similar to: R-alpha: Mutivariate Analysis

Here are the functions documented in the Blue Book that I found missing in R (ignoring the ones which are obviously outdated). aggregate allocated amatch axes chull clorder cutree cycle date debugger dget discr faces interp l1fit labclust lag loglin monthplot mstree mulbar napsack odometer persp plclust plotfit rep.int restore rreg sabl sablplot set.seed smooth sort.list Stable stars

Dear R providers and users, I need to compare two sets of data coming from two different multivariate distributions. I would like to apply a two sample Kolmogorov Smirnov test in multivariate case. Is there any R code or package ? Your help is highly appreciated. Many thanks, Esmail Amiri. ____________________________________________________________ Do You Yahoo!? Get your free @yahoo.co.uk

Hello everyone, Does anyone know how to implement the argument "unit" in R's plclust function ? I used to use Splus where this argument exists but it has not been implemented in R's plclust. The reason why I switched from Splus to R is that Ward's method is not implemented for S's hclust whereas it is implemented for R's hclust. What I would need is S's plclust

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

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

Dear Thomas, Unfortunately, I do not know if any packages implement this functionality. Though, it is a topic that interests me. Unlike the "classic discriminant", I prefer to work with the reduced polynomial. This "discriminant" is generalizable to a superset of Chebysev polynomials (which I called Cardano-polynomials). x^3 - 3*c*x - 2*d = 0 x^5 - 5*c*x^3 + 5*c^2*x - 2*d =

kalish at psy.uwa.edu.au wrote: > > I'm a newbie at R, and can't get libraries to really work. > I did this: > > library(help = mva) > cancor Canonical Correlations > cmdscale Classical (Metric) Multidimensional Scaling > dist Distance Matrix Computation > hclust Hierarchical Clustering

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

Dear R I recently performed a cluster analysis. It produced the dendogram no problem but unfortunately the font size of the row.names were all cluttered due to their large size So I tried to change the font size using plclust(cluster.results, labels=iris$specie, cex=0.8) and R came back to me saying Error in plclust(cluster.results, labels = iris$specie, cex = 0.8) : unused argument(s)

I use hierclust (hierarchical clustering) in multiv package. In the documentation it is said that plclust (plotting a dendrogram) is available in S-plus. Can I find it anywhere (I have searched through http://lib.stat.cmu.edu/S and found only quotations of plclust in multiv) or is it only part of the S package (which I don't have)? Thanks --------------- Charles RAUX, Laboratoire

Is the swiss data set in R the same as S dataset swiss.x . I was trying out some clustering by doing the following that I got from Venables and Ripley's book. h<-hclus(dist(swiss.x), method= "connected") plclust(h) cutree(h,3) plclust(clorder(h,cutree(h,3))) I tried swiss instead of swiss.x, it doesnot seem happy. Thanks ../Murli

Good afternoon, After cuting a hierarchical tree using cutree(), how to check correspondances between classes and branches? This is what we do: srndpchc <- hclust(dist(srndpc$x[1:1000,1:3]),method="ward") #creation of hierarchical tree plclust(srndpchc,hmin=20000) #visualisation srndpchc20000 = cutree(srndpchc,h=20000) #returns 4 classes table(srndpchc20000 ) srndclass20000 =

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

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],

I want to plot the cluster trees from the results of hclust() on different datasets, but all with the same distance scale corresponding to the dataset with the largest distance range. However, plclust() does not accept ylim values. Does anyone know of a way around this problem? Mike White

Hi all, I need to plot a dendrogram, but I built a hclust object. Follow the code lines: >dendObject<-list(merge=merge,height=height,order=order,labels=labels,call="hclust(d=d)",method="complete", dist.method="euclidean") >class(dendObject)<-"hclust" >plclust(dendObject,axes=FALSE) Error in cl[[2]] : subscript out of bounds Anybody can help

Dear R-help, I have performed a hierarchical clustering on some data that I have and would like to know some nice ways of visualizing it. I have 2 related questions: i) How to color the labels AND the terminal branch of a dendogram? This is my inelegant way of just getting the colored labels. data(iris) # Formatting data into required format ir <- iris[ ,-5] ir.class <- c(

When I use the function cancor of mva package, I found that it doesn't work when one of the matrix has only one column, or both have only one column. The function in Splus 5 with the same name works under those situations. The version of R I am using is 0.63.2 (released on Jan., 1999) on Solaris. Kenny Ye Assistant Professor Department of Applied Math and Statistics SUNY at Stony Brook

hello list, when I use method lda of the MASS package I experience a warning: variables are collinear in: lda.default(data[train, ], classes[train]) Is there an easy way to recover from this issue within the MASS package? Or how can I tell how severe this issue is at all? I understand that I shouldn't use lda at all with collinear data and should use "quadratische" (squared?)

I want to know will builtin dll always call other builtin dll or it depends on winecfg setting just like other dll? Is it reliable that these dll mixed togther? For example, the builtin gdiplus depend on and call ole dll, does the builtin gdiplus designed to work with both builtin and native ole dll? Will there be any incompatibilities, like different memory allocator? regards,