similar to: GLS models and variance explained

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,

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'm trying to do a simple (?) analysis of a 1D spatial data set, allowing for spatial autocorrelation. (Actually, I'm comparing expected vs. observed for a spatial model of a 1D spatial data set.) I'm using models like gls(obs~exp,correlation=corExp(form=~pos),data=data) or gls(obs~exp,correlation=corLin(form=~pos),data=data) This form is supposed to fit a linear model of

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 trying to plot the original data with the line of the model using the predict function. I want to add SE to the graph, but not sure how to get them out as the predict function for gls does not appear to allow for SE=TRUE argument. Here is my code so far: f1<-formula(MaxNASC40_50~hu3+flcmax+TidalFlag) vf1Exp<-varExp(form=~hu3) B1D<-gls(f1,correlation=corGaus(form=Lat~Lon,

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...

Hello there fellow R users, Im trying to fit a gls model to data which has serial correlation in the errors e(t)=p*e(t-1). However I dont seem to be having much luck in erradicating the autocorrelation in the residuals. I have created the following example. library(nlme) x<-rnorm(100) y<-3+2*x y<-y+arima.sim(100,model=list(ar=(0.6)))+rnorm(100,0,0.2) #Create a data set with first

I'd like to compare two models which were fitted using gls, however I'm having trouble interpreting the results of gls. If any of you could offer me some advice, I'd greatly appreciate it. Short explanation of models: These two models have the same fixed-effects structure (two independent, linear effects), and differ only in that the second model includes a corExp structure for

Dear gls-experts, while reading and testing some examples of the book "introductionary time series analysis with R", I encountered the following fact which puzzles me. Confidence intervals for global temperature time series (P99) computed from general least squares (GLS) to fit the time series. I repeat the example from the book and get the same results: temp.gls=gls(temp ~

Dear R-helpers, I am doing a multiple linear regression of an ozone time-series on time and other explanatory variables. I have been using the "lm" model but I am recently experimenting with "gls". With the "lm" model I was able to look at the residuals by $res in the "summary (lm(...))" and then check with "acf" for autocorrelation in these

Dear all, I have been using the function gls in the package nlme in R to fit some spatial regressions (as described in Dormann et al.). However, I have been struggling trying to find a way to calculate a measure of effect size from these models, so I wanted to know if any of you had an idea on how to do this. More precisely, I am producing a multiple model with an exponential correlation

Dear Users, >From previous analysis (semi-variograms using package gstat), I found spatial autocorrelation in my dataset. The best fitted model to this spatial correlation structure is the Gaussian model (Spherical, Exponential, Linear tested and comparison done by Sum of Square errors). So I used corGaus function to define this spatial autocorrelation in my lme model using the option

Dear R Help, I am having difficulty using Variogram within GLS to examine spatial structure of nested data. My data frame consists of ecological measurements of a forest, in which three landscape positions ("landposi") are compared. Each landscape position is replicated five times ("replicat"), and the environment is measured ("canopy", "litdepth", etc.)

Dear All, I would like to be able to estimate confidence intervals for a linear combination of coefficients for a GLS model. I am familiar with John Foxton's helpful paper on Time Series Regression and Generalised Least Squares (GLS) and have learnt a bit about the gls function. I have downloaded the gmodels package so I can use the estimable function. The estimable function is very

When fitting a simple linear or polynomial regression using lm, R provides a p-value for the whole model as well as for the individual coefficients. When fitting the same models using gls (in order to correct for autocorrelation), there doesn't seem to be a p-value provided for the whole model, although LL, AIC and BIC statistics are provided. Is it possible to obtain a p-value for the whole

I am trying to extract into a scalar the value of Phi from the printed output of gls or lme using corAR1 correlation. ie I want the estimate of the autocorrelation. I can't see how to do this and haven't seen it anywhere in str(model.lme). I can get all the other information - fixed and random effects etc. Is there an obvious way so that I can save the brick wall some damage? TIA

I am trying to fit a generalised least squares model using gls in the nlme package. The model seems to fit very well when I plot the fitted values against the original values, and the model parameters have quite narrow confidence intervals (all are significant at p<5%). The problem is that the log likelihood is always given as -Inf. This doesn't seem to make sense because the model

Dear List: I am having some trouble reproducing some GLS estimates using matrix operations that I am not having with other R procedures. Here are some sample data to see what I am doing along with all code: mu<-c(100,150,200,250) Sigma<-matrix(c(400,80,16,3.2,80,400,80,16,16,80,400,80,3.2,16,80,400),n c=4) sample.size<-100 temp <-

Dear R-help members, I have a question regarding how to use varComb function to specify a variance function for the "weights" in the gls. I need to fit a linear model with heteroscedasticity. The variance function is exp(c0+nu0*W +nu1*W^2) where W is a covariate. Initially I want to use varFunc to define my own variance function following the instruction in the Pinheiro and Bates

Dears, I am specifying a panel model with GLS function and corCAR1 and cor AR1 parameters to correct for serial autocorrelation. But I have one seemingly trivial question: those models fitted that way are random effects models or pooling models? Thanks again, Tomas Notes [[alternative HTML version deleted]]