similar to: Bootstrapping PCA + Standard Error Scree

hello, I've two questions today. 1) I'm trying to do a scree diagram, I did a Google for a specific command I could used to do so. All I could find is a screeplot. Are they the same command? 2) what command can I used to present a PC scores, eigenvectors of the PC scores, and component correlations? thanks! -- View this message in context:

Hello can anyone help, I have been running the following script to obtain a PCA plot but the end result is rather disappointing as the points are very very small and there are no titles etc geochemdata<-read.csv(file.choose(),header=TRUE) names(geochemdata) library(vegan) bstick<-function(n, tot.var=1) rev(cumsum(tot.var/n:1)/n) geopca<-rda(geochemdata, scale=TRUE) geopca

Hi! Is there an easy way to add to the scree-plot labels to each value pertaining to the cumulative proportion of explained variance? Thanks and a happy new year Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com --------------------------------------------------- M-TD Modelling and Technology Development PSE-C

Hi, I am using R to do a principal components analysis for a class which is generally using SPSS - so some of my question relates to SPSS output (and this might not be the right place). I have scoured the mailing list and the web but can't get a feel for this. It is annoying because they will be marking to the SPSS output. Basically I'm getting different values for the component

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:

Hello, I am using function princomp from the package psych. I have my principle component object mypc: mypc <- princomp(covmat=mycor) plot(mypc) # shows me a screeplot Question: how could I actually see the values displayed in the screeplot. I don't mean on the graph - I just want to know the actual value for each component (e.g., 10, 3.2, 1.8, etc.) I need to know how much variance,

My previous post about prcomp and princomp was done in some haste as I had long ago indicated to Kurt that I would try to have this ready for the June release, and it appeared that I would miss yet another release. I also need to get it out before it becomes hopelessly buried by other work. Brian Ripley kindly pointed out some errors, and also pointed out that I was suggesting replacing some

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 All, I want to know if there is some easy and reliable way to estimate the number of dominant eigenvalues when applying PCA on sample covariance matrix. Assume x-axis is the number of eigenvalues (1, 2, ....,n), and y-axis is the corresponding eigenvalues (a1,a2,..., an) arranged in desceding order. So this x-y plot will be a decreasing curve. Someone mentioned using the elbow (knee)

try "sipsak -M -O desktop -B "foo" -s sip:<user>@<registrar> -H <ip of registrar>" the trick is to specify the "-O desktop" parameter + the "-H <ip of registrar>" parameter. Sipsak fakes the host-header of the registrar so that the Snom thinks it is coming from your Asterisk server, then lets the message through to the

I've read through many postings about principle component analysis in the R-help archives, but haven't been able to piece together the information I need. I'd like to recreate an SPSS-like experience of factor analysis using R. Here's what SPSS produces: 1. Scatterplots of all possible variable pairs, with regression lines. xyplot(my.dataframe) is perfect but for the lack of

>>>>> Ross Ihaka writes: > I have got a little side-tracked (from graphics) and am putting > together a little multivariate analysis library. This is just > intended to be a "core" library rather than anything exhaustive. > Mainly it is a matter of putting togther code which already exists at > StatLib. Here is my present list (only some of which is

This patch makes windows zoom in and out of a zero point on create/unmap. I've cleaned up the formatting/style to be consistent with the rest of compiz (eg adding spaces around operators, tabifying, using C style comments). This patch could use some more work. It should only apply to decorated toplevels, but right now it also affects XUL menus (I guess because they are not unmanaged like

Hello! Very often one can hear that MDS usually ends with two-dimensional solution. Of course, there are methods, like Scree-test (proposed by Kruskal and Wish, 1981), to determine optimal number of dimensions. However, I am trying to find references to this two-dimensional gold-standard. Can anyone point me to authors which explicitly states that two-dimensions are typical and easiest to

   Hello, list:    I'm not sure about where to send this question. I have several repeatability calculations, together with their 95% confidence intervals, and I would like to plot them, in a way similar to error bars. I was wondering if there is any specific function to do this, or any method I can apply in R, as I haven't found anything like that in the R book.    Thank you very much

Hi I'm trying to run a R script in a computer grid using the Rscript interpreter, but the Rscript is not returning zero (even when the scripts processes succesfully) on its exit which causes the scheduler to detect an error and not records the output. Is there any way to get the Rscript returning zero ? -- "A critical section of code is like a bathroom. Only one person is allowed

Hello everyone: It's the first time I write to this mailing list. Sorry in advance if my doubt has already been posted before, but I have been checking the archives and I haven't been able to find anything satisfactory. I am running a mixed effects model with nested effects (site and pair, referred to barn swallow nests located in different places in different farms). My dependent

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.

I am a newcomer to R trying to convert a SAS program to R. Does anyone know if there is a functional equivalent of the SAS 'Factor' procedure? For example in SAS: proc factor DATA=cor method=principal rotate=varimax proportion=0.9 scree where 'cor' is a correlation matrix (as in the R 'cor' function) This should get you a list of eigen values as well as a factor

Hi I am trying to explore the use of random forests for regression to identify the important environmental/microclimate variables involved in predicting the abundance of a species in different habitats, there are approx 40 variable and between 200 and 500 data points depending on the dataset. I have successfully used the randomForest package to conduct the analysis and looked at the %IncMSE