similar to: princomp loading help

Hi. I was comparing the components from princomp's loadings and the eigen given the same input. I found that the sign of componenets (+/-) are opposite between the two components (from princmop and eigen) but the magnitudes are identical. Why? Thanks! [[alternative HTML version deleted]]

w.age.female.2004 <- nls(WEIGHT ~ (alpha*TOTAL^beta)/454, start=list(alpha=1, beta=3), data=spottedseatrout2004.female.data) I am trying to fit above model to length-weight data of a fish species (spotted seatrout) by year (1999-2006). The convergence occurred for all the years except 2002 and 2004. In these two year, R shows the error called

Attached is a histogram chart with a break on y-axis which my friend made for me using phot shop. Do anyone know how to make such a break using R? Thanks in advance. (See attached file: 2007 age distribution.doc) Hongsheng (Hank) Liao, Ph.D. Lab Manager Center for Quantitative Fisheries Ecology 800 West 46th Street Old Dominion University Norfolk, Virginia 23508 Phone:757.683.4571

My stat textbook tells me that using Shapiro-Wilk test for each variable one by one is not equal to a test for multivariate normality as a whole. Does R have a function of testing for multivariate normality? Thanks. Hongsheng (Hank) Liao, Ph.D. Lab Manager Center for Quantitative Fisheries Ecology 800 West 46th Street Old Dominion University Norfolk, Virginia 23508 Phone:757.683.4571

Where can I download Bhat package? I need to use plkhci function in this package, which estimates confidence interval using likelihood ratio. Thanks. Hongsheng (Hank) Liao, PhD. Lab Manager Center for Quantitative Fisheries Ecology Old Dominion University 757-683-4571 [[alternative HTML version deleted]]

I am trying to fit a logistic model to my 10 year data (1999-2008) by year. Codes like below: Year <- c(1999: 2008) for(y in 1:length(year)) { file.input <- paste("C:\\", year[y], "\\data.csv", sep="") table <- read.csv(file=fileinput, header=TRUE, as.is=TRUE, na.strings=c("")) initial <- getInitial(percent ~ SSlogis(age, Asym,

Full_Name: Jerome Asselin Version: 1.7.1 OS: Red Hat Linux 7.2 Submission from: (NULL) (24.77.125.119) Setting the parameter na.action=na.omit should remove incomplete records in princomp. However this does not seem to work as expected. See example below. Sincerely, Jerome Asselin data(USArrests) princomp(USArrests, cor = TRUE) #THIS WORKS USArrests[1,3] <- NA princomp(USArrests, cor =

Peter, As I understand your Q. You probably have data that is similar to each other like stock Prices for all RHS variable. In that case the difference between corr and cov is not significant; however, if your RHS contains totally dissimilar variables it matters a great deal. If x1 income, x2 job type, x3 Education level, etc..., then taking cov of these variables would not be desireable

Some webpage has described prcomp and princomp, but I am still not quite sure what the major difference between them is. Can they be used interchangeably? In help, it says 'princomp' only handles so-called R-mode PCA, that is feature extraction of variables. If a data matrix is supplied (possibly via a formula) it is required that there are at least as many units as

Hello! I am running principle components analysis using princomp function in pacakge psych. mypc <- princomp(mydataforpc, cor=TRUE) Question: I'd like to use pairwise deletion of missing cases when correlations are calculated. I.e., I'd like to have a correlation between any 2 variables to be based on all cases that have valid values on both variables. What should my na.action be in

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)

I'm attempting to modify how biplot draws its red vectors (among other things). This is how I've started: Biplot <- function(xx, comps = c(1, 2), cex = c(.6, .4)) { ## Purpose: Makes a biplot with princomp() object to not show arrows ## ---------------------------------------------------------------------- ## Arguments: xx is an object made using princomp() ##

Hello, I have a problem with the princomp method, it seems stupid but I don't know how to handle it. I have a dataset with some regular data and some outliers. I want to calculate a PCA on the regular data and get the scores for all data, including the outliers. Is this possible on R? Thank you for helping!!! -- View this message in context:

As of R 1.7.0, princomp no longer accept matrices with more coloumns than rows. I'm curious: Why was this decision made? I work a lot with data where more coloumns than rows is more of a rule than an exception (for instance spectroscopic data). To me, princomp have two advantages above prcomp: 1) It has a predict method, and 2) it has a biplot method. A biplot method shouldn't be too

Hello fellow Rers, I have a no-doubt simple question which is turning into a headache so would be grateful for any help. I want to do a principal components analysis directly on a correlation matrix object rather than inputting the raw data (and specifying cor = TRUE or the like). The reason behind this is I need to use polychoric correlation coefficients calculated with John Fox's

This problem is not limited to R, but R is one of the packages in which it arises. princomp is a nice function which creates an object for which inspection methods have been written. Unfortunately, princomp does not admit cases in which the x matrix is wider than high (i. e. more variables than observations). Such cases are typical in spectroscopy and related disciplines. It would be nice if the

Hi is there a way to plot only the loadings in a biplot (with the nice arrows), and to skip the scores? thanks christoph

According to R help: princomp() uses eigenvalues of covariance data. prcomp() uses the SVD method. yet when I run the (eg., USArrests) data example and compare with my own "hand-written" versions of PCA I get what looks like the opposite. Example: comparing the variances I see: Using prcomp(USArrests) ------------------------------------- Standard deviations: [1] 83.732400 14.212402

Dear all, perhaps I am using princomp.formula and prcomp.formula in a way that is not documented to work, but then the documentation just says: formula: a formula with no response variable. Thus, to avoid a lot of typing, it would be nice if one could use '.' and '-' in the formula, e.g. > library(DAAG) > res <- prcomp(~ . - case - site - Pop - sex, possum)

Hi all! I am working with R package cluster and I have a little problem: let's say I have two datasets...first one ("A") is divided into 4 clusters by means of Pam algorythm. Let's say I want to project the second database ("B") onto the Comp.1 X Comp.2 graph, and see where its elements are placed. The two datasets are made of different dim (54x19 and 28x19). I tried