similar to: Scree diagram,

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

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,

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

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:

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, Suppose I've got a data set that I found the eigenvalues and eigenvectors. Then I want to draw a screeplot for the eigenvalues. However it returns: Error in matrix(w.m, nc = NC) : negative extents to matrix Then I tried a plot() function on the eigenvalues of the data set, and I can successfully draw it! And the output looks like what the screeplot would show to me. Is there any

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

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

Full_Name: Gavin Simpson Version: 2.5.0 OS: Linux (FC5) Submission from: (NULL) (128.40.33.76) Screeplots are a common plot-type used to interpret the results of various ordination methods and other techniques. A number of packages include ordination techniques not included in a standard R installation. screeplot() works for princomp and prcomp objects, but not for these other techniques as it

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)

I'm trying to make a screeplot including the Cumulative Proportion of the Variance, something that can easily be done in S-Plus with 'screeplot(pc.object,cumulative=T)'. How can I access the Proportion of Variance in an princomp object and how could I get the Cumulative Proportion of the Variance on the screeplot? Many thanks in advance, Jan:-) --

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 people, I want to make a biased dice using the sample() function and print out the results after n number of runs, I've successfully generated the dice using the following command: mydie2<-function(n=1000,y=NULL,...){ for(i in 1:n){ x<-sample(1:6,n,replace=TRUE,prob=c(1,1,2,3,2,1)/10) x=runif(n) if(x<=1/10){y[i]=1} else if(x<=2/10){y[i]=2} else if(x<=4/10){y[i]=3} else

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

Dear all, I'm using R 2.8.1 under Vista. I programmed a Simulation with the code enclosed at the end of the eMail. After the simulation I want to analyse the columns of the single simulation-runs, i.e. e.g. Simulation[[1]][,1] sth. like that but I cannot address these columns... Can anybody please help? Best, Thomas ############################ CODE ############################

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

Hi, I?ve written a script to run several multivariate statistical analysis automatically. As one result a biplot and screeplot is produced. Now I?d like to display the name of the inputdatset as part of the title of these graphics and I do not want to enter it each time I run the script. How can I extract the name of a dataset? An (shortened) extraction from the script: >Test1 <-

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