similar to: How to construct confidence bands from a gls fit?

According to Hadley's ggplot book (p. 109), both the graphs below should have a legend, and yet none appears in my hands. Any suggestions? I can't see a typo. Is there a bug? library(ggplot2) data(LakeHuron) huron = data.frame(year=1875:1972,level=LakeHuron) p = ggplot(huron, aes(year)) + geom_line(aes(y= level - 5), colour = 'blue') + geom_line(aes(y= level + 5), colour

Hello All, I am trying to create a legend for a black-white graph. The package I use is ggplot2. It can add colors to the legend key but not line types. Can you please help? # example from Wickman (2009, ggplot2 - elegant graphics for data analysis, page 109) library(ggplot2) huron <- data.frame(year=1875:1972, level=LakeHuron) ggplot(huron, aes(year)) +

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 Everyone, I am trying to do a time series regression using the lm function. However, according to the durbin watson test the errors are autocorrelated. And then I tried to use the gls function to accomodate for the autocorrelated errors. My question is how do I know what ARMA process (order) to use in the gls function? Or is there any other way to do the time series regression in R? I highly

I am trying to analyse a data with gls/lm using the following set of models prcn.0.lm <- lm( log10(Y)~(cond-1)+(cond-1):t ,prcn) prcn.1.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn,cor=corAR1()) prcn.0.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn) prcn.1m.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn,cor=corAR1(),method="ML") I get the following AICs for these models:

Hello listmembers, I've been thinking of using gls in the nlme package to test for serial correlation in my data set. I've simulated a sample data set and have found a large discrepancy in the results I get when using the default method REML vs. ML. The data set involves a response that is measured twice a day (once for each level of a treatment factor). In my simulated data set, I

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 all, I am confused. I have encountered some strange behaviour of gls > data(co2) > co2.y <- aggregate(co2,1,mean) > co2.y.data <- data.frame(co2=as.numeric(co2.y),year=seq(1959-1980,along=co2.y)) > co2.1.gls <- gls(co2~year+I(year^2), co2.y.data) > co2.2.gls <- update(CO2.1.gls, corr=corAR1()) > summary(CO2.2.gls) > plot(CO2.2.gls) plot shows standardized

Hi, r-users I got a problem when I try to call a *gls* function in loop structure. The gls function seems not able to recognize the parameters that I pass into the loop function! (But, if I use lm function, it works.) The code looks like this: ================================================= gls.lm <- function(Data, iv1, dv1) { gls.model <- gls(Data[ , dv1] ~ Data[ , iv1], correlation =

There is a bug in 'anova.gls' in the 'nlme' package (3.1-90). The=20 bug is triggered by calling the function with a single 'gls' object=20 and specifying the 'Terms' argument but not the 'L' argument: > library(nlme) > fm1Orth.gls <- gls(distance ~ Sex * I(age - 11), Orthodont, + correlation =3D corSymm(form =3D ~ 1 |

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 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

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, 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

Hi, I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv

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 Rers, I have threes series, x, y, z and I want to fit a model z ~ x + y. First of all, I fit a lm. I found the residuals are correlated, by looking at the acf() and pacf(). Then I tried to fit a gls model allowing residuals to be correlated (correlation = corARMA(p=5, q=1)): y.na <- as.data.frame(y[complete.cases(y),]) y.gls <- gls(z ~ x + y, data = y.na, correlation=corARMA(p=5,

A couple weeks ago I posted a message on this topic to r-help, the response was that this seemed like odd behavior, and that I ought to post it to one of the developer lists. I posted to r-sig-mixed-models, but didn't get any response. So, with good intentions, I decided to try posting once more, but to this more general list. The goal is (1) FYI, to make you aware of this issue, in case it

Hello R-help I am having trouble getting gls to find the R objects that comprise a linear model when the data=named.object option(option!) is not specified. In the gls() help it states data is "an optional data frame containing the variables named in model, correlation, weights, and subset. By default the variables are taken from the environment from which gls is called". An example:

How does one coerce predict.gls to incorporate the fitted correlation structure from the gls object into predictions? In the example below the AR(1) process with phi=0.545 is not used with predict.gls. Is there another function that does this? I'm going to want to fit a few dozen models varying in order from AR(1) to AR(3) and would like to look at the fits with the correlation structure