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Hi, I was wondering if function eigen() does something different from the function call eigen() in SAS. I'm in the process of translating a SAS code into a R code and the values of the eigenvectors and eigenvalues of a square matrix came out to be different from the values in SAS. I would also appreciate it if someone can explain the difference in simple terms. I'm pretty new to both

Dear R-devel list members, Ben Fairbank draw it to my attention that factanal() (in the stats package) doesn't report factor correlations for oblique rotations. Looking at the source, I see that factanal also doesn't save the factor-transformation (rotation) matrix from which these correlations can be computed. I've modified the source, attached below, so that the transformation

I try to code the ULS factor analysis descrbied in ftp://ftp.spss.com/pub/spss/statistics/spss/algorithms/ factor.pdf # see PP5-6 factanal.fit.uls <- function(cmat, factors, start=NULL, lower = 0.005, control = NULL, ...) { FAfn <- function(Psi, S, q) { Sstar <- S - diag(Psi) E <- eigen(Sstar, symmetric = TRUE, only.values = TRUE) e <- E$values[-(1:q)] e <-

Dear List, First off, my deepest gratitude to the Sweave developers: this tool has improved my quality greatly. A question in my work I use \Sexpr{} statements scalar values and the xtable package for all manner of tables. What I'd like to do is to use a vector inline, rather than a whole separate table. Something like: %%%%%%%%%%%%%%%% begin code % Latex junk % Sweave block:

--=-hiYzUeWcRJ/+kx41aPIZ Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: 8bit Hi, on March 10 I filed a wishlist bug report asking for the inclusion of some changes to factanal() and the associated print method. The changes were originally proposed by John Fox in 2005; they make print.factanal() display factor correlations if factanal() is called with rotation =

Dear all, I know there have been various questions posted over the years about loops but I'm afraid that I'm still stuck. I am using Windows XP and R 2.9.2. I am generating some data using the multivariate normal distribution (within the 'mnormt' package). [The numerical values of sanad and covmat are not important.] > datamat <-

Hello, R users, I have two factors (treat, section) anova design experiment where there are 3 replicates. The objective of the experiment is to test if there is significant difference of yield between top (section 9 to 11) and bottom (section 9 to 11) of the fruit tree under treatment. I found that there are interaction between two factors. I wonder if I can contrast means from levels of

Hello, R experts, If I understand Ted's anwser correctly, then I can not contrast the mean yields between sections 1-8 and 9-11 under "Trt" but I can contrast mean yields for sections 1-3 and 6-11 because there exists significant interaction between two factors (Trt:section4, Trt:section5). Could I use the commands below to test the difference between sections 1-3 and 6-11 ?

R version: 1.7.1 OS: Red Hat Linux 7.2 When "covmat" is supplied in princomp(), the output value "center" is all NA's, even though the input matrix was indeed centered. I haven't read anything about this in the help file for princomp(). See code below for an example: pc2$center is all NA's. Jerome Asselin x <- rnorm(6) y <- rnorm(6) X <- cbind(x,y)

In summary.glm I'm trying to get a better feel for the z output. The following lines can be found in the function 1 if (p > 0) { 2 p1 <- 1:p 3 Qr <- object$qr 4 coef.p <- object$coefficients[Qr$pivot[p1]] 5 covmat.unscaled <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) 6 dimnames(covmat.unscaled) <- list(names(coef.p), names(coef.p))

Hi, I have a set of covariance matrices but not the original data. I want to carry out some exploratory factor analysis. So, I am trying to construct a covariance matrix list as the input for factanal. I can construct a list which includes the cov, the centers, and the n.obs. But it doesn't work. I get an error that says "Error in sqrt(diag(cv)) : Non-numeric argument to mathematical

Dear friends, I'm using R to produce the following Factor Analysis: > matriz.cor<-hetcor(matrix(as.factor(data), ncol=variables, byrow=T))$correlations > factanal(x=data, factors=2, covmat=matriz.cor, scores='regression') Then the screen output shows the following message: Error en factanal(x = data, factors = 2, covmat = matrix, : requested scores without

Dear all, I've sent this question 2 days ago and got response from Sarah. Thanks for that. But unfortunately, it did not really solve our problem. The main issue is that we want to use our own (manipulated) covariance matrix in the calculation of the mahalanobis distance. Does anyone know how to vectorize the below code instead of using a loop (which slows it down)? I'd really appreciate

Hello, I have been trying for weeks to compile a 64-bit Rblas. I started with ATLAS where I have had success in the past, but 64 bit was not behaving, and as each compilation takes between 9 and 12 hours, "test, check, and revise" was not going to be really viable. I therefore switched to OpenBLas (OPBL). I was successful in compiling R-3.0.1 and an OPBL-based BLAS for Windows 64bit

Dear R users, How to find all single minima within each next part of analyzed vector (table) Select all minima (mass_value=min & mass_value<2) (many) in vector(table), BUT first put mask on table in order to select within one window mask (5 elements) only one local minimum, and next to search within the next time window mask the second minimum (only one local along second mask)

Dear all, We have a data frame x with n people as rows and k variables as columns. Now, for each person (i.e., each row) we want to calculate a distance between him/her and EACH other person in x. In other words, we want to create a n x n matrix with distances (with zeros in the diagonal). However, we do not want to calculate Euclidian distances. We want to calculate Mahalanobis distances, which

On 4 Jan 2003 at 12:51, Wolfgang Lindner wrote: > Please excuse me, if the following questions are *too* off-topic, but I found it > interesting. In inspecting your code I came across an R feature, I could not > find in the online manuals: > > Q1. Looking at the left-handside in your function def: > > "cov.cor" <- function ( covmat ) { >

Hi I am new to R so I apologize if this is trivial. I am trying to predict the resistance or susceptibility of my sequences to a certain drug with a randomForest function from a file with amino acids on each of the positions in the protein. I ran the following: > library(randomForest) > > path <- "C:\\..." > path2 <- "..." > name <-

why does teh following code not work? a<-rnorm(50) dim(a)<-c(10,5) mycov<-function(mat,i,j){ sum((mat[,i]-mean(mat[,i]))*(mat[,j]-mean(mat[,j]))) } covmat<-function(X){ outer(1:dim(X)[2],1:dim(X)[2],function(v1,v2){mycov(X,v1,v2)})} from what i know covmat(a) should give the covariance matrix of a, but it gives a matrix with constant

Hi, I have the following piece of code that is part of a larger function. This piece is the most time consuming part of the function, and I would like to make this a bit more efficient. Could anyone suggest a way to do this faster? In particular, I would like to replace the nested "for" loop with a faster construct. I tried things like "kronecker" and "outer"