similar to: Stangle - dropping re-used code chunks

How exactly should I go about turning off annotation when running Stangle (Rtangle) with "R CMD Stangle myfile.Rnw"? Ideally I would like to be able to turn annotation off and on for individual code chunks, or maybe better, to annotate only named chunks. Are either of these things easily done? Is there some way to figure out this sort of thing without reading through the source code

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 Michael, see as an online resource: http://www.statsoft.com/textbook/sttimser.html or get hold on a time series analysis textbook, like one of the monographies written by Hamilton; Luetkepohl; Brockwell & Davis; Harvey or Box & Jenkins, to name but a few. In a nutshell, PACF 'eliminates' intermediate autocorrelations compared to ACF, e.g. an AR(1) process will ordinarily

hi, in 1.9.1, the return value from ARMAacf(pacf=TRUE) is not named by lags, contrary to ?ARMAacf. the simple fix is to move names(Acf) <- down after if(pacf), with an appropriate starting lag as pacf=TRUE appears to start at lag 1 (whereas pacf=FALSE starts at lag 0). for consistency, one could argue to append 1 for lag 0 for pacf=TRUE (or start pacf=F at lag 1). however, given the

Hi! I want to plot three graphs (residuals, ACF and PACF of a model). Ideally I would use a c(2,2) disposition where the residuals plot would start at position 1,1 and span to position 1,2. Then I would plot the ACF in position 2,1 and the PACF in position 2,2. Maybe is clearer like this: -------------------------- | | | residuals | |

I have data as below.Please let me know how the ACF and Pacf used to determine the order od arima model. Is there any rules need to be followed to determine order.Please advise > turkey.price.ts Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2001 1.58 1.75 1.63 1.45 1.56 2.07 1.81 1.74 1.54 1.45 0.57 1.15 2002 1.50 1.66 1.34 1.67 1.81 1.60 1.70 1.87 1.47 1.59 0.74 0.82

Hi helpers, can you help me in plotting acf and pacf functions in R. I am using the code acf(variable name) but it is not working. Expecting your reply. Thanks -- View this message in context: http://n4.nabble.com/ACF-and-PACF-tp1477149p1477149.html Sent from the R help mailing list archive at Nabble.com.

Hi, I tried to simulate an ARIMA model by using arima.sim(), say arima.sim(n=100,list(order=c(1,0,1),ar=0.6,ma=0.9,sd=1), but the acf and pacf of simulated data using acf() and pacf() are so much different from the theoritcal acf and pacf. For instance, in my case, ar=0.6 and ma=0.9, so the acf for all lags should be greater than 0 based on the theoritical calculation, but the acf of simulated

R: Is there an easy way to get the acf and pacf for an irregular times series? That is, the acf and pacf with lag lengths that are in units of time, not observation number. Thanks, Jason Higbee Research Associate Federal Reserve Bank of St. Louis The views expressed in this email are the author's and not necessarily those of the Federal Reserve Bank of St. Louis or the Federal Reserve

Dear list, according to the documentation of acf{ts} "the partial correlation coefficient is estimated by fitting autoregressive models of successively higher orders up to lag.max. " However, R seems to return the Yule-Walker estimates of the PACF by default. You can check this using c(1:10) as the series: the YW estimates are 0.7000000 and -0.1527035 for lags 1 and 2 . If the PACF

I have used acf() and pacf() in R to get the acf and pacf values at max/lag=20 but the output did not show the values associated with lag numbers. lag numbers is shown in decimals. -- Rashid Ameer View my recent publication at * http://www.emeraldinsight.com/fwd.htm?id=aob&ini=aob&doi=10.1108/17538391211282854 * Details for my works are available directly at

Thanks to Rolph Turner and Jason Turner ... I guess I was too excited about getting back on the list after an absense of several years ... I'll be a little more thoughtful about the problem before posting next time, and a little less trigger-happy with the "Send" e-mail button. Never-the-less, much appreciated. - Mohamed -----Original Message----- From: Rolf Turner

I downloaded and installed R-1.3.1 yesterday, and had to change line 2 of the "packages.html" file in $RHOME/doc/html/ The configuration steps were "the usual", tar zxvf ... cd ... ./configure make make check make install If it's relevant, Sys.info() sysname release "Linux" "2.2.14"

Hi all, I am modelling a time series with missing data. *Q1)* However, I am not sure if I should use the next *graphics* to understand my data: *a)* ACF & PACF (original series) *b)* ACF & PACF (residuals) * * *Q2)* I am using *tsdiag*, so I obtain a graphic with 3 plots: stand. residuals vs time; acf for residuals; Ljung-Box for residuals (it is wrong for residuals). I know that using

Sorry for the off-topic question, but I know there are some talented LaTeX users out there. Which bibliography style gives only the year in text citations (e.g "for further details, see Anderson (1992)" )? Thanks Jason -- Indigo Industrial Controls Ltd. http://www.indigoindustrial.co.nz 64-21-343-545 jasont at indigoindustrial.co.nz

Hi, I wanted to understand exactly how acf and pacf works, so I tried to calculate ac and pac manually. For ac, I used the standard acf formula: acf(k) = sum(X(t)-Xbar)(X(t-k)-Xbar))/sum(X(t)-Xbar)^2. But for pac, I could not figure out how to calculate it by hand. I understand that in both R and EVIEWS, it is done using the Durbin-Levinson algorithm by the computer. However, I don't

Hi, I noticed that whenever I ran acf or pacf, the plot generated by R always includes two horizontal blue doted lines. Furthermore, these two lines are not documented in the acf documentation. I don't know what they are for, but it seems that they are important. Could someone tell me what they are and how are they calculated? Thanks, -- Tom [[alternative HTML version deleted]]

[I originally emailed this to Friedrich Leisch but got no response and I just wanted to make sure it made it in before release.] While working with Stangle(), I noticed a problem when using 'split = TRUE'. Particularly, when there are two chunks where one chunk's name is a prefix of another chunk's name, then the two chunks will be written to a single file rather than two

Estimados amigos Estoy dibujando las funciones acf y pacf de una variable de una serie "zoo": > ls.str(pat="T0.5") T0.5 : 'zoo' series from 2017-11-08 23:00:00 to 2017-11-15 06:59:00   Data: num [1:9120, 1:3] 55 49.8 51 50.1 36.5 ...   Index:  POSIXct[1:9120], format: "2017-11-08 23:00:00" "2017-11-08 23:01:00" "2017-11-08

I sent this to the list last week, and haven't seen it pop up. Either I deleted it when it did appear, or possibly it was destroyed as spam...? If it did appear and I somehow missed it, appologies. In a nutshell, can the function FUN supplied to by() deduce what level of factor by() was on when FUN was called? I've been digging through the functions, and can't see where the