The latent root
of judgment matrix A is determined as AW = [[lambda].sub.max] W.
Assessment of Eindhoven Classification Model Latent Root
Causes in Accessioning, Specimen Setup, and Gross Examination Processes Code Category Examples Latent Errors Errors That Result From Underlying System Failures Technical: Physical items such as equipment, physical installations, software, materials, labels, and forms TD Design Inadequate design of tissue cassette labelers allow for multiple cases to become mixed up.
For extracting factors we have employed principal components analysis and latent root
For each component the sum of the squared values of these coefficients is referred to as an 'eigenvalue' or 'latent root'.
(16) Another way to put this is to say that the total variance of the system (and thus the sum of the latent roots or eigenvalues) will always be equal to the number of original variables (17) (6 in our case) and so if there were no cross-correlations each component could be expected to have a latent root or eigenvalue of 1.
These methods include ridge regression, partial least squares, and latent root regression (Bertrand et al.
Latent root regression analysis: an alternative to PLS.
The three latent roots of this secular equation are - [a.sub.1] - [a.sub.2], [a.sub.2]N/([a.sub.1] + [a.sub.2])[beta] - b - [gamma], and [a.sub.1]N/([a.sub.1] + [a.sub.2])[beta] - b - [gamma].
Obviously, one of the latent roots of (13) is [[lambda].sup.*.sub.1] = (([a.sub.2] - [a.sub.1])/[a.sub.1]) (b + [gamma]).
Some of the more common latent roots
(Mobley, 1999) that one can cite for premature equipment failure are:
As Lord (1980, p.21) lamented that "there is no generally valid statistical test to determine whether a set of test items is strictly unidimensional." Lord proposed a "rough" procedure, in which the size of the latent roots
of the tetrachoric item intercorrelation matrix are compared to see if there is one dominant factor.
(EIGENVALUES) FOR REAL AND RANDOM DATA Eigenvalues 1 2 3 4 5 6 PCA 7.70 2.53 2.16 1.52 [*] 1.27 1.15 PA 1.68 1.57 1.49 1.42 1.36 1.30 PA+ 1.48 1.41 1.36 (*.)Bold type highlights values leading to decision to retain four components.