similar to: alternative generator for normal distributed variables

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 R users, I'm here trying to understand correlated residuals in nonlinear estimation. I'm reading/studying the book Bates, D. M. and D. G. Watts, (1988), /Nonlinear regression analysis and its applications/, Wiley, NY. pages 92-94, trying to reproduce the figures and to find out the code in R to perform the necessary calculations. I also consulted Pinheiro and Bates, but without

Dear R users, someone knows how to remove auto-correlation from a frequencies time series? I've tried by differencing (lag 1) the cumulative series (in order to have only positive numbers) , but I can't remove all auto-correlation. If it's useful I can send my db. x <- # autocorrelated series new1<-cumsum(x) new2<-diff(new1,lag=1,differences = 1) acf(new2) #

Hi there, I am using gls from the nlme library to fit an AR(1) regression model. I am wondering if (and how) I can separate the auto-correlated and random components of the residuals? Id like to be able to plot the fitted values + the autocorrelated error (i.e. phi * resid(t-1)), to compare with the observed values. I am also wondering how I might go about calculating confidence (or

Hello, I'm a newby in R. I have created a data.frame holding panel data, with the following columns: "id","time","y", say: periods = 100 numcases = 100 df = data.frame( id = rep(1:numcases,periods), time = rep(1:periods, each = numcases) ) df = transform(df,y=c(rnorm(numcases*periods)+id) I want to check whether "y" is autocorrelated. I came across

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

I am trying to calibrate a non linear mixed model with a AR(1) autocorrelation structure. This structure is indicated by the ACF plot. When calibrating the model without the AR(1), everything comes out nicely. However, when the AR(1) structure is included in the model, the Phi parameter estimate comes out to be 1. This result occurs with various starting values in the correlation statement. Am I

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 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 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

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 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, Im new to R so this question is quite fundamental. Im trying to compare some autocorrelations generated by the acf function to some theoretical correlations. How can I have acces to just the autocorrelations, for computation? This is some of my code: > acf.data<-c(acf(x)) > acf.data This is the R output: $acf , , 1 [,1] [1,] 1.000000000 [2,]

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 =

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

How can I make sure the residual signal, after subtracting the trend extracted through some technique, is actually trend-free ? I would greatly appreciate any suggestion about some Stationarity tests. I'd like to make sure I have got the difference between ACF and PACF right. In the following I am citing some definitions. I would appreciate your thoughts. ACF(k) estimates the correlation

Hello all, I am trying to use lme to examine how a response variable (Chla) changes over time in different treatments (2 Temp & 2 Light levels). Within each treatment combination, there are two replicate tanks (each with unique TankID) with coral fragments in them. All tanks are subject to the same environment until Time=0, when treatments are imposed, and Chla is measured for each

Hi everyone, I've got the following problem: I've got a matrix [1000,2] and two vectors. In very matrix row there is two coefficients b0 and b1. The vectors are two variables x and y. I want to do a loop to take b0 and b1 and with x and y calculate the residual of a linear model and calculate the second order coefficient of autocorrelation. What I did is : rho<-function(mat, x,y){

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

Hi list, I have the following code to compute the acf of a time series acfresid <- acf(residfit), where residfit is the series when I type acfresid at the prompt the follwoing is displayed Autocorrelations of series ?residfit?, by lag 0.0000 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333 1.000 -0.015 0.010 0.099 0.048 -0.014 -0.039 -0.019 0.040 0.018