similar to: Nonparametric Canonical Correlation

Dear R-users, I have the following question: I am currently performing Canonical Correlation analysis in two data sets (of the same size: identical cases and identical variables. The data in the second data set are actually a transformed version of the data in the first data set.) Now I want to compare the (first two) canonical correlations as well as set correlation in these two data sets to

Dear R, I recently did a canonical correlation analysis on two subsets of data (location and weather). So I now have canonical scores for location and weather. but I'd now like to do a scatterplot matrix using the pairs statement. Is there a way to somehow join location.U and weather.V to become a new data set from which I could undertake a scatterplot matrix of the canonical variates?

Dear all, I didn't find any graphical solution in the package "mva" to plot the canonical scores from a CCA (canonical correlation analysis). Does anybody knows how to plot or has anybody already programmed : - the map of the canonical scores, - the graph of the canonical weights, - the correlation circle i.e. the canonical loadings ? Thank you for help ...

Dear Sir/Madam, This is Animesh working on Canonical Correlation Analysis ( CCA ) and I am using CCA package in R. I have a doubt and it may be very basic question. Hope someone will get back to me with answer. I am trying to follow the documentation of CCA but could not make out some of the stuffs. 1) I simulated some of the data sets and got the result. I want to

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],

Colleagues, I ran some nonparametric regressions in R (run in RedHat Linux), then a colleague repeated the analyses in SAS. When we obtained different results, I tested S-Plus (same Linux box). And, got yet different results. I replicated the results with a small dataset: DATA: 37.5 23 37.5 13 25 16 25 12 100 15 12.5 19 50 20 100 13 100 10 100 10 100 16 50 10 87.5

For some small sample sizes I would like to exemplify the combinatorics underlying certain nonparametric tests like Mann-Whitney-Wilcoxon, Kruskal-Wallis and Spearman's rank correlation. I have written a function all.perm which delivers all permutations of 1,2,...,n and which works reasonably well. This can be used to generate P(R=r) of Spearman's rank correlation:

I tried 2 methods to estimate C.I. of correlation coefficient of variables x and y: > x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1) > y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8) #METHOD 1: Pearson's ********************************************************** > cor.test(x, y, method = "pearson", conf.level = 0.95) Pearson's

Hello everybody ! I try to obtain in R eigenvectors and canonical analysis on MANOVA results, but I don't find how to process? In particular, I would be interesting to obtain "standardized canonical coefficients" of the canonical variates. There analysis give some information on the correlation between response variates. My data are organised in 2 terms (one is continu, one is a

I have the following code:   ## to check correlation between the simulated uniform data x2 <- uni[,1] ; x2[1:10] y2 <- uni[,2] ; y2[1:10] result2 <- boot(cbind(x2,y2), f, 20) # get 95% confidence interval boot.ci(result2, type="bca") cor.test(x2,y2, method="pearson", conf.level=0.95)   part of my data:   > x2 <- uni[,1] ; x2[1:10]  [1] 0.63933145 0.71677785

Dear R users, Does the results below make any sense? Can the the interval of the correlation coefficient be between *-1.0185* and -0.8265 at 95% confidence level? Liviu > library(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="spearman")) Spearman's rank correlation rho data: mpg and wt S = 10292, p-value = 1.488e-11 alternative hypothesis: true rho is not

Can anyone recommend a good nonparametric density approach for data bounded (say between 0 and 1)? For example, using the basic Gaussian density approach doesn't generate a very realistic shape (nor should it): > set.seed(1) > dat <- rbeta(100, 1, 2) > plot(density(dat)) (note the area outside of 0/1) The data I have may be bimodal or have other odd properties (e.g. point mass

Dear List, I would like to obtain biplot containing arrows and variable labels. However, after checking the previous message and package CCA and function anacor, my attempt remains an open question. Please kindly advise code or package I might miss to achieve it. Thank you. Elaine [[alternative HTML version deleted]]

Hello, I tried to obtain a nonparametric correlation matrix with the following syntax: by(mydat, mydat$GROUP, function(subset) { matrix <- cbind(subset$WK1FREQ, subset$WK2FREQ, subset$WK3FREQ, subset$WK4FREQ, subset$BESTDAY) cor(matrix, method="kendall",use="pairwise.complete.obs") }) Error: syntax error In addition to the matrix, I was hoping to obtain p-value and CI

Hello, I wuold like to propose my modifications of the original cor.test to you : I tried to calcolate the correct p-value for Spearman and Kendall's test with ties. Let me know what you think. Thanks you for your time. Antonietta di Salvatore test <- function(x, ...) UseMethod("test") test.default <- function(x, y, alternative = c("two.sided",

Dear R users, A new package titled `np' is now available from CRAN. The package implements recently developed kernel methods that seamlessly handle the mix of continuous, unordered, and ordered factor datatypes often found in applied settings. The package also allows users to create their own nonparametric/semiparametric routines using high-level function calls (via the function npksum())

Dear R users, A new package titled `np' is now available from CRAN. The package implements recently developed kernel methods that seamlessly handle the mix of continuous, unordered, and ordered factor datatypes often found in applied settings. The package also allows users to create their own nonparametric/semiparametric routines using high-level function calls (via the function npksum())

hi, i'm looking for a smart way of distributing items to different groups according to their correlation(s). the correlation of items within one group should be minimal, whereas (canonical) correlation between the blocks should be maximal. example: you have 20 items, which are to be split into 4 blocks with 5 items in each block. (of course, the numbers are to be interchangable). so far, 2

Dear R users: Could you please help me out how to use R to get and interpret Standardized discriminant functions and structure coefficients for sex, Degree_Program, and Enrol_USM Please find the data file at: http://www.usm.maine.edu/~aaboueissa/Rhelps/Rhelps.html data files are (xls file: dataforRxls or txt file: dataforRtxt) Output looks like this table. Standardized

Dear All, I am using a specific approach for my master thesis. In essence, a supervised reclassification is used as an intermediate step to find chemical parameters which are able to reclassify defined groups. These variables will be used in a next step where location and scale estimators of the groups are important. Traditionally linear discriminant analysis is used for reclassification which