similar to: partial autocorrelation for Rt vs. Nt-1, ......., Nt-h

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

Hi, I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv

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 all, I'll preface this with saying I've gone through the archives, and am still in need of some help. I've been using this likelihood model with mean = 0 and s.d. = sqrt( (c + ( 1 / N1 ) + ( 1 / N2 ) ) * x * ( 1 - x )), where c is a genetic drift parameter (usually very small, like between .005 - .001), N1 and N2 are my population sizes (~200), and x is a value between 0 and 1.

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

I am getting the following: *** Weave Errors *** Error in driver$runcode(drobj, chunk, chunkopts) : Error in eval(expr, envir, enclos) : couldn't find function "pacf.mts" *** Source Errors *** Error in eval(expr, envir, enclos) : couldn't find function "pacf.mts" make[1]: *** [checkVignettes] Error 1 I don't really understand the new namespace mechanism,

pacf in devel seems by default to return a different number of lags than 1.7.1 for $pacf. I don't see any mention of this in the NEWS file, or any change in the documentation, so I suspect it is and error, though it may be an undocumented improvement. (Newbie question: How is the simplest way to display a function like pacf.default that is not exported from a namespace?) Paul

Dear R community, I am using R version 2.4.1 GUI 1.18 on an iBook G4 with OS 10.4.10. When I try to load package 'gplots' I get the following message: > library(gplots) Loading required package: gdata Attaching package: 'gdata' The following object(s) are masked _by_ .GlobalEnv : reorder.factor Loading required package: gtools Attaching package: 'gtools'

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

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 | |

Dear all, following the thread http://tolstoy.newcastle.edu.au/R/e2/devel/07/09/4338.html regarding the bug in the partial autocorrelation function for multivariate time series. I have prepared a web page with patches and relevant information. http://www2.stat.unibo.it/giannerini/R/pacf.htm Please do not hesitate to contact me for further clarifications regards Simone --

Dear R community, I am having trouble with a particular plot that I am trying to produce using Hmisc's xYplot function. I've been using primarily lattice and Hmisc packages for my plotting needs for the past few years, with great success. However, what I want to do now with xYplot is plot more than one data trend in the same panel, much as I would use xyplot from package lattice in

Dear all, I found the following behaviour with pacf() in the multivariate case, set.seed(10) x <- rnorm(1000,sd=10000) y <- rnorm(1000,sd=1) pacf(ts(cbind(x,y)),plot=FALSE,lag.max=10) Partial autocorrelations of series 'cbind(x, y)', by lag , , x x y 0.047 ( 1) 0.000 ( -1) 0.011 ( 2) 0.000 ( -2) 0.005 ( 3) 0.000 ( -3) 0.013 ( 4)

When I use the command for PACF, lags 5, 10, 15, and 20 are labeled. I would like to label lag 1. I would greatly appreciate if someone could tell me how to do this. Below is the command that I am using: pacf(data$R1,main="Series R1 Residuals") [[alternative HTML version deleted]]

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

Hi, I'm slowly working through Tsay's "Analysis of Financial Time Series" 3rd ed. ?I'm trying to replicate Table 2.1 on p.47, which gives PACF, AIC, and BIC for the monthly simple returns of the CRSP value-weighted index. The data: http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3/m-ibm3dx2608.txt > da <-

Dear users I am fitting a Generalized Additive Mixed Models (gamm) model to establish possible relationship between explanatory variables (water temperature, dissolved oxygen and chlorophyll) and zooplankton data collected in the inner and outer estuarine waters. I am using monthly time-series which are auto-correlated. In the case of the inner waters, I have applied satisfactoryly (by

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 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

Dear R community, Since upgrading to R v.2.6.1 and re-installing package Hmisc (binary for Mac OS X v.3.4-3), I have been getting a error when trying to make xYplots: >plotcv<-c(34.88, 41.51, 45.81, 51.05, 51.66) >plotcv.se<-c(2.406551, 3.071291, 4.331407, 3.213873, 4.838150) >month<-c(6, 7, 8, 9, 10) >library(Hmisc) >xYplot(Cbind(plotcv, plotcv + plotcv.se,