similar to: writing autocorrelation and partial auto correlation functions to a file

Hello list, Is there any way to bandpass filter in R thanks nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

Dear list, I searched the libraries but could not find means to compute the svd of a coupled field. Is it possible in R Thanks nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

Dear useRs, I am trying to construct a time series using as.ts function, surprisingly when I plot the data the x axis do not show the time in years, however if I use ts(data), time in years are shown in the x axis. Why such difference in the results of both the commands Thanks nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma

Hi list, I am trying to fit arima model for a grid of 360x161x338 points, where 360x161 is the spatial dimension and 338 is the number of time steps I have, which is seasonal. For this purpose I used the auto.arima function in forecast package. After fitting residuals at each grid in space, the auto correlations are still significant ( but < 0.2). This make me think that the data

Hi all, I am trying to find the autocorrelation of some time series. I have say 100 files, some files have only missing values(-99.99, say). I dont want to exclude these files as they represent some points in a grid. But when the acf command is issued i get an error. Error in plot.window(...) : need finite 'ylim' values In addition: Warning messages: 1: In min(x) : no

HI all., I have some univariate time series that need to be prewhitened. HOw this can be performed in R. I am thinking of to fit an ARIMA model and substract this from the original series. Is this the correct way THanks in advance nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

HI all, I am trying to compute the EOF of a matrix using prcomp but unable to get the expansion co-efficients. is it possible using prcomp or are there any other methods thanks nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

HI list, I want to know whether tsdiag uses k-(p+q) as the lag in ljung box test. How is it possible to save those values nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

Hi list, I have the following code to compute the acf of a time series acfresid <- acf(residfit), where residfit is the series when I type acfresid at the prompt the follwoing is displayed Autocorrelations of series ?residfit?, by lag 0.0000 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333 1.000 -0.015 0.010 0.099 0.048 -0.014 -0.039 -0.019 0.040 0.018

HI list, I am using auto.arima from forecast package, I wonder whether its possible to save model orders to a seperate file Thanks nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

Hi list, I am not a frequent user of R. Recently I used R in principal component analysis and got the result as a class, which has information like standard deviation and principal components from 1 to 10. How is it possible to extract the column corresponding to first principal component and write it to a file the out from prcomp command is something like this Standard deviations:

Hi, I've a question about a FQDN VS Shorewall configuration: I'd like to install a firewall that should forward the web requests to different web servers (iis, apache, and so on...); all web sites are registered with the same ip to internic (eg. www.example1.com-->151.99.234.3-->apache-server, www.example2.com-- >151.99.234.3-->iis-server, etc...) so I'm wondering if I

I have the following code to write the output from auto.arima function. The issue is not in finding the model but to divert its out put fit to a file order_fit.txt. code runs but nothing is written to order_fit.txt where am I going wrong library(forecast) for (i in 1:2) { filen = paste("file",i,".txt",sep="") data <- read.table(filen) dat1 <- data[,1] xt <-

Hi All, I am trying to reproduce this using R instead. [image: Full-size image (38 K)] I tried using the following code *SChla <- read.csv("SM_Chla_data.csv")* *Atlantis <- SChla[16:66,]* *head(Atlantis)* * * Seamount Station Depth Pico Nano Micro Total_Ch dbar Latitude Longitud 16 Atlantis 1217 Surface 0.0639 0.1560 0.0398 0.2597 2.082 -32.71450 57.29733

Hi, I have two global raster maps, each of the same variable but from different sources. The values range from 0 to 5 in whole numbers. Is there a statistical test in R that can quantify the similarity of the spatial patterns (i.e., highs and lows)? Thanks, -- View this message in context: http://r.789695.n4.nabble.com/map-similarity-spatial-autocorrelation-in-R-tp4647877.html Sent from the

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

Hi all, I am analyzing data on habitat utilization of seals in the Southern Ocean. My data show spatial autocorrelation, which I'm interested in incorporating into my model. I am trying to model the presence of dives (versus simulated pseudo-absences) using a binomial generalized binomial model (glmmPQL), since I can incorporate the autocorrelation structure to the model using that package.

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

Hi, I am trying to calculate the autocorrelation matrix for an input matrix with the size n*m where n=7 (the dimensionality of my input feature vectors) and m being the time. Thus one could think of the input data as a 7-dimensional time-series. Does anyone know of any way to calculate the autocorrelation matrix for such an input? I tried various functions, but none give me a matrix that returns

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