similar to: a question on autocorrelation acf

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 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 all, I'm new with R (and S), and relatively new to statistics (I'm a computer scientist), so I ask sorry in advance if my question is silly. My problem is this: I have a (sample of a) discrete time stochastic process {X_t} and I want to estimate Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} } where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for me to compute

This has ABSOLUTELY nothing to do with R but I was hoping that someone might know because there are obviously a lot of very bright people on this list. Suppose I had a time series of data and at each point in time t, I was calculating x bar + plus minus sigma where x bar was based on a moving window of size n and so was sigma. So, if I was at time t , then x bar t plus minus sigma_t would be

Dear users, I have two continuous variables which are two different measures taken each year from 1975 to 2005. I want to see if the two variables are correlated but need to take into account the fact that they are a time series. I have been following an example from 'The R Book' where you plot the ACF: par(mfrow=c(1,1) acf(cbind(x,y)) and this appeared to work fine, producing four

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

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'

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

Hi everyone, I'm trying to use the acf() function to calculate the autocorrelation of each column in a matrix. The trouble is that I can only seem to get the function to work if I extract the data in the column into a separate matrix and then apply the acf() function to this column. I have something like this: acf(mat,lag.max=10,na.action=na.pass) ...but I would really like to apply the

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, 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

When I run the "acf()" function using the "acf(ts.union(mdeaths, fdeaths))" example, the "acf()" function calls the "acf.plot()" function to generate this plot... http://members.cox.net/ddebarr/images/acf_example.png The plot in the lower right-hand corner is labeled "fdeaths & mdeaths", but the negative lags appear to belong to "mdeaths

Hello, I would like, if it is possible, to compare the effect of a variable across regression models. I have looked around but I haven't found anything. Maybe someone could help? Here is the problem: I am studying the effect of a variable (age) on an outcome (local recurrence: lr). I have built 3 models: - model 1: lr ~ age y = \beta_(a1).age - model 2: lr ~ age + presentation

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

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 there, I have R 0.64.2. I wonder if this R has acf function to calculate autocorrelation? since the help(acf) doesnt seem to be able to help me. thanks, Peppy -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

Hi, I'm plotting 5 autocorrelation plots on one page. Using par(mfrow=c(3,2)) everything comes out fine. However, for each plot, it prints a title on top of each plot that says Series followed by the variable name used in the plot. I want to suppress those titles, but I also want a general figure title on the bottom of the page. I've looked at the Murrell book as well as the acf

Hello r-help, I have a couple of time-series of different length and I would like to produce a simple overview plot showing the autocorrelation functions of the series. The time-series are stored in a dataframe like this: > test.data item year value 1 xxx 1961 -1.09 2 xxx 1962 0.21 3 xxx 1963 -0.81 [trimmed] 8

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....

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