Displaying 20 results from an estimated 90 matches similar to: "Lag representation in ccf() while zoo object is used?"
2009 Jul 15
1
Plotting hourly time-series data loaded from file using plot.ts
Hello everyone,
I am just a tyro in R and would like your kindly help for some
problems which I've been struggling for a while but still in vain.
I have a time-series file (with some missing value ) which looks like
time[sec] , Factor1 , Factor2
00:00:00 01.01.2007 , 0.0000 , 0.176083
01:00:00 01.01.2007 , 0.0000 , 0.176417
[ ... ]
11:00:00 10.06.2007 , 0.0000 , 0.148250
12:00:00 10.06.2007
2012 Jun 07
0
na.pass option in ccf function
Hi everyone,
I have been working with the ccf function recently, and in particular to
do my calculations I have been using "na.action = na.pass". I noticed
that the help documentation mentions that with this option the computed
estimate may not be a valid autocorrelation sequence and was wondering
if anyone could clarify what this means.
In particular, the example below gives
2008 May 08
1
significance threshold in CCF
Hi everyone,
When the CCF between two series of observations is plotted in R, a line indicating (presumably) the significance threshold appears across the plot. Does anyone know how this threshold is determined (it is different for each set of series) and how its value can be extracted from R? I've tried saving the CCF into an object and unclassing the object, but there's nothing there to
2002 Jun 11
1
extra 0 in ccf
im using R 1.5.0 on redhat.
when i use ccf in the ts library i get, what i think is, an incorrect
entry in the lag and acf components. as an example, look at the second
entry in the lag and acf components below:
> library(ts)
> tmp <- ccf(ts(rnorm(3)),ts(rnorm(3)),plot=F)
> tmp$lag
, , 1
[,1]
[1,] -1
[2,] 0
[3,] 0
[4,] 1
> tmp$acf
, , 1
[,1]
[1,]
2006 Apr 13
1
How does ccf() really work?
I can't understand the results from cross-correlation function ccf()
even though it should be simple.
Here's my short example:
*********
a<-rnorm(5);b<-rnorm(5)
a;b
[1] 1.4429135 0.8470067 1.2263730 -1.8159190 -0.6997260
[1] -0.4227674 0.8602645 -0.6810602 -1.4858726 -0.7008563
cc<-ccf(a,b,lag.max=4,type="correlation")
cc
Autocorrelations of series 'X',
2006 Sep 15
1
"ccf versus acf"
I am trying to run a cross-correlation using the "ccf()" function. When
I select plot = TRUE in the ccf() I get a graph which has ACF on the
y-axis, which would suggest that these y-values are the auto-correlation
values.
How should I adjust the code to produce a plot that provides the
cross-correlation values?
Here is my code:
w002dat <-
2007 Mar 29
1
ccf time units
Hi,
I am using ccf but I could not figure out how to calculate the actual lag in
number of periods from the returned results. The documentation for ccf
says:"The lag is returned and plotted in units of time". What does "units of
time" mean? For example:
> x=ldeaths
> x1=lag(ldeaths,1)
> results=ccf(x,x1)
> results
Autocorrelations of series 'X', by lag
2009 Apr 20
2
Cross-Correlation function (CCF) issues
Dear all,
I have two series of returns and I want to find the cross-correlations
between these two series. I know of the ccf, but it does not work as I'd
like
if i type
ccf(x,y,lag.max=20,type="correlation",plot=FALSE)
i got the error message
Error in na.fail.default(ts.intersect(as.ts(x), as.ts(y))) :
missing values in object
So i found that somebody suggested to type
2009 Jul 03
0
A fast version of ccf () accepting missing values ?
Dear R-Helpers,
I need to compute cross-correlation on two signals wich may contain
missing values.
One cannot pass "Na.action=na.pass" to the ccf() function.
So, I wrote a naive function of my own (see below).
Unsurprisingly, this function is not very fast.
Do you think that it is possible to do better, or should I accept my fate
?
Bruno.
my_ccf <- function (X, Y,
2010 Apr 13
0
ccf problem (cross-correlation)
Hi all,
I have a problem concerning my understanding of the cross-correlation (ccf)
function in R.
assume a time serie as:
> t<-seq(0,6.28,by=0.01);
> my_serie<-ts(sin(t),start=0,end=6.28,deltat=0.01)
then I generate an other one shifted by 12 time points:
> my_shifted_serie<-ts(sin(t),start=0+0.12,end=6.28+0.12,deltat=0.01)
if I do the cross-correlation I get that the two
2010 Apr 26
1
Why am I getting different results from cor VS ccf ?
