similar to: covariate data errors

Dear List: I am working to construct a matrix of a particular form. For the most part, developing the matrix is simple and is built as follows: vl.mat<-matrix(c(0,0,0,0,0,64,0,0,0,0,64,0,0,0,0,64),nc=4) Now to expand this matrix to be block-diagonal, I do the following: sample.size <- 100 # number of individual students I<- diag(sample.size) bd.mat<-kronecker(I,vl.mat) This

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,

Hi all, Is it possible to have a test value for assessing the normality of residuals from a linear regression model, instead of simply relying on qqplots? I've tried to use fitdistr to try and fit the residuals with a normal distribution, but fitdsitr only returns the parameters of the distribution and the standard errors, not the p-value. Am I missing something? Cheers, Federico

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 R-users, There are tons of methods out there for fitting independant variables to a dependent variable. All stats books tell you about the assumptions behind OLS (ordinary least squares) and warn against abusive use of the method (which many of us do disregard by lack of a better knowledge). Most introductory text books stop there and don't tell you what the next best option might be. I

I found this paper on ANOVA on unequal error variance. Has this be incorporated to any R package? Is there any textbook that discuss the problem of ANOVA on unequal error variance in general? http://www.jstor.org/stable/2532947?cookieSet=1

A couple of questions: How to be sure that gls allowes errors to be correlated and/or have unequal variances? (is this on auto or is there a switch?) How to calculate confidence limits for a linear regresssion? -------------- next part -------------- A non-text attachment was scrubbed... Name: dthompson.vcf Type: text/x-vcard Size: 303 bytes Desc: Card for David Thompson Url :

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

I have a bayesian hierarchical normal regression model, in which the regression coefficients are nested, which I've wrapped into one regression framework, y = X %*% beta + e . I would like to run data through the model in a filter style (kalman filterish), updating regression coefficients at each step new data can be gathered. After the first filter step, I will need to be able to feed

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 R-sters, Just wondering what I might be doing wrong. I'm trying to fit a multiple linear regression model, and being ever mindful about the possibilities of autocorrelation in the errors (it's a time series), the errors appear to follow an AR1 process (ar(ts(glsfit$residuals)) selected order 1). So, when I go back and try to do the simultaneous regression and error fit with gls,

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

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

Hi to all, In other statistical software, such as Eviews, it is possible to regress a model with the Least Squares method, assuming that the residuals follow an AR(q) process. For example the resulting regression is something like y = 1.2154 + 0.2215 x + 0.251 AR(1) How is it possible to do the same in R? Thank you very much in advance, Constantine Tsardounis http://www.costis.name

In R factanal output, I can't find a function to give me residuals e. I mannually got it by using x -lamda1*f1 -lamda2*f2 - ... -lamdan*fn, but the e I got are not uncorrelated with all the f's. What did I do wrong? Please help. Yijun ____________________________________________________________________________________ Be a better friend, newshound, and

Dear all, I met with some problems when dealing with a time series with serial correlation. FIRST, I generate a series with correlated errors set.seed(1) x=1:50 y=x+arima.sim(n = 50, list(ar = c(0.47))) SECOND, I estimate three constants (a, b and rho) in the model Y=a+b*X+u, where u=rho*u(-1)+eps library(nlme) gls(y~x,correlation = corAR1(0.5)) # Is it the right procedure?

Hi all, I would like to do a weighted linear regression, when the error of the dependent variable is correlated. So I have a weighting (covariance) matrix instead of a vector. As I understood the „weights” argument in the lm function should be a vector and not a matrix. Can anyone suggest me a function (package) which would do the job? Thanks a lot! Emese [[alternative

Hi: The gls function I used in my code is the following fm<-gls(y~x,correlation=corARMA(p=2) ) My question is how to extact the AR(2) parameters from "fm". The object "fm" is the following. How can I extract the correlation parameters Phi1 and Phi2 from "fm"? These two parametrs is not in the "coef" componenet of "fm". Thanks a

Hi there, I am using gls from the nlme library to fit an AR(1) regression model. I am wondering if (and how) I can separate the auto-correlated and random components of the residuals? Id like to be able to plot the fitted values + the autocorrelated error (i.e. phi * resid(t-1)), to compare with the observed values. I am also wondering how I might go about calculating confidence (or

Hello, I have been trying to get an ANOVA table for a linear model containing a single nested factor, two fixed factors and a covariate: carbonmean<-lm(C.Mean~ Mean.richness + Diversity + Zoop + Diversity/Phyto + Zoop*Diversity/Phyto) where, *Mean.richness* is a covariate*, Zoop* is a categorical variable (the species), *Diversity* is a categorical variable (Low or High), and