similar to: PLS in R and SAS

Hi all, I'm looking for an efficient solution (speed and memory) for the following problem: Given - a data.frame x containing numbers of type double with nrow(x)>ncol(x) and unique row lables and - a character vector y containing a sorted order labels Now, I'd like to sort the rows of the data.frame x w.r.t. the order of labels in y. example: x <- data.frame(c(1:4),c(5:8))

I would like to announce the availability of the 'nipals' package on CRAN, https://cran.r-project.org/web/packages/nipals/. The nipals package tries to do just one thing really well...find principal components of a matrix using Nonlinear Partial Least Squares. Missing values are allowed, the principal components are orthogonal, and the code has been heavily optimized. Details of the

I would like to announce the availability of the 'nipals' package on CRAN, https://cran.r-project.org/web/packages/nipals/. The nipals package tries to do just one thing really well...find principal components of a matrix using Nonlinear Partial Least Squares. Missing values are allowed, the principal components are orthogonal, and the code has been heavily optimized. Details of the

Hi all, I've been trying to figure out how to superimpose a histogram on a biplot that shows the relative contribution of each axis. I have been using the NIPALS function (http://biomserv.univ-lyon1.fr/~dray/ files/softwares/nipals.R) to run principal component analyses. Here is a toy example. source("http://biomserv.univ-lyon1.fr/~dray/files/softwares/nipals.R")

This problem is not limited to R, but R is one of the packages in which it arises. princomp is a nice function which creates an object for which inspection methods have been written. Unfortunately, princomp does not admit cases in which the x matrix is wider than high (i. e. more variables than observations). Such cases are typical in spectroscopy and related disciplines. It would be nice if the

Hello i need some precision about nipals in the chemometrics package in R . When i use nipals in chemometrics i obtain T and P matrix. I really don't understand what to do with these two matrix to obtain the scores for every the component (like in spss fo example) Comp1 Comp2 Comp3 quest1 0,8434 0,54333 0,3466 quest2 0,665 0,7655 0,433 Thank you very

Dear all, I would like to fit a linear regression with replication (on each year, observation is replicated, e.g 4 times). The independent variable ranges for instance 1-5 year, so I expect to have a linear fit of 5 points. For that purpose I do these (with dummy variables x and y): x<-rep(seq(1:5),4) y<-rnorm(20) linreg<-lm(y~x) fitted.values(linreg) # why produce 20 points of

Dear list members; The code given below corresponds to the PCA-NIPALS (principal component analysis) algorithm adapted from the nipals function in the package chemometrics. The reason for using NIPALS instead of SVD is the ability of this algorithm to handle missing values, but that's a different story. I've been trying to find a way to improve (if possible) the efficiency of the code,

Hi I have submitted a package (rioja) to CRAN. It checks OK for all R versions and OS's except r-release-macosx-ix86 where it fails when checking the examples. Specifically, it fails because R can't find the package vegan which is needed in a function. Here is the snippet from the check results: ### Begin snippet checking examples ... ERROR Running examples in 'rioja-Ex.R'

Hi, Seeking suggestions to compute uncentered (pearson correlation) correlation efficiently. corr from stats library works on x and y columns. dist from amap library works on x and y rows. My data layout is slightly different such that row(i) of matrix x is compared to row(i) of matrix y. Thanks [[alternative HTML version deleted]]

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

I've been using the pcaMethods to develop a scores matrix ======================================= data(iris) pcIr <- pca(iris[,1:4], method="nipals", nPcs=3, cv="q2") test <- scores(pcIr) ======================================== What I'm looking to do is to use lattice's barchart to plot the scores something like below, but expanded to all the scores

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

I have 4000 observations that I need to randomly relabel 1000 times and then calculate the mean of the 1000 values at each of the 4000 points. Any ideas for where to begin? Thanks Kevin -- View this message in context: http://r.789695.n4.nabble.com/Random-Relabelling-tp3463100p3463100.html Sent from the R help mailing list archive at Nabble.com.

Dear list, I want to calculate SPE (squared prediction error) in x-space, can someone help? Here are my codes: fit.pls<- plsr(Y~X,data=DAT,ncomp=3,scale=T,method='oscorespls',validation="CV",x= T) actual<-fit.pls$model$X pred<-fit.pls$scores %*% t(fit.pls$loadings) SPE.x<-rowSums((actual-pred)^2) Am I missing something here? Thanks in advance. Stella Sim

Good day all. This is to thank all those who have helped in fixing this problem. Starting with a text book was indeed a problem, however, that gave me a clue of what I was looking for. This, with your contributions added to other materials I got on the net, put me on the right track. Thank you so much. Warmest regards Ogbos On 31 January 2010 14:07, S Ellison <S.Ellison@lgc.co.uk> wrote:

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

Friends, Is there a Partial Least Squares package implemented in R? Thanks, Lana [[alternative HTML version deleted]]

I need some help for a curious question of a friend of mine. She usually does some experiments (3-5 repeats for each exp) and then she calculates mean and standard deviation. In microsoft excel she writes something like the following sample mean sd a 1.25 0.35 b 2.65 0.65 c 3.45 0.50 She can do a vertical barplot graph just giving mean value and specifying the

... I checked and Brian and I are both right (see bottom for prior mail exchange). Let me explain: ============================================================= 1. Indeed, in principle, princomp allows data matrices with are wider than high. Example: > x1 [,1] [,2] [,3] [,4] [1,] 1 1 2 2 [2,] 1 1 2 2 > princomp(x1) Call: princomp(x = x1) Standard deviations: