similar to: Help with acf

Full_Name: Ian McLeod Version: 2.3.1 OS: Windows Submission from: (NULL) (129.100.76.136) > There is a simple bug in acf as shown below: > > z <- 1 > acf(z,lag.max=1,plot=FALSE) > Error in acf(z, lag.max = 1, plot = FALSE) : > 'lag.max' must be at least 1 > This is certainly a bug. There are two problems: (i) the error message is wrong since lag.max is

I started to check what I thought I knew with autocovariance and it doesn’t jive with the the calculations given by ‘R’. I was wondering if there is some scaling or something that I am not aware of. Take the example Ø d <- 1:10 Ø (a <- acf(d, type="covariance", demean=FALSE, plot=FALSE)) Autocovariances of series ‘d’, by lag 0 1 2 3 4 5 6

Dear all, Does this make any sense: var() = cov() != acf(lag.max=0, type="covariance")? I have daily data of IBM for May 2005, and I'm using the logarithmic return: > ibm200505$LRAdj.Close [1] NA 0.0203152 0.0005508 -0.0148397 -0.0025182 0.0092025 -0.0013889 [8] 0.0098196 -0.0103757 -0.0274917 0.0005716 -0.0159842 -0.0074306 0.0091710 [15] 0.0002898 0.0226306

In the R Document, the usage of the acf() is as follow: acf(x, lag.max = NULL, type = c("correlation", "covariance", "partial"), plot = TRUE, na.action = na.fail, demean = TRUE, ...) But now I want to get the result picture like: plot(x,type="l") or plot(x,type="p") How can I do this with acf function? 仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭佒伮 伬侎仯仭

I am trying to understand the function acf stats:::acf shows me the function I am having trouble understanding the usage "$acf" in the following acf <- array(.C(R_acf, as.double(x), as.integer(sampleT), as.integer(nser), as.integer(lag.max), as.integer(type == "correlation"), acf = double((lag.max + 1L) * nser * nser), NAOK =

Hi I'm getting the following error that do not make sense to me, what am Idoing wrong ? > acf(Recsim[1,], lag.max=1) Error in acf(Recsim[1, ], lag.max = 1) : 'lag.max' must be at least 1 Regards EJ

Dear ladies and gentlemen, I would like to calculate autocovarinace and cross-covariance scores 1, 2 and 3 of four classes A, B, C and D. I am using acf and ccf from time sires library. My problem is that I can not separate my data among the classes A, B, C and D. When I calculated acf for Score 1, I got a wrong result. The reason being that instead of using ony 60, 40 and 20, the program

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

Hi R-sters, Just wondering what I might be doing wrong. I'm trying to fit a multiple linear regression model, and being ever mindful about the possibilities of autocorrelation in the errors (it's a time series), the errors appear to follow an AR1 process (ar(ts(glsfit$residuals)) selected order 1). So, when I go back and try to do the simultaneous regression and error fit with gls,

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.

I have used acf() and pacf() in R to get the acf and pacf values at max/lag=20 but the output did not show the values associated with lag numbers. lag numbers is shown in decimals. -- Rashid Ameer View my recent publication at * http://www.emeraldinsight.com/fwd.htm?id=aob&ini=aob&doi=10.1108/17538391211282854 * Details for my works are available directly at

Hi R users, where can I find the equations used by acf function to calculate autocorrelation? I think I misunderstand acf. Doesn't acf use following equation to calculate autocorrelation? [image: R(\tau) = \frac{\operatorname{E}[(X_t - \mu)(X_{t+\tau} - \mu)]}{\sigma^2}\, ,] If it does, then the autocorrelation of a sine function should give a cosine; however, the following code gives a

Full_Name: Hiroyuki Kawakatsu Version: 1.3.1 OS: win2k Submission from: (NULL) (130.158.140.105) the lag labels in the acf plot is screwed when the ts frequency>1. try, for example, #acf mis-feature x <- ts(rnorm(200), frequency=4); acf(x,lag.max=24); h. > version _ platform i386-pc-mingw32 arch x86 os Win32 system x86, Win32

Hi, The default value for lag.max in ACF implementation is 10*log10(N) There several publications recommending setting lag.max to: - N/4 (Box and Jenkins, 1970; Chatfield, 1975; Anderson, 1976; Pankratz, 1983; Davis, 1986; etc.) - sqrt(N)+10 (Cryer, 1986) - 20<=N<=40 (Brockwell and Davis) Why R uses 10*log10(N) as a default? Please, give me a reference to a book or article where the

Hi R, I have doubt. >x= c(4,5,6,3,2,4,5) >acf(x,plot=F,lag.max=1) Autocorrelations of series 'x', by lag 0 1 1.000 0.182 But if I actually calculate the autocorrelation at lag1 I get, >cor(x[-1],x[-length(x)]) [1] 0.1921538 Even in excel I get 0.1921538 value. So, I want to know what the 'acf' function is calculating here....

this one is not a false alarm like my previous message. i have cut and paste the code below so if anyone could run it would be appreciated. basically, my question is why the horizontal axis of the acf plot is labelled with such huge numbers when the labels should be 1 through 10 since may lag.max = 10 ? i looked at the cdoe of acf but it was pretty much beyond me. i think it has something to

Hello, I am attending a course in Computational Statistics at ETH and in one of the assignments I am asked to prove that a time series is not autocorrelated using the R function "acf". I tried out the acf function with the given data, according to what I found here: http://landshape.org/enm/options-for-acf-in-r/ this test data does not look IID but rather shows some trends so how can I

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

I need to modify the graph of the autocorrelation. I tried to do it through plot.acf but with no success. 1. I would like to get rid of the lag zero 2. I would like to have numbers on the x-axis only at lags 12, 24, 36, 48, 60, ... Could anybody help me in this? Any help will be appreciated Thank you for your attention Stefano [[alternative HTML version deleted]]

Dear all, I have one Model (M3) fitted using the lme package, and I have checked the correlation structure of within-group errors using plot(ACF (M3,maxLag=10),alpha=0.05) But now I am not sure how to interpret this plot for the empirical autocorrelation function. The problem is that I am used to see/interpret diagrams in which all the autocorrelation Lags, except lag-1, are inside the