do it yourself
> like this. The plot statement is instructed not to plot the axis and then
> we extract into tt all the dates which are day of the month 1. Then
> we manually draw the axis using those.
>
> li
When the axis labelling does not work well you will have to do it yourself
like this. The plot statement is instructed not to plot the axis and then
we extract into tt all the dates which are day of the month 1. Then
we manually draw the axis using those.
library(zoo)
set.seed(1)
z <- zoo(runif(
Hi,
I'm using zoo because it can automatically label the months of a time
series composed of daily observations.
This works well for certain time series lengths, but not for others, e.g.:
While:
> library(zoo)
> plot(zoo(runif(10), as.Date("2005-06-01") + 0:50))
Shows up the months and day
Spencer Graves wrote:
> I know of no software for time series clustering in R. Google
> produced some interesting hits for "time series clustering". If you
> find an algorithm you like, the author might have software.
> Alternatively, the algorithm might be a modification of something
I know of no software for time series clustering in R. Google
produced some interesting hits for "time series clustering". If you
find an algorithm you like, the author might have software.
Alternatively, the algorithm might be a modification of something
already available in R. If
Dear Listers:
I happened to have a problem requiring time-series clustering since the
clusters will change with time (too old data need to be removed from data
while new data comes in). I am wondering if there is some paper or reference
on this topic and there is some kind of implementation in R?
will appear as labels and tickmarks...
HTH
Dubravko
YOU WROTE:
[R] Time series plot
Jiang, Jincai \(Institutional Securities Management\)
Tue, 02 May 2006 15:28:13 -0700
I have some time series data like
01/02/1990 0.531 0.479
01/03/1990 0.510 0.522
01/06/1990 0.602
Try this (where you can replace textConnection(L) with name
of file containing data):
L <- "01/02/1990 0.531 0.479
01/03/1990 0.510 0.522
01/06/1990 0.602 0.604"
library(zoo)
z <- read.zoo(textConnection(L), format = "%m/%d/%Y")
plot(z, plot.type = "single")
This will give more info on zoo:
lib
I have some time series data like
01/02/1990 0.531 0.479
01/03/1990 0.510 0.522
01/06/1990 0.602 0.604
there is no weekends and holidays.
how do I graph them in a single plot that the x-axis is the dates and
the y-axis is the time series?
Thank you
Regards,
Jincai Jiang
(Office) 212-761-39
You might want to look at the dyn package. It allows
time series in model formulae, e.g. this aligns UKgas
and diff(UKgas) properly:
dyn$lm(UKgas ~ diff(UKgas))
dyn$ transforms the above to
dyn(lm(dyn(UKgas ~ diff(UKgas
and the inner dyn adds class "dyn" to the formula's class vector
The problem here is that you called model.frame() with (I presume, the
'factory-fresh' default) na.action=na.omit, and model.frame is documented
to remove tsp attributes in that case.
Use model.frame(..., na.action = NULL), e.g.
model.frame(~UKgas, na.action=NULL)[[1]]
is a time series
Dear List
The UKgas data is stored as an object of class 'ts'. I am trying to use "UKgas"
in a formula as argument to a function. However, I do not know how to access
the 'time series' information in the response (such as start() end() etc.).
Here is a boiled down example.
ssm <- function(fo
I don't have a direct answer to your question, but in case you are
interested in a general introduction to time series capabilties in R, I
will suggest the following:
1. Ch. 14 in Venables and Ripley (2002) Modern Applied Statistics
with S, 4th ed. (Springer)
2.
I am attempting to value convertible bonds through a Monte Carlo approach.
I want to express call schedules as date-price tuples. Naturally, these
tuples need to be expanded to match the frequency of the innovations in the
MC process.
1. Is there a straigh-forward way to accomplish this "expan
If this were my problem, I might start by considering each
stimulus-response pair as a one observation, and I'd break the MEG into
separate time series, each starting roughly 1 second before the stimulus
and ending roughly 1 second after. If you've averaged many of these,
I'm guessin
Hi,
I have some general questions about statistical analysis for a research
dataset and a request for advice on using R and associated packages for a
valid analysis of this data. I can only pose the problem as how to run
multiple ANOVA tests on time series data, with reasonable controls of the
fa
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1
I am just curious about this...
Would you have any experience or recommendation on a suitable R
package to start with?
Many thanks in advance,
- --
Jean-Luc Fontaine http://jfontain.free.fr/
-BEGIN PGP SIGNATURE-
Version: GnuPG v1.4.2.1 (GNU
Hi List,
I have a time series of 122 values, actualy it is a time series of daily
indian monsoon rainfall. now i want to filter this time series for a particular
oscilation say 10 to 20days oscilation. i want to find out what amount of
variance is explained by this mode. Which package is a
Have you read Pinheiro and Bates (2000) Mixed-Effects Models in S and
S-Plus (Springer)? The latter part about nonlinear modeling with mixed
effects sounds like it could help you a lot.
