similar to: Howto get unnormalized eigenvectors?

Full_Name: Martha Nason Version: 2.0.1 OS: Windows XP Submission from: (NULL) (137.187.154.154) I am running simulations using fisher's test on 2 x c tables and a very small p.value from fisher's test (<2.2e-16) is returned as a negative number. Code follows. > set.seed(0) > nreps.outer <-7 > pvalue.fisher <- rep(NA,nreps.outer) > > population1 <- c(

Hi I am conducting 2-sample Kolmogorov Smirnov tests for my Masters project to determine if two independant tree populations have the same size-class distribution or not. The trees have been placed into size-class categories based on their basal diameters. Once I started running the stats on my data, I got confused with the results. Just to show an example of what I was testing I ran stats

How can I get the result of, e.g., poly(1:3. degree=2) to give me the unnormalized integer coefficients usually used to explain orthogonal polynomial contrasts, e.g, -1 1 0 -2 1 1 As I understand things, the columns of x^{1:degree} are first centered and then are normalized by 1/sqrt(col sum of squares), but I can't see how to relate this to what is returned by poly(). >

Hi, I performed an experiment where I raised different families coming from two different source populations, where each family was split up into a different treatments. After the experiment I measured several traits on each individual. To test for an effect of either treatment or source as well as their interaction, I used a linear mixed effect model with family as random factor, i.e.

Hi I'm encountering some problems with coxme My data: I'm looking at the survival of animals in an experiment with 3 treatments, which came from 4 different populations, two of which were infected with a parasite and two of which were not. I'm interested if infected animals differe from uninfected ones across treatments. Factor 1: treatment (3 levels) Factor 2: infection state

Hi, dear R pros I try to understand eigen(). I have seen, that eigen() gives the eigenvectors normalized to unit length. What shall I do to get the eigenvectors not normalized to unit length? E.g. take the example: A [,1] [,2] V1 0.7714286 -0.2571429 V2 -0.4224490 0.1408163 Calculating eigen(A) "by hand" gives the eigenvectors (example from Backhaus,

Ape is an R package for "analyses of phylogenetics and evolution". The first version (0.1) has been released on 27 August 2002 and is available on CRAN. >From the 'Description' file of version 0.1: Ape provides functions for reading, and plotting phylogenetic trees in parenthetic format (standard Newick format), analyses of comparative data in a

Dear R-listers Is there anyone who knows why I get different eigenvectors when I run MatLab and R? I run both programs in Windows Me. Can I make R to produce the same vectors as MatLab? #R Matrix PA9900<-c(11/24 ,10/53 ,0/1 ,0/1 ,29/43 ,1/24 ,27/53 ,0/1 ,0/1 ,13/43 ,14/24 ,178/53 ,146/244 ,17/23 ,15/43 ,2/24 ,4/53 ,0/1 ,2/23 ,2/43 ,4/24 ,58/53 ,26/244 ,0/1 ,5/43) #R-syntax

Hello I've got a question about list indexing. If anyone can help or point in the direction of some documentation, that'd be great. I've checked the Introduction to R and R language definition. Say you have a list of vectors: >

Dear all I am so glad the R can provide the efficient calculate about eigenvector and eigenvalue. However, i have some puzzle about the procedure of eigen. Fristly, what kind of procedue does the R utilize such that the eigen are obtained? For example, A=matrix(c(1,2,4,3),2,2) we can define the eigenvalue lamda, such as det | 1-lamda 4 | =0 | 2 3-lamda | then

Hi, I am using eigen to get an eigen decomposition of a square, symmetric matrix. For some reason, I am getting a column in my eigen vectors (the 52nd column out of 601) that is a column of all NAs. I am using the option, symmetric=T for eigen. I just discovered that I do not get this behavior when I use the option EISPACK=T. With EISPACK=T, the 52nd eigenvector is (up to rounding error) a

Hi list, I am writing several functions and running out variable names. I am using words such as "t", "c", "matrix" to keep the notation same as formulas I am using. For example I have, unnormalized <- function(t, x, y){ val <- rnorm(t, mean=x, var=y) return(val) } metropolis <- function(t, c, x, y){ den1 <- unnormalized(t, mean=x, sd=y)

Hi Duncan et al, Our linker guy brought up a problem with how we pad out our exception tables. Right now we pad them out like this: .section __DATA,__gcc_except_tab .align 2 GCC_except_table13: .byte 0x0 #< --- hun? .byte 0x0 #< --- hun? Lexception13: .byte 0xFF .byte 0x0 .byte 0xB2, 0x1 > Here are his comments: The problem is that the linker parses FDE which gives it

Dear subscribers of R-devel I am experiencing that R crashes (further details are given below) in some complex matrix calculations when EISPACK=TRUE has been specified in eigen(). I discovered the behaviour some months ago just after the release of R-2.2.0, and it has been lying on my desk since. I apologise for not having nailed the problem down to a simple function call, but I thought I

Hi Bill, > It looks like your goal is to keep the 32-bit pointers in the call-site table 4-byte aligned. Here is another solution, instead of having two labels at the start of the LSDA (with pad bytes between them), have no pad bytes and instead use an unnormalized uleb128 for the call-site table length. By unnormalized, I mean one with leading zeros. For instance, instead of: this sounds

Dear R Helper, I am writing because I seek to perform a varimax rotation on my Principal Components Analysis (PCA) solution. (I have been performing PCA's using the eigen command in R.) If you can tell me how to perform this rotation when I use the eigen command (or the princomp command) I would be thrilled. Thanks so much! Wendy Treynor Ann Arbor, MI USA

I have the following situation I want to analyse with prcomp. Each subject has a curve called the contrast sensitivity function (CSF). This curve's overall shape is due to the additive output of 3 "channels" (eigenvectors). #this shows 3 SF channels; net CSF = c1 + c2+c3 x<-1:100 c1<-dnorm(x,mean=20,sd=20) c2<-dnorm(x,mean=50,sd=20) c3<-dnorm(x,mean=80,sd=20)

Hi everybody, I have some problems with the function eigen. I have a square matrix and I want to calculate the eigenvalues and eigenvectors. I apply the function eigen and I get it, however when I solve the same problem in Statistica software, I realise that some eigenvectors are the opposite. How can I get the same values? Thanks in advance [[alternative HTML version deleted]]

Hi ... Please could you help with probably a very simple problem I have. I'm completely new to R and am trying to follow a tutorial using R for Force Distribution Analysis that I got from ... http://projects.eml.org/mbm/website/fda_gromacs.htm. Basically, the MDS I preform outputs a force matrix (.fm) from the force simulation I perform. Then, this matrix is read into R and prcomp is

Can someone confirm that the EISPACK routines for eigenvalues of symmetric matrix are in base R. They seem to be, but I can't seem to locate where they are in the src tree. Thanks. Chong Gu -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or