search for: uncorrel

Hello, is it possible to create two uncorrelated random vectors for a given distribution. In fact, I would like to have something like the function "rnorm" or "rlogis" with the extra property that they are uncorrelated. Thanks for your help, Luba [[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

...eject the null hypothesis, than the residuals are independently distributed. Because the residuals are not independently distributed, we know that the squares of residuals are correlated: cov[(residuals_t)^2, (residuals_(t-k))^2] <> 0 (not zero for k <> 0) But, the residuals could be uncorrelated, (even when they are not independent distributed): cov[residuals_t, residual_(t-k)]=0 ! How can I test that merv.reg$residuals are uncorrelated ? Thanks a lot. [[alternative HTML version deleted]]

Dear professor, I'm using package nlme, but I can't find a way to specify two uncorrelated random effects. For example, a random intercept and a random slope. In package lme4, we can specify&nbsp;x + (x ll g) to realize, but how in nlme? Thanks! ???????????????????????? Zhen Wang Graduate student, Department of Medical Statistics, School of Public Health, Sun Yat-sen...

...) the null hypothesis, than the residuals are NOT independently distributed. Because the residuals are not independently distributed, we know that the squares of residuals are correlated: cov[(residuals_t)^2, (residuals_(t-k))^2] <> 0 (not zero for k <> 0) But, the residuals could be uncorrelated, (even when they are not independent distributed): cov[residuals_t, residual_(t-k)]=0 ! How can I test that merv.reg$residuals are uncorrelated ? Thanks a lot. [[alternative HTML version deleted]]

...So I know that: 1 - merv.reg$residual aren't independently distributed (Box-Ljung test) 2 - merv.reg$residual aren't indentically distributed (Breusch-Pagan test) 3 - merv.reg$residual aren't normally distributed (Jarque-Bera test) My questions is: It is possible merv.reg$residual be uncorrelated ? 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...

Folks: Suppose I have 3 random effects, A,B, and C. Using the older lme() function (in nlme) it was possible (using the pdMat classes) to specify that they are uncorrelated with identical variances. Is it possible to do this with lmer? My understanding is that if I specify them as lmer( y ~ ... + (A|Grp) + (B|Grp) + (C|Grp)) then they are uncorrelated but have different variances. Motivation: I'd like to use lmer instead of lme for fitting smoothing splines...

...ated residuals, and I have a means for quantifying those biases as a covariance matrix. I cannot, unfortunately, correct the data for these biases. It seems that this should be a straightforward task, but so much of the literature is concerned with the probability model in which the residuals are uncorrelated that I can't find a good reference. So in order of importance, please, can someone point me to a definitive reference for least squares with correlated residuals, and is there a standard R package to handle this case? Many thanks in advance, Anthony

...astic dependent variable defined as: y=d*z+sqrt(.5+.5*x^2)*e (eq.1) where d is a parameter and, z, x, and e are gamma r.vs. The variables x (the one creating the heteroscedasticity) and z are assumed to be positively correlated. I thought that since the two terms on the rhs of eq.1 are uncorrelated the 3rd central moment of y should equal the sum of the 3rd central moments of the two terms on the rhs. This seems to be correct as long as x and z in eq1 are uncorrelated. But if I make x and z correlated the 3rd moment of y exceeds the sum of the 3rd moments of the terms on the rhs. My...

...the network training set which was preprocessed by prestd. It converts the data back into unnormalized units. prepca preprocesses the network input training set by applying a principal component analysis. This analysis transforms the input data so that the elements of the input vector set will be uncorrelated. In addition, the size of the input vectors may be reduced by retaining only those components which contribute more than a specified fraction (min_frac) of the total variation in the data set. Thanks in advance.

