similar to: lapply with functions with changing parameters

hello, i have been using ccf() to look at the correlation between lightning and electrogamnetic data. for the most part it has worked exactly as expected. however, i have come across something that puzzles me a bit: > x <- c(1, 0, 1, 0, 1, 0) > y <- c(0, 0, 0, 0, 0, 0) > ccf(x, x, plot = FALSE) Autocorrelations of series 'X', by lag -4 -3 -2 -1 0

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

I am new to R and new to time series modeling. I have a set of variables (var1, var2, var3, var4, var5) for which I have historical yearly data. I am trying to use this data to produce a prediction of var1, 3 years into the future. I have a few basic questions: 1) I am able to read in my data, and convert it to a time series format using 'ts.' data_ts <- ts(data, start = 1988, end =

Dear R-users, I have been using R and its core-packages with great satisfaction now for many years, and have recently started using the "ccf" function (part of the "stats" package version 2.16.0), about which I have a question. The "ccf"-algorithm for calculating the cross-correlation between two time series always calculates the mean and standard deviation per time

Dear All, I am studying some process measurement time series in R and trying to identify time delays using cross-correlation function ccf. The results have however been bit confusing. I found a couple of years old message about this issue but unfortunately wasn't able to find it again for a reference. For example, an obvious time shift is observed between the measurements y1 and y2 when the

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

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

Hello! I found the ccf function gives different estimates than the simple lag correlations. Why is that? This is my code: set.seed(20) x<-rnorm(20) y<-x+rnorm(20,sd=0.3) print("R CCF:") print(ccf(x,y,lag.max=2,plot=F)) myccf<- c( cor(y[-(1:2)],x[-(19:20)]) , cor(y[-1],x[-20]), cor(y,x), cor(x[-1],y[-20]),cor(x[-(1:2)],y[-(19:20)]) )

I browsed the web and the archive, and apart from someone asking whether anyone else had rsync problems after installing OpenSSH 3.4, I came up empty. Can anyone point me in the right direction to debug this? I've got over 50 GB to keep in sync, and don't know of another elegant way to do it. Environment: source system = bb: - Linux version 2.4.7-10smp

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 <-

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

Does anyone know of any R code for computing partial cross-correlation? I have examples of cross correlation functions (ccfs) that are not smooth but rather consist of a peak of several high values in consecutive lags, with sharp drops on either side. This indicates that y(t) is a function of some average of x(t-tau) at the set of lags tau over which the ccf is high. I could sort out these

I ran across this problem when playing with ccf(). Its function call is >function (x, y, lag.max = NULL, type = c("correlation", "covariance"), plot = TRUE, na.action = na.fail, ...) Internally, ccf() calls plot(), which digs up plot.acf() whose default style is type='h' . I wanted to pass the argument type='l' to the plotting routine, but of

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

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

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

Dear R gurus, I would like to use the ccf function on two matrices that are each 196000 x 12. Ideally, I want to be able to go row by row for the two matrices using apply for the ccf function and get one 196000 X 1 array output. The apply function though wants only one array, no? Basically, is there a way to use apply when there are two arrays in order to do something like correlation on a row

Dear R-help, How could a cross-correlation plot be optimized such that the relationship between seasonal time-series can be studied? We are working with strong seasonal time-series and derived a cross-correlation plot to study the relationship between time-series. The seasonal variation however strongly influences the cross-correlation plot and the plot seems to be ?rather? symmetrical (max

Dear All, I have 2 time-series data sets and would like to check the cross correlation. These data sets were set as a zoo object, called data, and in general look like: V1 V2 2007-01-01 00:00:00 0.0 0.176083 2007-01-01 01:00:00 0.0 0.176417 2007-01-01 02:00:00 0.0 0.175833 2007-01-01 03:00:00 0.0 0.175833 2007-01-01

Hello All, I'm having trouble with the ccf() function. I am trying to do cross-correlation between two time-series, but I keep getting an error message I don't know what to do with. This what I type and the error message I get: > ccf(ts(mod[,1]),ts(mod[,2]),na.action='na.exclude',type='cor') Error in "colnames<-"(`*tmp*`, value = c("ts(mod[,