Factor Analysis Error Correlation Matrix Singular
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Near Singular Matrix Error Eviews
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Singular Correlation Matrix
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How To Fix Near Singular Matrix
makes a matrix singular and what are implications of singularity or near-singularity? up vote 32 down vote favorite 37 I am doing some calculations on different matrices (mainly in logistic regression) and I commonly get the error "Matrix is singular", where I have to go back and remove the correlated variables. My question here is what would you consider a "highly" correlated matrix? Is there a threshold value of correlation to represent this word? Like if a what does near singular matrix mean in eviews variable was 0.97 correlated to another one, is this a "high" enough to make a matrix singular? Apologies if the question is very basic, I wasn't able to find any references talking about this issue (a hint towards any reference would be a big plus!). regression correlation matrix multicollinearity singular share|improve this question edited Sep 25 '13 at 16:59 ttnphns 25.9k560134 asked Sep 24 '13 at 10:55 Error404 3561415 2 Tip: search our site for VIF and correlation. –whuber♦ Sep 24 '13 at 13:18 Will definitely have a look. Cheers. –Error404 Sep 24 '13 at 13:26 2 @ttnphns has provided an outstanding explanation below (no surprise there, this seems to be his specialty). For a simple example of a situation where you can get a singular data matrix, it might help to read my answer here: qualitative-variable-coding-in-regression-leads-to-singularities. –gung Sep 24 '13 at 13:36 Indeed he did!! Actually saved me hours of reading with confusion. Thanks for your example @gung. That was very helpful guys. –Error404 Sep 24 '13 at 13:45 add a comment| 1 Answer 1 active oldest votes up vote 47 down vote accepted What is singular matrix? A square matrix is singular, that is, its determinant is zero, if it contains rows or columns which are proportionally interrelated; in other words, one or more of its rows (columns) is
Singular correlation matrix problem Tweet Welcome to Talk Stats! Join the discussion today by registering your FREE account. Membership benefits: • Get your questions answered by community gurus and expert researchers. correlation matrix is singular stata • Exchange your learning and research experience among peers and get advice and near singular matrix error. regressors may be perfectly collinear insight. Join Today! + Reply to Thread Results 1 to 6 of 6 Thread: Singular correlation matrix problem Thread Tools singular matrix error in eviews Show Printable Version Email this Page… Subscribe to this Thread… Display Linear Mode Switch to Hybrid Mode Switch to Threaded Mode 08-04-200806:30 AM #1 kobylkinks View Profile View Forum Posts Give Away Points http://stats.stackexchange.com/questions/70899/what-correlation-makes-a-matrix-singular-and-what-are-implications-of-singularit Posts 52 Thanks 0 Thanked 0 Times in 0 Posts Singular correlation matrix problem I'm using principal components to reduce problem dimension. I obtain correlation matrix whose determinant is about 10^(-13) as the result of strong correlated variables. Then I use discriminant analysis for the factor (principal components) space. 1) Is such a small determinant magnitude tolerant or it can cause unstable further predictions ? 2) http://www.talkstats.com/showthread.php/5180-Singular-correlation-matrix-problem If not ok how can I resolve the problem ? Konstantin. Reply With Quote 08-04-200806:42 AM #2 vinux View Profile View Forum Posts Visit Homepage Dark Knight Posts 2,002 Thanks 52 Thanked 235 Times in 199 Posts Originally Posted by kobylkinks I'm using principal components to reduce problem dimension. I obtain correlation matrix whose determinant is about 10^(-13) as the result of strong correlated variables. Then I use discriminant analysis for the factor (principal components) space. 1) Is such a small determinant magnitude tolerant or it can cause unstable further predictions ? 2) If not ok how can I resolve the problem ? Konstantin. You should do an inital study on the variables. Do the following i) remove variable with zero standard deviation( constant value for all records) ii) Remove multicollinearity(high). May be two variables are perfectly correlated, like, one variable may be derived variable of other one. By doing above you can avoid the singularity of correlation matrix. In the long run, we're all dead. Reply With Quote 08-04-200804:03 PM #3 kobylkinks View Profile View Forum Posts Posts 52 Thanks 0 Thanked 0 Times in 0 Posts So what the determinant magnitude should be about t
from my own experience. The Problem There are four situations in which a researcher may get a message about a matrix being http://www2.gsu.edu/~mkteer/npdmatri.html "not positive definite." The four situations can be very different in terms of their causes and cures. First, the researcher may get a message saying that the input covariance or correlation matrix being analyzed is "not positive definite." Generalized least squares (GLS) estimation requires that the covariance or correlation matrix analyzed must be positive definite, and maximum singular matrix likelihood (ML) estimation will also perform poorly in such situations. If the matrix to be analyzed is found to be not positive definite, many programs will simply issue an error message and quit. Second, the message may refer to the asymptotic covariance matrix. This is not the covariance matrix being analyzed, but rather a weight matrix to near singular matrix be used with asymptotically distribution-free / weighted least squares (ADF/WLS) estimation. Third, the researcher may get a message saying that its estimate of Sigma (), the model-implied covariance matrix, is not positive definite. LISREL, for example, will simply quit if it issues this message. Fourth, the program may indicate that some parameter matrix within the model is not positive definite. This attribute is only relevant to parameter matrices that are variance/covariance matrices. In the language of the LISREL program, these include the matrices Theta-delta, Theta-epsilon, Phi () and Psi. Here, however, this "error message" can result from correct specification of the model, so the only problem is convincing the program to stop worrying about it. "Not Positive Definite"--What Does It Mean? Strictly speaking, a matrix is "positive definite" if all of its eigenvalues are positive. Eigenvalues are the elements of a vector, e, which results from the decomposition of a square matrix S as: S = e'Me To an extent, however, we can discuss positive definiteness in terms of
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