similar to: problem with PCA loading plot

Dear colleagues, I a m a beginner with R and I would like to add labels (i.e. the variable names) on a pca loading plot to determine the most relevant variables. Could you please tell me the way to do this kind of stuff. The command I use to draw the pca loading plot is the following : Plot(molprop.pc$loading[,1] ~ molprop.pc$loading[,2]) Thanks for your help Fred Ooms

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

Hi When I call for the loadings of my plsr using the command, x <- loadings(BHPLS1) my loadings contain variable names rather than numbers. >str(x) loadings [1:94727, 1:10] -0.00113 -0.03001 -0.00059 -0.00734 -0.02969 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:94727] "PCIList1" "PCIList2" "PCIList3" "PCIList4" ... ..$ : chr

Hi I am attempting to do a loadings plot from a plsr object. I have managed to do this using the gasoline data that comes with the pls package. However when I conduct this on my dataset i get the following error message. >plot(BHPLS1, "loadings", comps = 1:2, legendpos = "topleft", labels = "numbers", >xlab = "nm") Error in

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

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

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 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 <-

Hello, I'm using the R package called mvpart, which is about the multivariate regression trees. The function I wrote is: mrt1<- mvpart(coefmat~sChip+sScreen+sMem,data=mixdata, xv="pick", plot.add=TRUE,uniform=TRUE,which=4,all=TRUE,xadj=2,yadj=2,rsq=TRUE,big.pts=TRUE,wgt.ave.pca=TRUE,legend=TRUE,bars=F, pca=TRUE) where "coefmat" is a matrix(of dimension N*K) to store

Hello, I am trying to complete a PCA on a set of standardized ring widths from 8 different sites (T10, T9, T8, T7, T6, T5, T3, and T2). The following is a small portion of my data: T10 T9 T8 T7 T6 T5 T3 T2 1.33738 0.92669 0.91146 0.98922 0.9308 0.88201 0.92287 0.91775 0.82181 1.05319 0.92908 0.97971 0.95165 0.98029 1.14048 0.77803 0.88294 0.96413 0.90893 0.87957 0.9961 0.74926 0.71394 0.70877

Hello everybody, I Would like to get some help to plot together, Principal Components Analysis (PCA) and clusters. I am handling environmental data from 25 locations spread across 5 different ecosystems.When grouped into 5 clusters, locations from different ecosystems are arranged in the same group. So, I want to plot together PCA and Clusters, in a such way that locations belonging to the same

I am trying to run PCA on a matrix (the first column and row are headers). There are several cells with NA's. When I run PCA with the following code: ______________________________________ setwd("I:/PCA") AsianProp<-read.csv("Matrix.csv", sep=",", header=T, row.names=1) attach(AsianProp) AsianProp AsianProp.pca<-princomp(AsianProp, na.omit)

Hi all I have produced a biplot for a PCA (see attached pdf) that I ran however the names of the variables which are placed at the end of the arrows overlap and are thus unreadable. Similarly some of the numbered points overlap. I was wondering if there was a way to edit the biplot to move the label names and if not what the best alternative is. Thanks Anna pca<-biodata[,3:10]

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:

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

Hello all, I'm currently using R to do PCA Analysis, and was wondering if anyone knew the specific R Code that could limit the output of the PCA Analysis so that you only get the Principal Component features as your output and none of the extraneous words or numbers that you don't want. If that was unclear, let me use linear regression as an example: "lm(y~x)" is the normal

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: ....

Dear expe-R-ts, I try some test data for a factorAnalysis (resp. pca) in the sense of Prof. Ripley's MASS ? 11.1, p. 330 ff., just to prepare myself for an analysis of my own empirical data using R (instead of SPSS). 1. the data. ## The test data is (from the book of Backhaus et al.: Multivariate ## Analysemethoden. Springer 2000 [9th ed.], p. 300 ff):

Hi all, I' m trying to figure out if it is appropriate to do a PCA having only categorical data (not ordinal). I have only find the following quote: One method to find such relationships is to select appropriate variables and to view the data using a method like Principle Components Analysis (PCA) [4]. This approach gives us a clear picture of the data using KL-plot of the PCA. However, the

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