similar to: Help about princomp

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

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 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

Hi everyone, I am relatively new to R, and I need to perform the principal components analysis of a data matrix. I know that there are a bunch of methods to do it (dudi.pca, princomp, prcomp...) but I have not managed to find a method that can return the residuals obtained by retaining X principal components of the original data, as this MATLAB function can do: http://is.gd/6WeUFF Suggestions?

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

I know this question is very simple, but I am not figure it out I have the data frame:   test<- data.frame(year=c(2000,2000,2001,2001),x=c(54,41,90,15), y=c(29,2,92,22), z=c(26,68,46,51)) test   year    x   y   z 1 2000 54 29 26 2 2000 41  2  68 3 2001 90 92 46 4 2001 15 22 51   I want to sum the vectors x, y and z within each year (2000

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

Is it possible to make disappear the id numbers from scatter.dudi (mc analysis) ? a <- as.factor(c(1, 2, 3, 2, 1)) b <- as.factor(c(3, 2, 3, 1, 1)) x <- as.factor(c(1, 2, 2, 1, 3)) y <- as.factor(c(2, 2, 3, 1, 1)) dat <- data.frame(a=a, b=b,x=x,y=y) summary(dat) dat require(ade4) dat.acm <- dudi.acm(dat, scann = FALSE, nf = 2) scatter.dudi(dat.acm) Thank you very much !

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

Hello, I am trying to run a PCA in R and I cannot get the PC scores for each of the values. Using pcX <- princomp(X) then loadings(pcX) I can get a listing of the eigenvectors but not the actual PC scores for each value in the dataset. I greatly appreciate any help anyone can offer Thanks Ken

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

Hi all, I am trying to compare two distance matrices with R. I would like to create a XY plot of these matrices and do some linear regression on it. But, I am a bit new to R, so i have a few questions (I searched in the documentation with no success). The first problem is loading a distance matrix into R. This matrix is the output of a the Phylip program Protdist and lookes like this: 5

> Hi > I'm Ph.D student and I need an R code to compute the chi square diistance between n profile rows in a matrix. > > could you help me please? > Thanks > > Paola > ____________________________________________________________

Hello R-ers, I'm trying to do a weighted principal components analysis. I couldn't find any such option with princomp or prcomp. Does anyone know of a package or way to do this? More specifically, the observations I'm working with are averages from populations of varying sizes. I thus need to weight the observations by sample size. Ideally I could apply these weights at the cell

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)

Hello list. I am running dovecot v2.2.13 on debian 8.8; I tried to add a "address =" statement for only localhost and one private IP, but then I got a complaint from system: dovecot[20515]: Error: systemd listens on port 143, but it's not configured in Dovecot. Closing. What is the proper way to resolve this? TIA & Regards, Dudi

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 =

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 all, (Mac OS X 10.4.11, R 2.6.0) I have a quantitative dataset with a lot of Na?s in it. So many, that it is not possible to delete all rows with NA?s and also not possible, to delete all variables with NA?s. Is there a function for a principal component analysis, that can deal with so many NA?s. Thanks in advance Birgit Birgit Lemcke Institut f?r Systematische Botanik

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)