similar to: A fast version of ccf () accepting missing values ?

Dear R-Users, I have a question concerning the determination of breakpoints and comparison of slopes from broken-line regression models. Although this is rather a standard problem in data analysis, all information I gathered so far, did not answer my questions. I added a subsetted example of my data. Basically it is a timeseries of recorded phenotypes in three different groups of plants. You

>> 2. ABI docs for Darwin (x86, x86_64, ppc, ppc64) you might find >> somewhere @apple.com. There you can have all 3 types of PIC code: >> static (no pic at all), DynamicNoPIC and full PIC. > > Okay. We need documentation, what is the difference between > DynamicNoPIC and > full PIC ? The best way to figure this out is to run a small program through and look at

Hello, I am looking for some help with this question: how could I test structural breaks in a instrumental variables´s model? For example, I was trying to do something with my model with three time series. tax_ivreg <- ivreg(l_y ~ l_x2 + l_x1+ dl_y | lag(l_x2, -1)+lag(l_x2, -2)+ lag(l_x1, -1)+lag(l_x1, -2)+lag(l_y, -1)+lag(l_y, -2), data=tax1) summary(tax_ivreg) ## after estimating it,

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. 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

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

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 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

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

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

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

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 =

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

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'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]]

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

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,]

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',

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

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