search for: uncorrelatedness

...? cov[residual_t, residual_(t+k)] = 0 ? Even when residuals are not independent distributed ! (and we know that they aren't normally distributed and they aren't indentically distributed ) And how can I tested it ? Thanks. > Hint, if a ts is normally distributed then independence and uncorrelatedness > are equivalent, hence you can test for normally distributed errors (e.g. > Jarque-Bera-Test). > > HTH, > Bernhard > [[alternative HTML version deleted]]

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

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,

How does one rotate the loadings from a principal component analysis? Help on function prcomp() from package mva mentions rotation: Arguments retx a logical value indicating whether the rotated variables should be returned. Values rotation the matrix of variable loadings (i.e., a matrix whose olumns contain the eigenvectors). The function princomp returns this in the element

Hi all I'm using the Box-Ljung test (from within R) to test if a time-series in independently distributed. 2 questions: 1) p-value returned by Box-Ljung: IF I want to test if the time-series is independant at say 0.05 sig-level (it means that prob of erroneously accepting that the time-series is independent is 0.05 right?) --> then do I consider time-series as "independant"

Dear R users, I want to generate two random variables (X1, X2) from uniform distribution (-0.5, 0.5) with a specified correlation coefficient r. Does anyone know how to do it in R? Many thanks! Menghui

I do not understand, from a PCA point of view, the option center=F of prcomp() According to the help page, the calculation in prcomp() "is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix" (as it's done by princomp()) . "This is generally the preferred method for numerical accuracy"