similar to: KMO sampling adequacy and SPSS -- partial solution

Hi All, How do i calculate KMO for a dataset? *Dataset:---------------------* m1 m2 m3 m4 m5 m6 m7 m8 1 2 20 20 2 1 4 14 12 2 9 16 3 5 2 5 5 15 3 18 18 18 13 17 9 2 4 4 7 7 2 12 2 11 11 11 5 7 8 5 19 5 2 20 18 6 7 4 7 4 7 9 3 3 7 5 5 5 12 5 13 13 12 8 6 6 4 3 5 17 17 16 9 12 12 4 2 4 4 14 14 10 5 14

Hello, This is in response to a post from a couple of years back regarding Kaiser-Meyer-Olkin Measures of Sampling Adequacy. (http://tolstoy.newcastle.edu.au/R/help/05/12/17233.html) As it turns out, last year Trujillo-Ortiz et al. at the Universidad Autonoma de Baja California wrote and posted a script for MATLAB that does the job. You can see it (with a discussion of KMO statistics) at

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

Hi, I have done different discriminant function analysis of multivariat data. With the CV=True option I was not able to perform the predict() call. What do I have to do? Or is there no possibility at all? You also need the predicted values to produce a plot of the analysis, as far as I know. Here my code: pcor.lda2<-lda(pcor~habarea+hcom+isol+flowcov+herbh+inclin+windprot+shrubcov+baregr,

Hi, I would like to conduct a MANOVA. I know that there 's the manova() funciton and the summary.manova() function to get the appropriate summary of test statistics. I just don't manage to specify my model in the manova() call. How to specify a model with multiple responses and one explanatory factor? If I type:

Hej! I have the following problem: I would like to do partial correlations on non-parametric data. I checked "pcor" (Computes the partial correlation between two variables given a set of other variables) but I do not know how to change to a Spearman Rank Correlation method [pcor(c("BCDNA","ImProd","A365"),var(PCor))] Here''s a glimpse of

Hi All, This is my R-version information:--- > version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 7.1 year 2008 month 06 day 23 svn rev 45970 language R version.string R version 2.7.1 (2008-06-23) While calculating partial

I'm trying to write code to calculate partial correlations (along with p-values). I'm new to R, and I don't know how to do this. I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while

Dear all, I'm writing my manuscript to publish after analysis my final data with ANOVA, ANCOVA, MANCOVA. In a section of my result, I did correlation of my data (2 categirical factors with 2 levels: Quantity & Quality; 2 dependent var: Irid.area & Casa.PC1, and 1 co-var: SL). But as some traits (here Irid.area) are significantly influenced by the covariate (standard length, SL), I

Muy buenas, quiero calcular correlaciones de Pearson entre dos variables (a,b) teniendo en cuenta una tercera (c). Para ello estoy usando una función llamada "pcor.test" (http://www.yilab.gatech.edu/pcor.html), que en realidad no está en ningún paquete de R, que yo sepa. ¿Alguien conoce una función similar en alguna librería de R? Por otro lado, para ver si me cuadraban los resultados,

Hello everyone My name is Vangelis and I want to ask a question about partial correlation. I have used the command "pcor.shrink" to evaluate the partial correlations of a data.frame but the problem is that in the output results I cannot see whether these correlations are significant or not. Is there any command which can show me if these correlations are significant at 95% level or

Hello, This may not be a bug--but I'm not sure. I have R 1.91 installed on a machine that's running Windows XP. In Cygwin, when I attempt to use the "build" command it tells me that TMPDIR is set to an invalid directory. See the results below: stat20:/cygdrive/d/dirk/bands/implementation -> which r /cygdrive/d/Programs/R/rw1091/bin/r

We have 4 class C networks, 199.129.206.* 199.129.207.* 199.129.208.* 199.129.209.* I see IP addresses from all 4 classes thru tcpdump. Some of these computers broadcast as C-class computers; eg, to 199.129.206.255 Some broadcast to 255.255.255.255 Of course, several C class networks would induce me to use a B-class broadcast, 199.129.255.255 This works for the computers I

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

Hello everyone I tried to install the library GeneNT in order to use the command pcor.confint because I want to construct confidence intervals for partial correlations but among other demanding the specific library needs the library "Graph" which I don't have it and I cannot find it at this site. Is there any other site that I can download this library? Thanks Kind regards

Hello, I'm looking for an efficient function for calculating partial correlations. I'm currently using the pcor.test () function, which is equivalent to the cor.test() function, and can receive only single vectors as input. I'm looking for something which is equivalent to the cor() function, and can receive matrixes as input (which should make the calculations much more efficient).

I am trying to optimize a nested function using nlminb. This throws out an error that y is missing. Can someone help me with the correct syntax?? Thank you. test1 <- function(x,y) { sum <- x + y return(sum) } test2 <- function(x,y) { sum <- test1(x,y) sumSq <- sum*sum return(sumSq) } nlminb(start = c(1,1), test2,lower = c(0,0), upper = c(5,5)) -- View this message in

---------- Forwarded message ---------- From: <r-help-bounces@r-project.org> Date: Mon, May 11, 2009 at 10:24 PM Subject: The results of your email commands To: das.moumita.online@gmail.com The results of your email command are provided below. Attached is your original message. - Results: Ignoring non-text/plain MIME parts - Unprocessed: What is the function for partial

Why do we get Partial correlation values greater than 1? I have used the default function pcor.mat :-- I have manipulated the default pcor.mat function a bit so ignore tha variables corr_type,element1_in_no,element2_in_no,P.Please ignore the ?pairwise? section and have a look at athe ?listwise ? part i.e else part. *pcor.mat <-

r-help: I have an R package suggestion. After spending several hours the other day installing about a dozen packages, I had an idea. In xemacs, there is a "sumo" package which allows me to install a large bundle of xemacs packages at one time (about a 120 modes including ESS). I think R should have a similar bundle. It would be so much easier than hunting/downloading/installing.