similar to: how do I do the autocovariance of a moving average?

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 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.

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), :

Hi! I just installed R on my computer, and I don't manage to run the function acf (autocovariance and autocorrelation function). It says me that this function is unknown. However, the standard functions like cos or read.table are OK. Can someone help me please? Thanks a lot. Naime -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

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 there, Just wondering why my post was rejected? cheersRachel Subject: repeating autocovariate functions From: r-help-owner@r-project.org To: moyble@hotmail.com Date: Sat, 2 Feb 2013 02:56:27 +0100 Message rejected by filter rule match --Forwarded Message Attachment-- Date: Fri, 1 Feb 2013 17:56:14 -0800 From: moyble@hotmail.com To: r-help@r-project.org Subject: repeating autocovariate

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

Please pardon me if you see this message twice. The mail server has a bit problem. ***************************************************** 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

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

I got the code for how to do regression without an intercept out of the back of my book and the next part of the homework asks me to do it with an intercept. The problem is, Q1 disappears whenever I try. Here is my code: Without the intercept: load("tsa3.rda") > > Q=factor(rep(1:4,21)) > reg=lm(log(jj)~0+trend+Q,na.action=NULL) > model.matrix(reg) trend Q1 Q2 Q3 Q4 1

hello I have covariance stacionary proces, and i want to estimate some parameter of this proces via gmm. My problem is with write "g" -function. 0 order autocovariance is not problem 1 and higher order autocavariance are problem, because add order from 0 mean that I "loose" one "observacion" if I have 100 observation and i am going to use mean, variance and first

I am working with amino acid sequences changing each letter to numbers.I have a data from acf transformation called Zm as shown below. I would like to get Indices D1 to D10 and then create F1 to F10 as indicated below. Is there anyway I can do that in R without typing each of them one by one. For example to get D1 to D3, I have to type D1<-c(Zm[[1]])[1][[1]], D2<-c(Zm[[2]])[1][[1]], and

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so

Hi R users This looks a simple question Is there any difference between between rnorm(1000,0,1) and running rnorm(500,0,1) twice in terms of outcome ? TM

Guys: 1).When I using the 'quantile' function, I get really confused. Here is what I met: > x<-zoo(rnorm(500,0,1)) > quantile(x,0.8) 400 1.060258 > c=rnorm(500,0,1) > quantile(c,0.8) 80% 0.9986075 why do the results display different? Is that because of the different type of the class? 2).And I want to use the 'rollapply' function to compute a

Hi R experts, I am trying to remove autocorrelation from Simple Moving Average time series. I know that this can be done by using seasonal ARIMA like, library(TTR) data <- rnorm(252) n=21 sma_data=SMA(data,n) sma_data=sma_data[-1:-n] acf(sma_data,length(sma_data))

I need to compute the Fourier transformation of the autocovariance function of a panel composed by 196 time series. With autocovarance function i mean a matrix Gamma(u) which contains the covariance of all the variables at time (t) with all the variables evaluated at time (t-u). It can be represented by a tri-dimensional matrix whose dimensions are (196,196,K), where K is the maximum lag