Hi, I work for a company which makes meteor and wind radar (http://www.gsoft.com.au). On occasion (ie during meteor showers such as the Leonids) we configure the system to save raw data as it comes out of the acquisition system, the data rate for this varies (depends on acquisition parameters and number of coherent integrations etc), but usually it is around 600kb/sec. The data consists of 16

...d specifically. I will do an example with T=4. The model is x(i,t) = a*x(i,t-1) + u(i,t) ie x(i,2) = a*x(i,1) + u(i,2) x(i,3) = a*x(i,2) + u(i,3) x(i,4) = a*x(i,4) + u(i,4) I view u(i,t) as a function of a: u(i,t)[a] = x(i,t)-a*x(i,t-1) . the Arellano-Bond method then claims that u(i,3) should be uncorrelated with x(i,1); u(i,4) should be uncorrelated with x(i,1) and also with x(i,2). Blundell Bond adds the further condition that u(i,4) should be uncorrelated with x(i,2)-x(i,1). so, I think of having four sums, each over all firms i's. Let me call cross-sectional summing as sumi. the pen...

Hello, Great to see the new metafor package for meta-analysis. I would like to perform a meta-analysis in which I initially calculate the intercept of the model with a nested random-effects structure. In lme, this would be model<- lme(v3~1, random=~1|species/study, weights = varFixed(~Wt), method = "REML") where multiple effects sizes are measured for some studies and more than

I have measurements with uncorrelated uncertainties on both axes. I would like to get the uncertainties on the intercept and the slope of the weighted linear regression model taking into account the uncertainties of the measurements. Is these any way to do that in R? Thanks- Nicolas

On p. 48 of "Statistics Complements" to the 3rd MASS edition, http://www.stats.ox.ac.uk/pub/MASS3/VR3stat.pdf I read that the orthogonal rotations of Z Lambda^-1 remain uncorrelated, where Z is the PC and Lambda is the diag matrix of singular values. However, the example below that text is > A <- loadings(ir.pca) %*% diag(ir.pca$sdev) If ir.pca$sdev are the singular values, should that be diag(1 / ir.pca$sdev), or is it some discrepancy between S+ and R that I&...

is there any existing function for computing condition index? " analysing multivariate data" say that we can use condition index to check multicollinearity.saying that we can get it via SVD. The elements of the diagnoal matrix are the standard deviations of the uncorrelated vectors. the condition index is the ratio of the largest of these numbers to the smallest. so if i have a data frame a,containg variables x,y,z. my model is : model<-lm(y~x+z,data=a) so use the following to compute the condition index,but it seems wrong. temp<-svd(model.matrix(model))$d...

...same window using the code below.? Somehow it is plotting only one at a time.?? I borrowed the print function from xyplot. ? pdf("QQplotCorrUncorr.pdf") qq1<-qqPlot(residuals(fm), main="QQ plot for Correlated Model") qq2 <-qqPlot(residuals(fma), main="QQ plot for Uncorrelated Model") print(qq1, pos = c(0.0, 0.0, 0.5, 0.5), more = TRUE) print(qq2, pos = c(0.5, 0.5, 1, 1), more = FALSE ) ??? ?dev.off() Help will be appreciated. Thanks, A.K.

Dear all, I made a simple test of the Cholesky decomposition in the package 'Matrix', by considering 2 variables 100% correlated. http://blogs.sas.com/content/iml/2012/02/08/use-the-cholesky-transformation-to-correlate-and-uncorrelate-variables/ The full code is below and can be simply copy&paste in the R prompt. After uncorrelation I still have a correlation of +-100%... ########################################### # 4 observations of 2 variables, 100% correlated obs=matrix(nrow=2,ncol=4) obs[1,]=seq(from=1, to=4, by=1...

Hi, I have been looking at arima.sim to simulate the output from an ARMA model fed with a normal and uncorrelated input series but I cannot find a way to pass an intercept / constant into the model. In other words, the model input in the function allows only for the AR and MA components but I need to pass a constant. Can anyone help? Thanks Paolo [[alternative HTML version deleted]]

Could somebody program this kind of plot type to R, if none exists, based on mds or correlation tables or some more suitable method? What do you think about idea? Does it work? None similar or better exists? http://weightedassociationmap.blogspot.com/ Atte Tenkanen University of Turku, Finland