similar to: Different Autocorrelation using R and other softwares

Hi, how to estimate a the following model in R: y(t)=beta0+beta1*x1(t)+beta2*x2(t)+...+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3) 1) using "lm" : dates <- as.Date(data.df[,1]) selection<-which(dates>=as.Date("1986-1-1") & dates<=as.Date("2007-12-31")) dep <- ts(data.df[selection,c("dep")]) indep.ret1

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....

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 guys .. I would like to have some help about as.table . I made a table with the autocorrelations of the returns whit 10 lags and i get this : autocorrelazione2 <- as.table(c((cor(r2[-1151,],lag(r2))),(cor(r2[- c(1151,1150),],lag(r2, k=2))),(cor(r2[- c(1151,1150,1149),],lag(r2, k=3))),(cor(r2[- c(1151,1150,1149,1148),],lag(r2, k=4))),(cor(r2[- c(1151,1150,1149,1148,1147),],lag(r2,

I have been reading about autocorrelation in linear models over the last couple of days, and I have to say the more I read, the more confused I get. Beyond confusion lies enlightenment, so I'm tempted to ask R-Help for guidance. Most authors are mainly worried about autocorrelation in the residuals, but some authors are also worried about autocorrelation within Y and within X vectors

Hello folks, I have some data where spatial autocorrelation seems to be a serious problem, and I'm unclear on how to deal with it in R. I've tried to do my homework - read through 'The R Book,' use the online help in R, search the internet, etc. - and I still have some unanswered questions. I'd greatly appreciate any help you could offer. The super-super short explanation is

Hello, I can`t figure out how can increase the velocity of the fitting data by nls. I have a long data .csv I want to read evry time the first colunm to the other colunm and analisy with thata tools setwd("C:/dati") a<-read.table("Normalizzazione.csv", sep=",", dec=".", header=F) for (i in 1:dim(a[[2]]]) { #preparazione dati da analizzare

Hello, I have a matrix with the distances among sites. And I have another matrix with the presence and absence of each species in each site. I would like to test the spatial autocorrelation among sites. I have tried to use the function gearymoran of the ade4 package, but error messages keep popping up. Do you know any function for me to test the spatial autocorrelation of my data? Thanks,

Howdy All, I am looking for some good tutorials (books, websites, whatever) for calculating/testing for Spatial Autocorrelation using R. Specifically, I am wanting to test for autocorrelation of a number of variables measured at a set of discrete locations. Up to this point I have been exploring the "spdep" package and I can get "moran.test" to work, but I am concerned that

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

Dear all, I am currently facing a problem related to the spatial autocorrelation of a sample of stations; these stations supply weekly data for a fixed time-window during the year (namely, 4-6 months per year). For this reason I'm trying to use the R package 'spdep' (specifically Moran's I) in order to get rid of it. Does anyone know how is it possible (if it is...) to

I'm currently working with some time series data with the xts package, and would like to generate a forecast 12 periods into the future. There are limited observations, so I am unable to use an ARIMA model for the forecast. Here's the regression setup, after converting everything from zoo objects to vectors. hire.total.lag1 <- lag(hire.total, lag=-1, na.pad=TRUE) lm.model <-

Hi, I am attempting to correct my models p-values due to temporal autocorrelations. It is not possible to model the correlation, so I have to make do with the p-value correction. I am feeling a bit thick here....I cannot get the autocorrelation values. What is the argument? My aim is to multiply the dependent variable autocorrelation with the independent variable autocorrelation and then

Hi R helpers, Is there a function (package?) in R available which tests "spatial autocorrelation" between points (e.g. vector layer of weather stations)? (e.g. Moran's I...) Via the archives we found out that there is a package 'spdep' which uses grid data for testing spatial autocorrelation. Thanks a lot, Jan

Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on

Hi all, I have run a ridge regression as follows: reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+final$g+final$u, lambda=seq(0,10,0.01)) Then I enter : select(reg) and it returns: modified HKB estimator is 19.3409 modified L-W estimator is 36.18617 smallest value of GCV at 10 I think it means that it is

Dear all, I am experiencing problems using the glmmPQL function in the MASS package (Venables & Ripley 2002) to model binomial data with spatial autocorrelation. My question - is the presence of birds affected by various hydrological parameters? Presence/absence data were collected from 83 sites and coupled against hydrological data from the same site. The bird survey sampling effort

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

Hi, I am looking at the effects of two explanatory variables on chlorophyll. The data are an annual time-series (so are autocorrelated) and the relationships are non-linear. I want to account for autocorrelation in my model. The model I am trying to use is this: Library(mgcv) gam1 <-gam(Chl~s(wintersecchi)+s(SST),family=gaussian, na.action=na.omit, correlation=corAR1(form =~

Dear all: Does anyone have any suggestions on how to make a spatially explicit landscape with spatial autocorrelation in R? In other words, a landscape where all cells have a spatial reference, and the environment values that are closer in space are more similar (positive spatial autocorrelation). Thank you, Laura