1. Consistent with that, I might start by averaging over all 15
people, then making plo
Hi!
First of all: I'm a newbie to both statistics and R, so please be
patient with me... I do however, like R because I've been programming
(pascal, IDL, perl, C etc) and designing models since -92, but never
related to statistics.
Ok, here we go:
I've got a set of 15 people, all of them observed
I am working on automatic optimization of ARIMA parameters.
That takes a lot of computing power, which I would like to reduce by aggregating
and smoothing.
Any thoughts on the subject?
Suggested algorithms?
What is the best order? aggregate then smooth or smooth then aggregate?
Many thanks in adv
PROTECTED] wrote:
> Dear List,
>
> The purpose of this e-mail is to ask about R time series procedures - as a
> biologist with only basic time series knowledge and about a year's
> experience in R.
>
> I have been using ARIMAX models with seasonal components on seasonal data.
Quoting Spencer Graves <[EMAIL PROTECTED]>:
> 1. Have you read the appropriate chapter in Venables and Ripley
> (2002) Modern Applied Statists with S (Springer)? If no, I suggest you
> start there.
>
> 2. Have you worked through the vignettes associated with the "zoo"
> package?
1. Have you read the appropriate chapter in Venables and Ripley
(2002) Modern Applied Statists with S (Springer)? If no, I suggest you
start there.
2. Have you worked through the vignettes associated with the "zoo"
package? If no, you might find that quite useful. [Are
Wensui Liu wrote:
> TS is a huge topic. The book recomended by statisitcian might be
> different from the one recommended by econometrician. Finance guy
> might recommend another. Could you please be more specific?
My software (http://moodss.sourceforge.net) collects, archives in a
SQL database
TS is a huge topic. The book recomended by statisitcian might be different
from the one recommended by econometrician. Finance guy might recommend
another.
Could you please be more specific?
On 9/8/05, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote:
>
> There has been a few questions on the subj
There has been a few questions on the subject lately.
Is there any book on the subject, if possible with a computer processing flavor,
that you would highly recommend?
Many thanks in advance,
--
Jean-Luc
__
R-help@stat.math.ethz.ch mailing list
https
Dear List,
The purpose of this e-mail is to ask about R time series procedures - as a
biologist with only basic time series knowledge and about a year's
experience in R.
I have been using ARIMAX models with seasonal components on seasonal data.
However I am now moving on to annual data
On 9/4/05, Anette Nørgaard <[EMAIL PROTECTED]> wrote:
> About time series graphs, I need help to move on:
>
> A time series of data directly from a data logger comes in the dat
> format created below:
>
> year<-c(rep(2005,10))
> doy<-c(rep(173,5),rep(174,5))
> time<-c(15,30,45,100,115,15,30,45,10
About time series graphs, I need help to move on:
A time series of data directly from a data logger comes in the dat
format created below:
year<-c(rep(2005,10))
doy<-c(rep(173,5),rep(174,5))
time<-c(15,30,45,100,115,15,30,45,100,115)
dat1<-c(0.022128,0.035036,0.051632,0.071916,0.081136,0.07837,
About time series graphs, I need help to move on:
A time series of data directly from a data logger comes in the dat
format created below:
year<-c(rep(2005,10))
doy<-c(rep(173,5),rep(174,5))
time<-c(15,30,45,100,115,15,30,45,100,115)
dat1<-c(0.022128,0.035036,0.051632,0.071916,0.081136,0.07837,
On Mon, 2005-08-22 at 13:04 +0100, Prof Brian Ripley wrote:
> On Mon, 22 Aug 2005, javier garcia - CEBAS wrote:
>
> > My native language is spanish and I would need to do two changes in the
> > default xlabels in timeseries plots:
>
> What sort of plots are you talking about here? (Not tsplot or
On Mon, 22 Aug 2005, javier garcia - CEBAS wrote:
> My native language is spanish and I would need to do two changes in the
> default xlabels in timeseries plots:
What sort of plots are you talking about here? (Not tsplot or plot.ts,
for example.) I think you are perhaps talking about plots of
Hi all;
My native language is spanish and I would need to do two changes in the
default xlabels in timeseries plots:
a) For timeseries that span more than one year, there are just xlabs and
tickmarks for the beginning of each year. Could I add extra tick marks for
each month easily?
b) The x
On Fri, 22 Jul 2005 14:46:31 -0400 Sean Davis wrote:
> Preface: this is a statistical question more than an R question.
