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

Dear colleagues, I've been searching for information on the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (MSA). This statistic is generated in SPSS and is often used to determine if a dataset is "appropriate" for factor analysis -- it's true utility seems quite low, but it seems to come up in stats classes a lot. It did in mine, and a glance through the R-help

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

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 am a beginner when it comes to using R, though fortunately I already know something about statistics. I think factor analysis should be used sparingly, but I occasionally use it. It doesn't seem to me that factanal() provides Kaiser's Measure of Sampling Adequacy, which should be computed for factor problems based on a small number of subjects, though perhaps it is elsewhere. Does anyone

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

?ginv provides 'Modern Applied Statistics with S' (MASS), 3rd, by Venables and Ripley as the sole reference. I happen to have this book (4th ed) on loan from our library, and as far as I can see, ginv is mentioned there twice, and it is *used*, not *explained* in any way. (It is used on p. 148 in the 4th edition.) ginv does not appear in the index of MASS. ginv is an implementation of

I am using the ginv function from MASS and have run across this problem that I do not understand. If I define the matrix A as below, its g-inverse does not satisfy the Moore-Penrose condition A %*% ginv(A) %*% A = A. The matrix A is X'WX in a quadratic regression using some very large dollar values. The much simpler matrix B does satisfy the MP condition. Am I doing something wrong? Is

This patch adds the DT nodes for the uniperif player IP blocks found on STiH407 family silicon. Signed-off-by: Arnaud Pouliquen <arnaud.pouliquen at st.com> Signed-off-by: Peter Griffin <peter.griffin at linaro.org> --- arch/arm/boot/dts/stih407-family.dtsi | 76 +++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) diff --git a/arch/arm/boot/dts/stih407-family.dtsi

### An numeric problem in R ######## ###I have two matrix one is########## A <- matrix(c(21.97844, 250.1960, 2752.033, 29675.88, 316318.4, 3349550, 35336827, 24.89267, 261.4211, 2691.009, 27796.02, 288738.7, 3011839, 31498784, 21.80384, 232.3765, 2460.495, 25992.77, 274001.6, 2883756, 30318645, 39.85801, 392.2341, 3971.349, 40814.22, 423126.2,

On Fri, 26 Aug 2016, Peter Griffin wrote: > This patch adds the DT nodes for the uniperif player > IP blocks found on STiH407 family silicon. > > Signed-off-by: Arnaud Pouliquen <arnaud.pouliquen at st.com> > Signed-off-by: Peter Griffin <peter.griffin at linaro.org> > --- > arch/arm/boot/dts/stih407-family.dtsi | 76 +++++++++++++++++++++++++++++++++++ > 1

On Fri, 26 Aug 2016, Peter Griffin wrote: > This patch adds the DT nodes for the uniperif player > IP blocks found on STiH407 family silicon. > > Signed-off-by: Arnaud Pouliquen <arnaud.pouliquen at st.com> > Signed-off-by: Peter Griffin <peter.griffin at linaro.org> > --- > arch/arm/boot/dts/stih407-family.dtsi | 76 +++++++++++++++++++++++++++++++++++ > 1

Dear All, While inverting a matrix the following error appears on my console: Error in solve.default(my_matrix) : Lapack routine dgesv: system is exactly singular With this respect, I have been replacing the solve() function with ginv(): the Moore-Penrose generalized inverse of a matrix. These are the questions I would like to ask you: 1. Would you also replace solve() with ginv() in

Dear useRs, We are pleased to announce the preliminary release of the IPSUR package. The primary audience was originally envisioned to be upper division undergraduate mathematics/statistics/engineering majors, but other useRs may find this material useful. In a nutshell, this package slightly modifies and adds selected functionality to the R Commander by John Fox. The changes were meant to

Dear useRs, We are pleased to announce the preliminary release of the IPSUR package. The primary audience was originally envisioned to be upper division undergraduate mathematics/statistics/engineering majors, but other useRs may find this material useful. In a nutshell, this package slightly modifies and adds selected functionality to the R Commander by John Fox. The changes were meant to

This patch enables the uniperif players 2 & 3 for b2120 boards and also adds the "simple-audio-card" device node to interconnect the SoC sound device and the codec. Signed-off-by: Arnaud Pouliquen <arnaud.pouliquen at st.com> Signed-off-by: Peter Griffin <peter.griffin at linaro.org> --- arch/arm/boot/dts/stihxxx-b2120.dtsi | 45 ++++++++++++++++++++++++++++++++++++ 1

Why do x<-b%*%ginv(A) and x<-solve(A,b) give different results?. It seems that I am missing some basic feature of matrix indexing. e.g.: A<-matrix(c(0,-4,4,0),nrow=2,ncol=2) b<-c(-16,0) x<-b%*%ginv(A);x x<-solve(A,b);x Thanks in advance, Angel

Hello to everybody, I have a quadratic programming problem that I am trying to solve by various methods. One of them is to use the quadprog package in R. When I check positive definiteness of the D matrix, I get that one of the eigenvalues is negative of order 10^(-8). All the others are positive. When I set this particular eigenvalue to 0.0 and I recheck the eigenvalues in R, the last

Thanks to Jay Kerns and Jim Lemon for their help. It's exactly what I wanted and "aggregate" is very efficient even with a huge data. Thanks "G. Jay Kerns" <gkerns@ysu.edu> a écrit : Hi Louis, If I am understanding your question correctly, here is one way: suppose your matrix is M of dimension n x k. D <- data.frame(M) # convert to data frame ones <- rep(1,

since ginv can deal with both singular and non-singular conditions, is there any other difference between them? if I use ginv only, will be any problem? thanks [[alternative HTML version deleted]]

Dear all; I'm kind of confused with the results obtained using the ginv function from package MASS and pinv function from Matlab. Accroding to the documentation both functions performs a Moore-Penrose generalized inverse of a matrix X. The problem is when I change the tolerance value, say to 1E-3. Here is some output from ginv 195.2674402 235.6758714 335.0830253 8.977515484 -291.7798965