similar to: Covariance matrix for first canonical variate

Hi, from what I understand about the canonical correlation function 'cancor', it looks for correlations in two sets of variables, each represented in matrix form. Right? Sounds exactly like what I need. I have tried the following but I am not sure how to interpret the results. AudioPCs <- c(ArTHarF0PCA$x[,2], ArTHarF1PCA$x[,2], ArTHarF2PCA$x[,2], ArTHarF3PCA$x[,2],

Hi ! I am trying to get at the covariance of the predictions of a linear model. Suppose the we have: > x<-runif(1000) > y<-2 + 25x*x +rnorm(1000) > lm1 <-lm(y~x, data = data.frame(y = y, x=x)) > x.pred <-runif(10) > y.hat <- predict(lm1, newdata = data.frame(x=x.pred)) I was wondering how to get an estimate of the covariance of y.hat which would be a 10 x 10

Hi everyone, Can I suggest an additional output component in cancor, from package mva? It would be useful to have the number of canonical correlation vectors, equivalently the rank of the covariance between x and y (label "rank"). This would usually be min(dx, dy), where dx and dy have already been computed for the svd function, but there might be situations where it was less than

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

i have search the web and not find code to cal these index.so i write one.if there exists code for such index,i hope you can let me know. this is my first R code.i send the the list and hope uesRs give me some advise for improve it or check if i make any mistake . i appriciate your suggestion . i have check the result roughly to the SPSS's. though i do not know excatly how SPSS calculate the

Dear all R users, I wanted to calculated a sample Variance covariance matrix of a five-variate normal distribution. However I stuck to calculate each element of that matrix. My question is should I calculate ordinary variance and covariances, taking pairwise variables? or I should take partial covariance between any two variables, keeping other fixed. In my decent opinion is I should go for the

Hello, I'm a beginning user of R, now I have a question about canonical correlation analysis (cca). In R,there is a function "cancor.R" used for cca; For example X(n*p1) and Y(n*p2)are the two matrix to be analyzed. In the example given by R, when n> max(p1,n2), cancor(X,Y) works; but when n<p1 or n<p2, cancor(X,Y) doesn't work well because cancor$cor == 1; how to cope

Dear R's! I am strugling with cancor procedure in R. I cannot figure out the meaning of xcoef and of yxcoef. Are these: 1. standardized coefficients 2. structural coefficients 3. something else? I have tried to simulate canonical correlation analysis by checking the eigenstructure of the expression: Sigma_xx %*% Sigma_xy %*% Sigma_yy %*% t(Sigma_xy). The resulting eigenvalues were the same

I think SAS Macros has capability to call R, and execute it without it being in the picture anywhere. So you can use SAS Macros in a file called R.sas In this you can create a macro called %Describe that can call R , load Hmisc ,run the describe function Note you will need repeated use of %put in this %describe for the mapping to take place Use %INCLUDE to include that file in all SAS

Hi, Does anybody know some package or function that computes canonical variates? Luis & Janete -------------------------------------------- sapo.pt/kitadsl -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

New versions of source packages for Hmisc and Design are available from CRAN for R 1.9 on Linux/Unix. Knowing Uwe Ligges, a Windows binary is not far behind. Changelogs are at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/ChangelogHmisc and http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/ChangelogDesign . These versions pass R CMD check for the latest R-devel so I expect they

New versions of source packages for Hmisc and Design are available from CRAN for R 1.9 on Linux/Unix. Knowing Uwe Ligges, a Windows binary is not far behind. Changelogs are at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/ChangelogHmisc and http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/ChangelogDesign . These versions pass R CMD check for the latest R-devel so I expect they

I am having difficulty running R CMD check using the 30May04 version of R-devel and a new version of the Hmisc package: * checking S3 generic/method consistency ... WARNING Error in .tryQuietly({ : Error in library(package, lib.loc = lib.loc, character.only = TRUE, verbose = FALSE) : .First.lib failed Execution halted See section 'Generic functions and methods' of the

R-Listers. The following is a rant originally sent privately to Frank Harrell in response to remarks he made on this list. The ideas are not new or original, but he suggested I share it with the list, as he felt that it might be of wider interest, nonetheless. I have real doubts about this, and I apologize in advance to those who agree that I should have kept my remarks private. In view of this,

Robust Covariance Estimation Software via Nearest Neighbor Variance Estimation (NNVE) Software to carry out robust covariance estimation by Nearest Neighbor Variance Estimation (NNVE) [Wang and Raftery (2002, J. Amer. Statist. Ass.)] is now available for R and Splus. In the simulation studies published in JASA, this had mean squared error at least 100 times smaller than that of other leading

Robust Covariance Estimation Software via Nearest Neighbor Variance Estimation (NNVE) Software to carry out robust covariance estimation by Nearest Neighbor Variance Estimation (NNVE) [Wang and Raftery (2002, J. Amer. Statist. Ass.)] is now available for R and Splus. In the simulation studies published in JASA, this had mean squared error at least 100 times smaller than that of other leading

Hello, currently I am trying to fit a generalized linear mixed model using the glmmPQL function in the MASS package. I am working with the data provided by the book from Heck, Thomas and Tabata (2012) - https://www.routledge.com/Multilevel-Modeling-of-Categorical-Outcomes-Using-IBM-SPSS/Heck-Thomas-Tabata/p/book/9781848729568 I was wondering, which variance-covariance structure the glmmPQL

Dear R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Hi all: I have always used SPSS to estimate weekly covariance based on a linear regression model but have to hard code the model Std. Error and the Mean-Square and then execute one week a the time. I was wondering if someone could give me an idea on how to estimate weekly(WK) covariance using the summary and anova of "dfr"(lineal model below). I have to do this for 52

Hello everyone Is there a function/command to simulate from "matrix variate normal distribution" in R. A follow up question would be is there a function/command to obtain the density, distribution and quantile function of "matrix variate normal distribution" in R. Wikipedia has a good description of "matrix variate normal distribution" which is also alternatively