>
> I have a vector of numbers (assume a regular time series). Within
> this time series, I have a set of regions of interest (all of
> different lengths) that I want to compa
Preface: this is a statistical question more than an R question.
I have a vector of numbers (assume a regular time series). Within this
time series, I have a set of regions of interest (all of different
lengths) that I want to compare against a "baseline" (which is known).
There is some auto
Paul,
Thank you so much for your thoughtful reply. I agree - there are many
possible descriptions for my data, and I realize that I don't want to
get bogged down with figuring out the 'best' model if something simple
will work well. For me, I think the difficulty is going to be handling
the cumula
We are leveraging too far on speculation, at least from what I can
see. PLEASE do read the posting guide!
"http://www.R-project.org/posting-guide.html";. In particular, try the
simplest example you can find that illustrates your question, and
explain your concerns to us in terms of
Dear Brett:
There are books for this topic that are more narrowly tailored to your
question. Lindsey's Models for Repeated Measurements and Diggle, et al's
Analysis of Longitudinal Data. Lindsey offers an R package on his web
site. If you dig around, you will find many modeling papers on this,
Thanks for the suggestion. Is such a model appropriate for count data?
The library you reference seems to just be form standard regressions
(ie those with continuous dependent variables).
Thanks,
Brett
On 7/16/05, Spencer Graves <[EMAIL PROTECTED]> wrote:
> Have you considered "lme" in
Have you considered "lme" in library(nlme)? If you want to go this
route, I recommend Pinheiro and Bates (2000) Mixed-Effect Models in S
and S-Plus (Springer).
spencer graves
Brett Gordon wrote:
> Hello,
>
> I'm trying to model the entry of certain firms into a larger num
Hello,
I'm trying to model the entry of certain firms into a larger number of
distinct markets over time. I have a short time series, but a large
cross section (small T, big N).
I have both time varying and non-time varying variables. Additionally,
since I'm modeling entry of firms, it seems like
Hello,
I'm trying to model the entry of certain firms into a larger number of
distinct markets over time. I have a short time series, but a large
cross section (small T, big N).
I have both time varying and non-time varying variables. Additionally,
since I'm modeling entry of firms, it seems like
On Fri, 8 Jul 2005, yyan liu wrote:
> Hi:
> I have two time series y(t) and x(t). I want to
> regress Y on X. Because Y is a time series and may
> have autocorrelation such as AR(p), so it is not
> efficient to use OLS directly. The model I am trying
> to fit is like
> Y(t)=beta0+beta1*X(t)+rho*
On 7/8/05, yyan liu <[EMAIL PROTECTED]> wrote:
> Hi:
> I have two time series y(t) and x(t). I want to
> regress Y on X. Because Y is a time series and may
> have autocorrelation such as AR(p), so it is not
> efficient to use OLS directly. The model I am trying
> to fit is like
> Y(t)=beta0+beta1*
Hi:
I have two time series y(t) and x(t). I want to
regress Y on X. Because Y is a time series and may
have autocorrelation such as AR(p), so it is not
efficient to use OLS directly. The model I am trying
to fit is like
Y(t)=beta0+beta1*X(t)+rho*Y(t-1)+e(t)
e(t) is iid normal random error. Anybo
Thanks, Gabor. Reading your suggestion (and a previous one as well) I
realized I surely expressed myself quite badly when asking the
question.
Luckily one person privately suggested the following solution, which
is exactly what I was looking for:
x[time(x)==2] <- 0
This works wonderfully. Howeve
On 4/26/05, Fernando Saldanha <[EMAIL PROTECTED]> wrote:
>
> I tried to assign values to specific elements of a time series and got
> in trouble. The code below should be almost self-explanatory. I wanted
> to assign 0 to the first element of x, but instead I assigned zero to
> the second element
Fernando Saldanha schrieb:
I tried to assign values to specific elements of a time series and got
in trouble. The code below should be almost self-explanatory. I wanted
to assign 0 to the first element of x, but instead I assigned zero to
the second element of x, which is not what I wanted. Is the
I tried to assign values to specific elements of a time series and got
in trouble. The code below should be almost self-explanatory. I wanted
to assign 0 to the first element of x, but instead I assigned zero to
the second element of x, which is not what I wanted. Is there a
function that will all
Thanks, Achim,
I managed to do what I wanted, thanks to your suggestion, except for
one thing. When I called ts.intersect I could only provide numerical
arguments (more precisely, objects that can be coerced into time
series, I guess). That means I was not able to pass the original
row.names that
On Tue, 12 Apr 2005 18:47:21 -0400 Fernando Saldanha wrote:
> Can one also predetermine a set and then estimate all the models one
> wants to compare using the zoo package?
