similar to: highly biased PCA data?

If I perform PCA on the 'eurodist' data, should I get an accurate geographic layout of the cities with biplot? (barring inversions, i.e. their is no way to define north.. but you get the idea...) I have a complex distance matrix, and I am thinking about how to cluster it and how to visualize the quality of the resulting clusters. If I could 'see' the clusters in space I could

Hi Listers, I am trying to reflect a PCA biplot in the x-axis (i.e. PC1) but am not having much success. In theory I believe all I need to do is multiply the site and species scores for the PC1 by -1, which would effectively flip the biplot. I am creating a blank plot using the plot command and accessing the results from a call to rda. I then use the calls to scores to obtain separate site and

Hi, I am doing PCA on several columns of data in a data.frame. I am interested in particular rows of data which may have a particular combination of 'types' of column values (without any pre-conception of what they may be). I do the following... # My data table. allDat <- read.table("big_select_thresh_5", header=1) # Where some rows look like this... # PDB SUNID1

Hi Listers, This has a simple answer but it has been eluding me nonetheless. I have been building a PCA plot from scratch with the ability to plot predefined groups in different colors. This has worked fine but when I try to get a polygon drawn around each of the groups it is not recognising my colour file correctly and is only printing the first colour in the file....code is below

First of all I should say this email is more of a general statistics questions rather than being specific to using R but I'm hoping that this may be of general interest. I have a dataset that I would really like to use PCA on and have been using the package pcaMethods to examine my data. The results using traditional PCA come out really nicely. The dataset is comprised of a set of questions

My objective is to look at differences in two species of fish from morphometric measurements. My morphometric measurements are head length, eye diameter, snout length, and measurements from tail to each fin. I want to use discrimanant function analyis to determine if there are differences between the two species. I am familiar with R but new to discrimannt function analysis. I want to learn

Hi, I hope this is not too trivial, but I've had this recurring problem and I think there is super easy solution, just not sure what it is. Please see short example below. ?I would like to get the frequency (counts) of all the variables in a single column (that is easy), but I would also like to return the value 0 for the absence of variables defined in another column. For example: animals

Dear list, I want to perform a logistic regression analysis with multiple categorical predictors (i.e., a logit) on some data where there is a very definite relationship between one predicator and the response/independent variable. The problem I have is that in such a case the p value goes very high (while I as a naive newbie would expect it to crash towards 0). I'll illustrate my problem

I''m new to rails and ruby, and after trying to get this to work correctly for hours, I thought I''d give this forum an try. I''ve setup a trivial project just to mess around with Rails database naming conventions and associations. I''ve got a table called Pets, and a table called Types. Pets contains a list of pets, Parky the cat, Daisy the dog, etc, And Types

Hello All, The basic premise of what I want to do is the following: I have 20 "entities" for which I have ~500 measurements each. So, I have a matrix of 20 rows by ~500 columns. The 20 entities fall into two classes: "good" and "bad." I eventually would like to derive a model that would then be able to classify new entities as being in "good

hi, i have two questions: (1) lookup: given a list of 'strings' in a list, i want to know the index of a given string in the list. if the string is not in the list, the index can be 0 or length()+1. for example, suppose i have names <- c("dog", "cat", "pig", "fish"); then i want lookup(names, "cat") to return 2 and lookup(names,

Dear all, I have read various descriptions of employing resampling techniques, such as the bootstrap, to estimate the uncertainties of the eigenvectors computed by PCA. When I try

Hi All, I have been trying to get to plot my PCA legend outside of the PCA plot, but success still alludes me. Can you guys please advise how I can achieve this. I used locater() to obtain coordinates for below the Comp.1 axis. Using these coordinates the legend disappears. Below is the code for the PCA and legend. Thanks in advance for the help. Regards Tinus r.cols <-

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 users, i would like to apply a PCA on image data for data reduction. The image data is available as three matrices for the RGB values. At the moment i use x <- data.frame(R,G,B)#convert image data to data frame pca<-princomp(x,retx = TRUE) This is working so far. >From this results then i want to create a new matrix from the first (second..) principal component. Here i stuck.

Hi all, I am doing bootstrap on a distance matrix, in which samples have been drawn with replacement. After that I do PCA on a resulted matrix, and these 2 steps are repeated 1000 times. pca(x) is a vector where I wanted to store all 1000 PCAs; and x is from 1 to 1000 SampleD is a new matrix after resampling; I am getting the following error message, which I don't understand: ....

After doing a PCA using princomp, how do you view how much each component contributes to variance in the dataset. I'm still quite new to the theory of PCA - I have a little idea about eigenvectors and eigenvalues (these determine the variance explained?). Are the eigenvalues related to loadings in R? Thanks, Paul -- View this message in context:

Dear R-helpers, Using the package Lattice, I performed a PCA. For example pca.summary <- summary(pc.cr <- princomp(USArrests, cor = TRUE)) The Output of "pca.summary" looks as follows: Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604

Hi, I have to construct an ellipse interval region on a PCAbiplot, I have my parameters "a" and "b" and I would apply the formula: draw.ellipse(x, y, a = , b = ) I have done a PCA on my data so I have my scores and loading for the first and second component, but my answer is what I have to choose as X and Y into the formula? if "a" and "b" are scalars or

Hi There, I'm attempting to plot 10 values on a three-dimensional PCA with text labels next to each point. While i have no trouble doing this on 2D plots using the 'text' or 'textxy' function, I cannot find a function to do this on a 3D plot. I am using princomp for my PCA: >PCA<-princomp(eucdata, cor=TRUE) >PCA$scores [,1:3] # the three principal components i