similar to: Autocorrelation in non-linear regression model

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so

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

Hi, I have collected data on trees from 5 forest plots located within the same landscape. Data within the plots are spatially autocorrelated (calculated using Moran's I). I would like to do a ANCOVA type of analysis combining these five plots, but the assumption that there is no autocorrelation in the residuals is obviously violated. Does anyone have any ideas how to incorporate these spatial

Dear All, Would like to ask the inconsistency in the autocorrelation from R with SPSS/Minitab. I have tried a dataset x with 20 data (1-20) and ask R to give the autocorrelation of different lags using the command < acf(x, lag.max=100, type = "correlation"), However while SPSS and Minitab give the same answers (0.85 for lag1), R gives 0.3688 which is much smaller. Obviously, the

Dear List, I am using the output of a ML estimation on a random effects model with first-order autocorrelation to make a further conditional test. My model is much like this (which reproduces the method on the famous Grunfeld data, for the econometricians out there it is Table 5.2 in Baltagi): library(Ecdat) library(nlme) data(Grunfeld)

Dear users, I have two continuous variables which are two different measures taken each year from 1975 to 2005. I want to see if the two variables are correlated but need to take into account the fact that they are a time series. I have been following an example from 'The R Book' where you plot the ACF: par(mfrow=c(1,1) acf(cbind(x,y)) and this appeared to work fine, producing four

Hello everyone, I'm working on a current linear model Y = a0 + a1* X1 + ... + a7*X7 + residuals. And I know that this model presents both heteroskedasticity (tried Breusch-Pagan test and White test) and residuals autocorrelation (using Durbin Watson test). Ultimately, this model being meant to be used for predictions, I would like to be able to remove this heteroskedasticity and residuals

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

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

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,

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

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,   I'm actually I’m performing a TSLS linear multiple regression on annually data which go from 1971 to 1997. After performing the TSLS regression, I tried to extract the R squared value using “output$r.squared” function and to perform autocorrelation (Durbin Watson and Breush Godfrey) and heterokedasticity tests (Breush-pagan and Goldfeld Quandt)  but I have errors messages. More

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

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

Dear R-users, I hope you will have time to read me and I will try to be brief. I am also sorry for my poor english. I used gls function from the package nlme to correct two types of bias in my database. At first, because my replicates are spatially aggregated, I would like to fit a corStruct function like corLin, corSpher, corRatio, corExp or corGaus in my gls model, and simultaneously,

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

Dear all: I would like to create a landscape of environmental values that follow a uniform frequency distribution and also have spatial autocorrelation in the landscape. I was wondering if there is an algorithm and/or package out there that creates autocorrelation of values that are distributed according to a non-normal frequency distribution. Any suggestions are greatly appreciated. Thank you,

Hi, I am trying out a generalized least squares method of forecasting that corrects for autocorrelation. I downloaded daily stock data from Yahoo Finance, and am trying to predict Close (n=7903). I have learned to use date functions to extract indicator variables for Monday - Friday (and Friday is missing in the model to prevent it from becoming full rank). When I run the following code...

hi, am trying to understand speex's algo. have a few questions. 1) autocorrelation: in the function, _spx_autocorr (for floating point version), there is a line ac[0] += 10; correct me if i am wrong, i suppose the addition of 10 is used to condition the autocorrelation matrix. wonder how the value of 10 is arrived at? 2) levinson durbin (LD) algo in the function _spx_lpc, i referred