similar to: correspondence analysis and canonical correspondence analysis in R

Over the last couple of years I have written quite a few R programs for various "psychometric" techniques, and I am regularly updating and expanding what is there. I now have (wholly or partially), or have planned -- gifi package (update to homals on CRAN). Code for multiple correspondence analysis, nonlinear principal component analysis, nonlinear multiset canonical

Dear UserR's, The smacof package (see also our PsychoR repository on http://r-forge.r-project.org/projects/psychor/) is uploaded on CRAN. This package provides the following approaches of multidimensional scaling (MDS) based on stress minimization by means of majorization (smacof): - Simple smacof on symmetric dissimilarity matrices - smacof for rectangular matrices (unfolding models) -

but maybe of use to some: http://gifi.stat.ucla.edu/psychoR/derivatives Computes generalized eigenvalue solutions Ax=\lambda Bx and generalized singular value solutions Rz=\gamma Px and R'x=\gamma Qy for matrices that are differentiable functions of a vector of parameters. Along with the decomposition the code returns arrays with all first-order partial derivatives of the values/vector wrt

anacor-0.9.0 is on CRAN (by De Leeuw and Mair) anacor does correspondence analysis and canonical correspondence analysis. It can make row plots, column plots, joint plots, Benz?cri plots, regression plots, and transformation plots. Where appropriate, plots can be in 3d using either rgl or scatterplot3d. Row and column points can be in standard scaling, Benz?cri scaling, Goodman scaling,

anacor-0.9.0 is on CRAN (by De Leeuw and Mair) anacor does correspondence analysis and canonical correspondence analysis. It can make row plots, column plots, joint plots, Benz?cri plots, regression plots, and transformation plots. Where appropriate, plots can be in 3d using either rgl or scatterplot3d. Row and column points can be in standard scaling, Benz?cri scaling, Goodman scaling,

Many new things at http://www.cuddyvalley.org/psychoR/ The scalassoc package, which fits exponential distance association models to indicator matrices, it now at version 1.0.0. It seems to be robust and can analyze large examples easily. It is a major improvement (in speed and robustness) over the distassoc package, which is at the same site. scalassoc does something neat (if you like

I think i mentioned this in private communication a while back, but in case anyone else cares: I have a version of canonical correspondence analysis (CCA) coded up in R. I based it on Ter Braak and Prentice (1988) Adv. Ecol. Res. 18:271-317, Ter Braak (1986) Ecol. 67(5):1167-1179, and Ter Braak (1995) Section 5.9 in Jongman, Ter Braak, and van Tongeren "Data Analysis in

R-ecologists: Anyone wanting to create a Krebs package for R can do so using the C-source code avalaible at: ftp://gause.biology.ualberta.ca/pub/jbrzusto/krebs/source.zip Barry J. Cooke Current mailing address: Ph.D. Candidate 3971 NW 23 Circle Environmental Biology and Ecology Gainesville, Florida, USA Department of Biological Sciences 32605

Hello! I need to perform weighted multidimensional scaling analysis(WMDS). I did rsitesearch, googled, but I could find no info on how to perform WMDS using R. In several places they say it is possible with the ALSCAL algorithm, but I could not find the relevant function to carry it out. ----------------------------------------------------------------- ???????? ?? ??????? ?? ??? ???????????!

Dear all, My data includes almost three thousand people who rank ten categories into three variables. The simple example below is almost same except I have many missing values. x <- cbind( sample( LETTERS[1:10] , 3000 , replace = TRUE ) , sample( LETTERS[1:10] , 3000 , replace = TRUE ) , sample( LETTERS[1:10] , 3000 , replace = TRUE ) ) I try to figure

Dear R-helpers, I have dist class objects for 10 individuals rating the dissimilarities (on a 100-point scale) of the same 10 faces (analogous to the bread data). I would like to get an individual differences scaling jointly for the individual judges and the faces, and plot them on the same axes. This is the example: library(smacof) data(breakfast) res.rect<-smacofRect(breakfast,

Hi R-users, I am new to R environment.I want to carry out a correspondence analysis on a contigency table with 64 columns and 298 observation of Environmental data.In many cells of the contigency table the frequency is just the value '1'. I got the following error. > CAIV(Eplankton) Error in if (L[i, j] < 0) return("Table L must contain non-negative numbers") :

Hi Michael, with res.uc$conf you'll get the single configurations for each rater. You can use these to produce the plot you want to have. Best, Patrick r-help-request at r-project.org wrote: > Send R-help mailing list submissions to > r-help at r-project.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://stat.ethz.ch/mailman/listinfo/r-help >

We need to keep the discussion on the list. When I run your code, there are several problems. strain.data <- read.xlsx("Dee rhiz.xlsx", sheetName ="strain", header = T, row.names = 1) str(strain.data) # lists 9 columns at the end with all NAs strain.data1 <- (strain.data, sqrt.dist = TRUE) # this is not a valid R line. I get Error: unexpected ',' in

Dear Sir/Madam, This is Animesh working on Canonical Correlation Analysis ( CCA ) and I am using CCA package in R. I have a doubt and it may be very basic question. Hope someone will get back to me with answer. I am trying to follow the documentation of CCA but could not make out some of the stuffs. 1) I simulated some of the data sets and got the result. I want to

Hello Sir I am getting problem in plotting in CCA . Could you please help me? I wrote the below command but I don't know why it is taking only first 5 env data rather than all 9. > strain.data <- read.xlsx("Dee rhiz.xlsx", sheetName="strain", header = T, row.names = 1) > env.data <- read.xlsx("Dee rhiz.xlsx", sheetName="env", header = T,

Hi all, thank you for your answers; i've tried both cca from vegan library, and dudi.coa from ade4 library; one last question: my deal is mainly with contingency tables, like the one i'm posting here acciaieria<-c(.41,.02,.44,.04,.09) laminatoio<-c(.34,.28,.26,.01,.11) fonderia<-c(.48,.05,.34,.08,.05) leghe<-c(.45,.19,.25,.03,.08)

x<-seq(-1,1,length=10) y<-seq(-1,1,length=10) a<-matrix(c(1,2,2,1),2,2) b<-matrix(c(2,1,1,2),2,2) fv<-function(x,y) { m<-x*a+y*b t<-m[1,1]+m[2,2]; d<-m[1,1]*m[2,2]-m[1,2]^2 return((t-sqrt(t^2-4*d))/2) } gv<-function(x,y) { t<-x*(a[1,1]+a[2,2])+y*(b[1,1]+b[2,2]) d<-(x*a[1,1]+y*b[1,1])*(x*a[2,2]+y*b[2,2])-(x*a[1,2]+y*b[1,2])^2 return((t-sqrt(t^2-4*d))/2) }

homals-0.9.0 is on CRAN -- by Jan de Leeuw and Patrick Mair This package implements the methods discussed in Gifi, Nonlinear Multivariate Analysis, Wiley, 1990. In the Gifi terminology it covers homals, princals, canals, morals, criminals, and overals. The R implementation fills several gaps in Gifi, adding multiple ordinal, numerical, and polynomial data transformations. Differences with

homals-0.9.0 is on CRAN -- by Jan de Leeuw and Patrick Mair This package implements the methods discussed in Gifi, Nonlinear Multivariate Analysis, Wiley, 1990. In the Gifi terminology it covers homals, princals, canals, morals, criminals, and overals. The R implementation fills several gaps in Gifi, adding multiple ordinal, numerical, and polynomial data transformations. Differences with