Sure, you can merge() several series first and then pass this as the
data argument to lm(). See the vignette of the zoo pack
Can one also predetermine a set and then estimate all the models one
wants to compare using the zoo package? Or can that be done only with
the tseries package?
Thanks.
FS
On 4/12/05, Achim Zeileis <[EMAIL PROTECTED]> wrote:
> Fernando:
>
> > This maybe a basic question, but I have spent severa
Fernando:
> This maybe a basic question, but I have spent several hours
> researching and I could not get an answer, so please bear with me. The
> problem is with time series in the package tseries.
BTW: the `tseries' package is not involved here.
> As the example
> below shows, the time series
This maybe a basic question, but I have spent several hours
researching and I could not get an answer, so please bear with me. The
problem is with time series in the package tseries. As the example
below shows, the time series can get misaligned, so that bad results
are obtained when doing regressi
On Wed, 16 Mar 2005 [EMAIL PROTECTED] wrote:
I'm using a dataset with unequally spaced time series
and I'd want to know if there is in R some function in
order to calculate the autocorrelation function, because
acf() in stats package cannot calculate it, because I
have many missing data, and data a
see packages zoo and its on cran.
> -Original Message-
> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
> Sent: Wednesday, March 16, 2005 7:17 PM
> To: r-help@stat.math.ethz.ch
> Subject: [R] Time Series
>
>
> Hello,
>
> I'm using a dataset with
Hello,
I'm using a dataset with unequally spaced time series
and I'd want to know if there is in R some function in
order to calculate the autocorrelation function, because
acf() in stats package cannot calculate it, because I
have many missing data, and data are not equally spaced.
And if so, is i
Hello, Kum-Hoe:
Have you considered the "strucchange" package? Inspired and
informed by Gabor's comments, it looks to me at the moment like this
package help you fit intervention / regression fits with time series /
ARMAX models with minimum hassle.
hope this helps.
spencer
Spencer Graves pdf.com> writes:
:
: Thanks. Which package(s) do you prefer for which purposes?
'ts' can be used for regularly spaced time series and supports
monthly and quarterly dates. The other ones listed below supports
irregular time series.
zoo's design goals are consistency w
Hi, Gabor:
Thanks. Which package(s) do you prefer for which purposes?
Best Wishes,
Spencer Graves
Gabor Grothendieck wrote:
Spencer Graves pdf.com> writes:
: In particular, what's the preferred way to keep track of dates with
: time series? I tried assigning a "Date" objec
Spencer Graves pdf.com> writes:
: In particular, what's the preferred way to keep track of dates with
: time series? I tried assigning a "Date" object somehow to a "ts"
: object, so far without success. Two of my attempts are as follows:
:
: > tst2 <- ts(1:11, frequency=365,
: +
Hello, All:
What documentation do you recommend for someone trying to learn how
to analyze time series in R beyond ch. 14 in Venables and Ripley (2002)
Modern Applied Statistics with S, 4th ed. (Springer), and a not-quite
random walk through the documentation on commands like arima
## The following corrects what I believe to have been an error in my
previous post:
Two time series questions:
I. FITTING TRANSFER FUNCTIONS WITH LAGS: Consider the following toy
example:
dates <- paste(11:21, "/01/2005", sep="")
Dates <- as.Date(dates, "%d/%m/%Y")
set.seed(1)
DF <-
Two time series questions:
FITTING TRANSFER FUNCTIONS WITH LAGS: Consider the following toy example:
> dates <- paste(11:21, "/01/2005", sep="")
> Dates <- as.Date(dates, "%d/%m/%Y")
> set.seed(1)
> DF <- data.frame(date=Dates, y=rnorm(11), x=rnorm(11, 3))
> arima(DF$y, c(1,0,0), xreg=lag
In a dataframe you call one of the variable (or the column) connecting the
name of the column to the dataframe name by means of the $ sign:
so z$energy is the column named energy in the dataframe z.
I don't know how to do the same with a multi-variable time-series. I tried
both z.energy, z$ener
Rene Pineda wrote:
I need information about space state models in structural model and kalman
filtering. I have a univariate time serie and i nedd aplicate space state model
Please use an informative subject line! See
?arima
and the references therein.
See also the R Newsletter, volume 2/2, 200
Costas Vorlow wrote:
Hello,
I am trying to rotate by 90 degrees a time series plot. So I need the
time axis to be the vertical one. Is there an easy way?
No, you have to do it manually, AFAIK.
Uwe Ligges
I couldn't guess anything from the help pages.
Apologies for a silly question.
Regards,
Cost
Hello,
I am trying to rotate by 90 degrees a time series plot. So I need the
time axis to be the vertical one. Is there an easy way?
I couldn't guess anything from the help pages.
Apologies for a silly question.
Regards,
Costas
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