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Hi guys, I'm trying to do the autocovariance of a moving average but it's giving me errors. Here is my code: > w=rnorm(500,0,1) > v=filter(w, sides=2, rep(1/3,3)) > acf(w, lag.max=20) <=that printed out a nice graph. > acf(v, lag.max=20) Error in na.fail.default(as.ts(x)) : missing values in object thanks a lot. -- View this message in context:

If first fit my data column V1 to column V2 using normal "lm" fitting, call it "fit1", then I used "acf(fit1$residuals, type='cov', 40) " function to obtain the autocovariance of the residuals, and then constructed a autocovariance matrix, I chose it to be 40x40. Call this autocovariance matrix B, I then use the following "lm.gls" function to

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

I'm a PhD student and I'm working with covariance function. I'm interested to know if exist some packages in R to calculate and plot the bidimensional Autocovariance Function. the input matrix is a matrix that describe a spatial location over a 2-D space and I want to use it in the same way I can use a time serie in the 1-D acf. Thanks, Nicola.

I'm a PhD student and I'm working with covariance function. I'm interested to know if exist some packages in R to calculate and plot the bidimensional Autocovariance Function. the input matrix is a matrix that describe a spatial location over a 2-D space and I want to use it in the same way I can use a time serie in the 1-D acf. Thanks, Nicola.

Hello! I have two questions. First of all, I have a problem dealing with acf (Autocovariance function) and need help. First I defined a time series, x, which is a vector created by x <- ts(rnorm(200)). So I plugged the series directly into the acf function, acf(x) and an error message popped up as: Error in .C("acf", as.double(x), as.integer(sampleT), as.integer(nser), :

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

Dear R-project users I would like to calculate a linear trend versus time taking into account a first order autoregressive process of a single time series (e.g. data$S80 in the following example) using th gls function. gls(S80 ~ tt,data=data,corAR1(value, form, fixed)) My question is what number to set in the position of value within corAR1? Should it be the acf at lag 1? I look forward for

Hello, I am trying to calculate the autocovariance matrix for any general farima(p,d,q) with p,q > 1. Could anyone give an idea how to implement in R or if there is any package for this? thank you beforehand. Jose.

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

Hello everybody, please excuse my bad English. I am Alfredo Naime and I'm from to Venezuela. I want to make a lib with tools for simulation (queues, inventories, factory, etc.) using object-oriented programming (OOP). You have any manuals on the handling of data types, classes, inheritance, etc. in R with examples and how to make a R lib. Thank you, very much. Alfredo

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]]

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

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

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

Dear All, I am very new to T. I need to fit a ARIMA model to my time series. So I found the auto correlation functions and partial auto correlation function in R. Now I want to save these valuse along with the significance levels to a file. How to do that?. I tried some function in R like write.table but returns an error "cannot coerce class "acf" into a

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.

Hi List, I'm trying to calculate the autocorrelation coefficients for a time series using acf at various lags. This is working well, and I can get the coefficients without any trouble. However, I don't seem to be able to obtain the significance of these coefficients from the returned acf object, largely because I don't know where I might find them. It's clear that the acf

I am guessing this is a very simple question, but this is only my second day with R so it is all still a bit imposing. I am trying to run an autocorrelation. I imported a CSV file, which has one column labeled "logistic". I ran the command: ACF(data$logistic,maxLag=10) However, I received the error: Error in UseMethod("ACF") : no applicable method for 'ACF'

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 =