similar to: acf()

Hi, Suppose I have a by b matrix which a and b are big numbers. How can I convert this matrix into a vector with length of a*b ? By column-wise or row-wise doesnt matter. My R version is 0.64.2. Kindest Regards, Peppy Adi-Purnomo ------- Stephanus (Peppy) Adi-Purnomo Energy Market Analyst - Energy Link Ltd - Dunedin email: s.adi.purnomo at energylink.co.nz http://www.energylink.co.nz

Hello, Is there any way we convert SAS dataset into R dataset? Kindest Regards, Peppy Adi-Purnomo ------ Peppy Adi-Purnomo Energy Market Analyst Energy Link Ltd Dunedin - New Zealand Ph.: +64 3 479 2475 Fax: +64 3 477 8424 Email: s.adi.purnomo at energylink.co.nz www.EnergyLink.co.nz -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, > Say we have a large-sized vector of observation A and a time vector with > the same size. The latter vector basically is the time flag when ith > obervation of A occurs, hence A[i] occurs at T[i]. > > I have no trouble with doing: T[A <= 10 | A >=20] for example > > Now, what I am confused: I tried to pull out when the largest value of A > with: > >

Dear Help desk, I have a model that I believe to be non linear. The relationship is something like: Response = Var1 + Var1^2 + Var1^3 Can I examine this relationship using glm or lm? However, glm or lm gives out pnly 1 coeficient for the model which is on Var1, unless I created some dummy var like (Var2 = Var1^2 and Var3 = Var1^3). Is there any other function handling this relationship?

Dear Help desk, I have a model that I believe to be non linear. The relationship is something like: Response = Var1 + Var1^2 + Var1^3 Can I examine this relationship using glm or lm? However, glm or lm gives out pnly 1 coeficient for the model which is on Var1, unless I created some dummy var like (Var2 = Var1^2 and Var3 = Var1^3). Is there any other function handling this relationship?

Hello, I need to use a procedure in R that similar to lme in Splus.. is this particular procedure has been implemented in the recent version? thanks. Regards, Peppy Adi-Purnomo Energy Market Analyst Energy Link Ltd Telp.: +64 3 479 2475 Fax. : +64 3 477 8463 www.energylink.co.nz -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

hello, I have a very old R version which calculates anova table for a stream of outcomes say: 1,5,4,3,2,4,5,2 which are grouped into 4 blocks, say A,B,C,D, of 2 say namely X,Y What is the command in R 0.64.2 to do this? My very old R command is anova.table but this command is unkown in R 0.64.2 Thanks, Peppy -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help

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 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

Well, I upgraded to a later release and have been having good luck (R vers. 0.65.0) I haven't upgraded my Red-Hat linux from 5.2 so I had to compile from source , ... so far so good. My problem is with the time series functions [library(ts)]. I'm trying to use the autocorrelation function-acf() and am having problems with it. mainly an error ERROR -Couldn't find function

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

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 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 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

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 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,

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,]

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]]