similar to: how to compute uncentered (pearson correlation) correlation efficiently

Dear all: I am comparing the PLS outputs of R and SAS for the following data set: Y x1 x2 x3 3 6 2 2 3 1 5 5 4 7 4 1 5 6 5 6 2 4 3 2 8 5 0 9 where Y is the dependent variable and x1, x2, x3 are the independent variables. I found several PLS algorithms in R (NIPALS,SIMPLS,KERNEL PLS). SAS has SIMPLS and NIPALS. The following are the NIPALS calculations of

Hi! My question(s) in the end might be silly but I am no expert on this, so here it goes: Noy-Meir (1973), Pielou (1984) and a few others have pointed to non-centered PCA being in some cases useful. They clearly explain that "it is the case" when multi-dimensional data display distinct clusters (which have zero, or near-zero, projections in some subset of the axes) and the task is

Hello, I have a short question about the prcomp function. First I cite the associated help page (help(prcomp)): "Value: ... SDEV the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix). ROTATION the matrix of variable loadings

Dear All I'm using PVClust to perform hierarchical clustering, for the output plot I can control most of the graphical I need, however the plot is large and I would like to split it vertically into two panels one above the other. Is there a way to plot only part of a PVClust plot, I tried to convert it to a dendrogram with result2 = as.dendrogram(result) however I get the error message

Hi people, I want to obtain the adjusted r-square given a set of coefficients (without the intercept), and I don't know if there is a function that does it. Exist???????????????? I know that if you make a linear regression, you enter the dataset and have in "summary" the adjusted r-square. But this is calculated using the coefficients that R obtained,and I want other coefficients

Given four known and fixed vectors, x1,x2,x3,x4, I am trying to generate a fifth vector,z, with specified known and fixed partial correlations. How can I do this? In the past I have used the following (thanks to Greg Snow) to generate a fifth vector based on zero order correlations---however I'd like to modify it so that it can generate a fifth vector with specific partial

I am trying to generate a fourth vector,z, given three known and fixed vectors, x1,x2,x3 with corresponding known and fixed correlations with themeselves and with z. That is, all correlations are known and prespecified. How can I do this? Thank you, ben