The Broken Stick model is one proposed method for estimating the number of statistically significant principal components.
brokenStick(k, n) bsDimension(lambda, FUZZ = 0.005)
An integer between 1 and
An integer; the total number of principal components.
The set of variances from each component from a principal
components analysis. These are assumed to be already sorted in
decreasing order. You can also supply a
A real number; anything smaller than
The Broken Stick model is one proposed method for estimating the number of statistically significant principal components. The idea is to model N variances by taking a stick of unit length and breaking it into N pieces by randomly (and simultaneously) selecting break points from a uniform distribution.
brokenStick function returns, as a real number, the
expected value of the
k-th longest piece when breaking a
stick of length one into
n total pieces. Most commonly used
via the idiom
brokenStick(1:N, N) to get the entire vector of
lengths at one time.
bsDimension function returns an integer, the number of
significant components under this model. This is computed by finding
the last point at which the observed variance is bugger than the
expected value under the broken stick model by at least
Kevin R. Coombes <firstname.lastname@example.org>
Jackson, D. A. (1993). Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology 74, 2204–2214.
Legendre, P. and Legendre, L. (1998) Numerical Ecology. 2nd English ed. Elsevier.
Better methods to address this question are based on the Auer-Gervini
brokenStick(1:10, 10) sum( brokenStick(1:10, 10) ) fakeVar <- c(30, 20, 8, 4, 3, 2, 1) bsDimension(fakeVar)
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