similar to: mca id numbers

Hi R-community, I am doing a PCA and I need plots for different combinations of axes (e.g., PC1 vs PC3, and PC2 vs PC3) with the arrows indicating the loadings of each variables. What I need is exactly what I get using biplot (pca.object) but for other axes. I have plotted PC2 and 3 using the scores of the cases, but I don't get the arrows proportional to the loadings of each variables on

Dear R-Help i have a newbie question, how edit size fonts and colors in the plots (scatter.acm or dudi.acm function) of multiple correspondence analysis in the ADE4 Package? thanks? Rafael Gutierrez Estad??stico Unidad de Tecnolog??a Cerro Matoso S.A. Tel. 4-7723350 Fax. 4-7723236 ************************************************************************************************* Este correo

Dear All, I am using the scatter.dudi finction in the 'ade4' package to produce correspondence analysis (nice) plots. I do not seem to figure out how to plot the raw coordinates only -- or column coordinates only. I would appreciate any help in doing that. Here is the example I am following -- from the package. data(banque) > banque.acm <- dudi.acm(banque, scann = FALSE, nf =

Is there an option to draw concentration ellipses in biplots ? It seems really nice to summarize large number of points of each group. Cheers../ Murli

Dear R-user, I am trying to use the function 'parLapply' from the 'snow' package which is supposed to work the same wys as 'lapply' but for a parallelized cluster of computers. The function I am trying to call in parallel is 'dudi.pca' (from the 'ade4' package) which performs principal component analyses. When I call this function on a list of

Hi all I?m trying to tests the significance of loadings from a ordination of 46 variables (caategorical, ordinal and nominal). I used dudi.mix from ade4 for the ordination. A years ago Jari Oksanen wrote this script implementing Peres-Neto et al. 2003 (Ecology) bootstraping method: netoboot <- function (x, permutations=1000, ...) { pcnull <- princomp(x, cor = TRUE, ...) res

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

Dear R-helpers, I have two data frames, op and em4: > str(op) 'data.frame': 37 obs. of 5 variables: $ m : num 0.202 0.336 0.122 0.139 0.14 ... $ lln : num 0.798 0.643 0.863 0.835 0.823 ... $ rrn : num 0.789 0.702 0.894 0.895 0.923 ... $ asym2: num 0.177 0.304 0.108 0.187 0.274 ... $ asym3: num 0.0755 0.0975 0.0818 0.0651 0.13 ... > str(rownames(op)) chr

bonjour, Je voudrais faire une analyse des correspondances multiples avec R. avec les repr?sentation graphiques correspondantes avec R. je ne sais pas comment proc?der .. en vour remerciant par avance Faouzi

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)

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")

Hola compañeros de la lista. Qué tal. Tengo un análisis de componentes principales, en el que se evalúan aproximadamente 1000 variables. Usando la función dudi.pca e inertia.dudi obtengo una cantidad de información sobre la influencia de las variables sobre los dos componentes principales. Me gustaría saber si existe alguna función que sobre esta información me arrojara la lista de

Hola! No he podido consultar la doc. del paquete ade4, algo debe estar caído en CRAN ahora mismo. Dos cosas sobre la metodología -aun desconociendo los detalles de cómo lo hace ade4: El output de un PCA, los "pesos" de cada variable en las dimensiones de los componentes se interpretan como correlaciones, a mayor valor absoluto mayor asociación variable-componente. Ahora, como tales

Hi all: How can I calculate the cumulative variance (or variance for each component) in correspondence analysis? If were possible in ADE4 package Thank you -- Antonio Punzón Merino O__---- Instituto Español de Oceanografía c/ /'_ --- Centro Oceanográfico de Santander (*) \(*) -- Promontorio de San Martín S/N ~~~~~~~~~~ 39004-Santander; Spain PO BOX: 240 Tlf: +34 942 29 10 60 Fax: +34

Dear R-users How can I get the eigenvalues out of an lda analysis? thanks a lot christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

Hola compañeros de la lista, qué tal. Estoy haciendo un análisis de componentes principales utilizando las funciones "dudi.pca" (paquete "ade4") y "PCA" (paquete "FactoMineR"). Sucede que al comparar las coordenadas de cada individuo que obtiene cada función, las que corresponden al segundo componente principal tienen idéntica magnitud pero con

Instalo ade4 correctamente y no me abren los datos como por ejemplo data(bsetal97) ¿Qué piensan de eso? De: r-help-es-bounces en r-project.org [mailto:r-help-es-bounces en r-project.org] En nombre de Francesc Carmona Enviado el: Tuesday, October 01, 2013 7:30 AM Para: r-help-es en r-project.org Asunto: Re: [R-es] Análisis de componentes principales con ade4 y FactoMineR Por definición

Dear all, I was looking for methods in R that allow assessing the number of significant principal coordinates. Unfortunatly I was not very successful. I expanded my search to the web and Current Contents, however, the information I found is very limited. Therefore, I tried to write code for doing a randomization. I would highly appriciate if somebody could comment on the following approach.

Hi, I misunderstand the definition of Canberra distance in R. On Internet and in function description pages of dist() from stats and Dist() from amap, Canberra distance between vectors x and y, d(x,y), is : d(x,y) = sum(abs(x-y)/(x+y)) But in use, through simple examples, we find that the formula is : d(x,y) = (NZ + 1)/NZ * sum(abs(x-y)/(x+y)) with NZ = nb of pairs of coordinates that are

Dear Forum, I have a class variable 1 (populations A-E), and two other class variables, variable 2 and variable 3. What I want is to see if the combination of var 2 and var 3, will give me a pattern that allows to distinguish populations. I found several packages like ade4, with pcaiv function and factoMineR. but there are not working. Using the ade4 package, when I try to build the pca: pca1