Hi all,
I am getting different results from ccf and cor,
Here is a simple example:
set.seed(100)
N <- 100
x1 <- sample(N)
x2 <- x1 + rnorm(N,0,5)
ccf(x1,x2)$acf[ccf(x1,x2)$lag == -1]
cor(x1[-N], x2[-1])
Results:
> ccf(x1,x2)$acf[ccf(x1,x2)$lag == -1]
[1] -0.128027
> cor(x1[-N], x2[-1])
[1] -0.1301427
Thanks,
Tal
----------------Contact
2010 Jul 14
1
ccf function
Hello,
I am a very new R user and not a statistician so please excuse any over
explanation, I'm just trying to be as clear as possible.
I have performed a cross correlation of two time series (my columns) in a single
data setusing:
ccf(ts(A[rows,columnX]),(A[rows,columnY]), lag=NULL, type="correlation",plot=F)
I?am able to get the results (for example):
Autocorrelations of
2011 Jan 19
2
CCF and missing values.
Hi,
I have missing values in my time series. "na.action = na.pass" works for acf and pacf. Why do I get the following error for the ccf?
> ts(matrix(c(dev$u[1:10],dev$q[1:10]),ncol=2),start=1,freq=1)
Time Series:
Start = 1
End = 10
Frequency = 1
Series 1 Series 2
1 68.00000 138.4615
2 70.00000 355.5556
3 68.76000 304.3200
4 68.00000 231.4286
5 69.74194 357.4963
6
2011 Jun 30
0
CCF of two time series pre-whitened using ARIMA
Hi all,
I have two time series that I would like to correlate but as they are
autocorrelated, I am "pre-whitening" them first by fitting ARIMA models,
then correlating their residuals....as described in
https://onlinecourses.science.psu.edu/stat510/?q=node/75
However, http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm discusses some
issues with ARIMA in R. In particular, for issue 2, if
2005 Jun 17
1
ccf
Hello group,
For my research I should calculate the cross-correlation between two
time series.
I don't know if the function ccf can calculate this with series that
have NA values.
e.g. temperature:
15.5
NA
12.3
10.0
NA
14.2
15,3
....
Can you help me?
Thank you very much!
Laura
2008 Apr 23
1
ccf and covariance
Hi.
It's my understanding that a cross-correlation function of vectors x
and y at lag zero is equivalent to their correlation (or covariance,
depending on how the ccf is defined).
If this is true, could somebody please explain why I get an
inconsistent result between cov() and ccf(type = "covariance"), but a
consistent result between cor() and ccf(type = "correlation")?
Or
2012 Oct 11
2
ccf(x,y) vs. cor() of x and lagged values of y
Hi
I'm computing the correlation between two time-series x_t and y_t-1
(time-series lagged using the lag(y,-1) function) using the cor() function
and the returned value is different from the value of ccf() function at the
same lag. Any ideas why this is so?
Thanks in advance for any hints.
Mihnea
[[alternative HTML version deleted]]
2006 Nov 28
1
ccf documentation bug or suggeston (PR#9394)
On 11/28/2006 11:50 AM, A.I. McLeod wrote:
> Hi Duncan,
Hi Ian.
>
> ccf(x,y) does not explain whether c(k)=cov(x(t),x(t+k)) or d(k)=cov(x(t),x(t-k)) is calculated. The following example demonstrates
> that the c(k) definition is used:
> ccf(c(-1,1,rep(0,8)),c(1,rep(0,9)))
> However S-Plus acf uses the d(k) definition in their acf function.
I don't think our code looks
2009 Jan 20
2
Confidence intervals in ccf()
Hi,
I have been running the ccf() function to find cross-correlations of time
series across various lags. When I give the option of plot=TRUE, I get a
plot that gives me 95% confidence interval cut-offs (based on sample
covariances) for my cross-correlations at each lag. This gives me a sense of
whether my cross-correlations are statistically significant or not.
However, I am unable to get R to
2006 Oct 02
1
CCF and ACF
Dear all,
given two numeric vectors x and y, the ACF(x) at lag k is
cor(x(t),x(t+k)) while the CCF(x,y) at lag k is cor(x(t),y(t-k)). See
below for a simple example.
> set.seed(1)
> x <- rnorm(10)
> y <- rnorm(10)
> x
[1] -0.6264538 0.1836433 -0.8356286 1.5952808 0.3295078 -0.8204684
0.4874291 0.7383247 0.5757814 -0.3053884
> y
[1] 1.51178117 0.38984324