similar to: Vector Autocorrelation Function in R?

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

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

Hello everybody, I am sorry if my questions are too simple or not easily understandable. I’m not a native English speaker and this is my first analysis using this function. I have a problem with a cross correlation function and I would like to understand how I have to perform it in R. I have yearly data of an independent variable (x) from 1982 to 2010, and I also have yearly data of a variable

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

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 R list As suggested by Prof Brian Ripley, I have tried to read acf literature. The main problem is I am not the statistician and hence have some problem in understanding the concepts immediately. I came across one literature (http://www.stat.nus.edu.sg/~staxyc/REG32.pdf) on auto-correlation giving the methodology. As per that literature, the auto-correlation is arrived at as per following.

Dear R list I am trying to understand the auto-correlation concept. Auto-correlation is the self-correlation of random variable X with a certain time lag of say t. The article "http://www.mit.tut.fi/MIT-3010/luentokalvot/lk10-11/MDA_lecture16_11.pdf" (Page no. 9 and 10) gives the methodology as under. Suppose you have a time series observations as say X =

Dear all, Is there a test in R for the randomness of a sequence of observations (e.g. to test the random number generator)? Specifically I am looking for autocorrelations which are not necessarily linear in nature, which the acf function does not seem to be flexible enough to detect as it tests for linear autocorrelation. Thanks in advance, Paul.

Hi, I am attempting to correct my models p-values due to temporal autocorrelations. It is not possible to model the correlation, so I have to make do with the p-value correction. I am feeling a bit thick here....I cannot get the autocorrelation values. What is the argument? My aim is to multiply the dependent variable autocorrelation with the independent variable autocorrelation and then

> Background: > OS: Linux Ubuntu Dapper > release: R 2.3.0 > editor: GNU Emacs 21.4.1 > front-end: ESS 5.2.3 > ----------------------------- > Colleagues > I have two spatial datasets (latitude, longitude, fish eggs) and (latitude, longitude, fish larvae) at the same 280 stations (i.e. 280 cases). I want to determine if the 2 datasets are spatially correlated. In other

Could anyone tell me whether there is an R function for removing autocorrelations from a series of observations before performing a linear or nonlinear regression analysis on them? Many thanks, Andrew Wilson

Hi, I have a binary (o/1 - coded) data set and want to test it's autocorrelation structure. Is that function implemented in R? Can I use the ACF - funtion with binary data? Thanks for your help, Daniel

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

I have got pairs of time series, where one usually is shorter (n typically about 5400) than the other (n typically about 52000). I would like to calculate the ccf for these series, but I haven't found a smart way to let the shorter "slide" along the longer one in steps. Manually splitting the longer series into shorter ones of the same length as the shorter is possible, but tedious.

Dear R community, I used a linear mixed model (named lm11) to model daily soil temperature depending upon vegetation cover and air temperature. I have almost 17,000 observations for six years. I can not account for autocorrelation in my model, since I receive the error message after applying the function: update(lm11, corr=corAR1()) Error: cannot allocate vector of size 220979 Kb Do

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 <-

Dear R community, I used a linear mixed model (named lm11) to model daily soil temperature depending upon vegetation cover and air temperature. I have almost 17,000 observations for six years. I can not account for autocorrelation in my model, since I receive the error message after applying the function: update(lm11, corr=corAR1()) Error: cannot allocate vector of size 220979 Kb Do

Hi everyone, I've got a question concerning the function acf(.) in R for calculating the autocorrelation in my data. I have a table with daily returns of several stocks over time and I would like to calculate the autocorrelation for all the series (not only for one time series). How can I do this? After that I want to apply an autoregressive model based on the estimated lag in the

Hello. I have some further problems with modelling an optimization problem in R: How can I model some optimization problem in R with a linear objective function with subject to some nonlinear constraints? I would like to use "optim" or "constrOptim", maybe with respect to methods like "Simulated Annealing" or "Sequential Quadric Programming" or something

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