similar to: Cochrane-Orcutt

Hello, Is there a package that implements the Cochrane-Orcutt itterative procedure for dealing with autocorrelation in a regression model? Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia University, Wolfville, NS, B0P 1X0 (902)585-1461 All opinions stated are personal, unless otherwise indicated.

Hi John, Hi Folks/Prof. Fox, I found some code John Fox had written sometime back on the Cochrane-Orcutt and Prais procedures here: https://stat.ethz.ch/pipermail/r-help/2002-January/017774.html I thought I would try it out and get the following errors below. Was wondering if anyone had any immediate opinions why this might be ? The linear model is the object regrCMSlm . Thanks, Tolga

Hello~   Did any one have used the package 'orcutt' ?   I find that it can not work smoothly in a single variable regression. I use the example following, it function very well.   But when I regress "cons" on "price" (use the "reg1<-lm(cons~price+income+temp)") , then  use "reg11<-cochrane.orcutt(reg1) ". There is an error message “Error in

Hi there fellow R-users, Can anyone tell me if there exits an R package that deals with serial correlation in the residuals of an lm model. Perhaps, using the Cochrane Orcutt or Praise Wilson methods? Thanks, Wayne Dr Wayne R. Jones Senior Statistician / Research Analyst KSS Limited St James's Buildings 79 Oxford Street Manchester M1 6SS Tel: +44(0)161 609 4084 Mob: +44(0)7810 523 713

i need to know how to estimate a linear regression whose coefficients sum to zero

Could anyone tell me whether there is an R function for removing autocorrelations from a series of observations before performing a linear or nonlinear regression analysis on them? Many thanks, Andrew Wilson

Dear R users: In the example of meta-analysis (cochrane, package rmeta), I can not found the p-value of Test for overall effect, and some other indices (Z, I, weight and et al). How can I get the these indices listed? > library(rmeta) > data(cochrane) > cochrane name ev.trt n.trt ev.ctrl n.ctrl 1 Auckland 36 532 60 538 2 Block 1 69 5

Hi, I need to correct for ar(1) behavior of my residuals of my model. I noticed that there are multiple gls models in R. I am wondering if anyone has experience in choosing between gls models. For example, how should one decide whether to use lm.gls in MASS, or gls in nlme for correcting ar(1)? Does anyone have a preference? Any advice is appreciated! Thanks, -- Tom [[alternative HTML

Dear R-help, I don't understand why Gls gives me an error when trying to fit a model with AR(2) errors, while gls (from nlme) does not. For example: library(nlme) library(rms) set.seed(1) d <- data.frame(x = rnorm(50), y = rnorm(50)) gls(y ~ x, data=d, correlation = corARMA(p=2)) #This works Gls(y ~ x, data=d, correlation = corARMA(p=2)) # Gives error # Error in

Dear List, I am looking for what B.S.Everitt refers to as Cochrane Method for testing independence in combined 2x2 contingency tables. Is it the same method as the Cochran-Mantel-Haenszel Chi-Squared Test for Count Data in R? Thanks, Serguei [[alternative HTML version deleted]]

Dear R developers, first of all let me join the chorus of congratulations for the release of R 1.0.0. Well, done! Unfortunately, I find it necessary to e-mail in a bug report regarding the `nlme' package. On my office machine I experience the following trouble: bossiaea:/opt/R$ R CMD check -c nlme Checking package `nlme' ... Massaging examples into `nlme-Ex.R' ... Running

Full_Name: W.B.Kloke Version: 1.8.0 OS: FreeBSD-4.7 Submission from: (NULL) (195.253.22.63) I found a parameter evaluation inconsistency in NLME package. I tried to use gls() inside a function, and I wanted use gls() for different subsets of a data frame: prgls <- function(name){ gls( log10(Y)~(cond-1)+(cond-1):t ,pr,subset=subject==name)} Applying this function with a string as parameter

I'd like to know why I cannot get a plot and the QQnorm in the same sheet. The commands are simple but: library(nlme) glmod1 <- gls(upfmla,correlation=corAR1(),method="ML") summary(glmod1) par(mfrow = c(2,1)) plot(glmod1, main="GLS Residuals vs. GLS Fitted") qqnorm(glmod1) No matter what (I tried different permutations of the plotting commands) the second drawing

Hi I have a huge array with series of data. For each cell in the array I fit a linear model, either using lm() or gls() with lm() there is no problem, but with gls() I get an error: Error in glsEstimate(glsSt, control = glsEstControl) : computed gls fit is singular, rank 2 as soon as there are data like this: > y1 <- c(0,0,0,0) > x1 <- c(0,1,1.3,0) > gls(y1~x1)

Dear R-help, The following nlme example, from help(Variogram.gls), does not work on my system. > data(BodyWeight) > fm1 <- gls(weight ~ Time * Diet, BodyWeight) > Variogram(fm1, form = ~ Time | Rat)[1:10,] Error in FUN(X[[1]], ...) : unused argument(s) (method ...) It looks like I might not have loaded a necessary package. I did load nls and lattice (the latter not actually being

Hello everybody, Please help with connecting the AIC and BIC numbers printed by summary.gls to the logLik number. 1. is the logLik number the true ML or density scaling constants have been omitted? 2. what is the formula for calculating the AIC and BIC from logLik (and how can I see it)? I tried printing summary.gls but it says object not found. Thank you very much. Stephen [[alternative

Dear List: My question is more statistical than R oriented (although it originates from my work with nlme). I know statistical questions are occasionally posted, so I hope my question is relevant to the list as I cannot turn up a solution anywhere else. I will frame it in the context of an R related issue. To illustrate the problem, consider student achievement test score data with multiple

Dear all I have undertaken some phylogenetic and non-phylogenetic regressions with gls() in nlme with single preictor variables. A p value is associated with the intercept (upper p value) and another with the predictor variable (lower). Which p value is important? What does it mean if the intercept p value is insignificant but the predictor is still significant? Thanks a lot, and sorry for my

I have spatial data on a sphere (the Earth) for which I would like to run an gls model assuming that the errors are autcorrelated, i.e. including a corSpatial correlation in the model specification. In this case the distance metric should be calculated on the sphere, therefore metric = "euclidean" in (for example) corSpher would be incorrect. I would be grateful for help on how to

Dear All -- I am trying to use within a little table producing code an anova comparison of two gls fitted objects, contained in a list of such object, obtained using nlme function gls. The anova procedure fails to locate the second of the objects. The following code, borrowed from the help page of anova.gls, exemplifies: --------------- start example code --------------- library(nlme) ##