search for: screeing

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:

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

Two questions about principal components analysis in R: Q.1) Hogenraad and McKenzie (1999) used Bruce Thompson's FACSTRAP program to bootstrap the factor loadings and scores in a principal components analysis. The input to the analysis was a word-word correlation matrix derived from a frequency count of x words across n texts. This is how they described their procedure: "Finally, we

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,

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

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

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

Dear all I have two 10*10 matrixes and I would like to compare theirs contents. By the word content I mean to check visually (not with any mathematical formulation) how similar are the contents. I also know edit that prints my matrix in the scree but still one edit blocks the prompt to launch a second edit() screen. What is the best way to compare these two matrices? I would like to thank you

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 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 there! To make a good choice for the estimation of the number of usefull factors. And I'd like to plot a graph like: http://de.wikipedia.org/wiki/Bild:Parallelanalyse.jpg where I don't need the green line (random values). But with str(factanal(data, factors=10)) I cannot figure out where I can find the values for plotting. Can you please help me in this question? Thanks, Martin

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 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

Hello R experts, I've used metaMDS to run NMDS on some fish abundance data, and am also working on correlating environmental data to the NMDS coordinates. I'm fairly new to metaMDS and NMDS in general, so I have what are probably some very basic questions. My fish abundance data consists of 66 sites for which up to 20 species of fish were identified and counted. I ran metaMDS on this data

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 users, I m quite a novice in using R for factor analysis and I would need some help to choose the right function. I have a contingency table and I would like to perform a Correspondence analysis on this table, followed by a hirarchical clustering of my variables projected in on the first principal components. Here are my question : - what is the more appropriate function to do so ...

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

I'm trying to figure out how to get out of this situation: > source('monit.r') Error in file(file, "r") : All connections are in use > showConnections() description class mode text isopen can read can write > help.search('connection') Error in file(file, "r") : All connections are in use > q() Save workspace image? [y/n/c]: y Error

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)

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