AuerGervini-class {PCDimension}

Estimating Number of Principal Components Using the Auer-Gervini Method

Description

Auer and Gervini [1] described a graphical Bayesian method for estimating the number of statistically significant principal components. We have implemented their method in the AuerGervini class, and enhanced it by automating the final selection.

Usage

AuerGervini(Lambda, dd=NULL, epsilon = 2e-16)
agDimension(object, agfun=agDimTwiceMean)

Arguments

Lambda

Either a SamplePCA object or a numerical vector of variances from a principal components analysis.

dd

A vector of length 2 containing the dimensions of the data used to created the Auer-Gervini object. If Lambda is a SamplePCA object, then the dimensions are taken from it, ignoring the dd argument.

epsilon

A numeric value. Used to remove any variances that are less than epsilon; defaults to 2e-16. Should only be needed in rare cases where negative variances show up because of round-off error.

object

An object of the AuerGervini class.

agfun

A function that takes one argument (a vector of step lengths) and returns a logical vector of the same length (where true indicates "long" as opposed to "short" steps).

Details

The Auer-Gervini method for determining the number of principal components is based on a Bayesian model that assaerts that the vector of explained variances (eigenvalues) should have the form

a_1 \le a_2 \le \dots \le a_d < a_{d+1} = a_{d+2} = \dots a_n

with the goal being to find the true dimension d. They consider a set of prior distributions on d \in \{1, \dots, n\} that decay exponentially, with the rate of decay controlled by a parameter \theta. For each value of \theta, one selects the value of d that has the maximum a posteriori (MAP) probability. Auer and Gervini show that the dimensions selected by this procedure write d as a non-increasing step function of \theta. The values of \theta where the steps change are stored in the changePoints slot, and the corresponding d-values are stored in the dLevels slot.

Auer and Gervini go on to advise using their method as a graphical approach, manually (or visually?) selecting the highest step that is "long". Our implementation provides several different algorithms for automatically deciding what is "long" enough. The simplest (but fairly naive) approach is to take anything that is longer than twice the mean; other algorithms are described in agDimFunction.

Value

The AuerGervini function constructs and returns an object of the AuerGervini class.

The agDimension function computes the number of significant principal components. The general idea is that one starts by computing the length of each step in the Auer-Gerivni plot, and must then separate these into "long" and "short" classes. We provide a variety of different algorithms to carry out this process; the default algorithm in the function agDimTwiceMean defines a step as "long" if it more than twice the mean step length.

Objects from the Class

Objects should be created using the AuerGervini constructor.

Slots

Lambda:

A numeric vector containing the explained variances in decreasing order.

dimensions

Numeric vector of length 2 containing the dimnesions of the underlying data matrix.

dLevels:

Object of class numeric; see details

changePoints:

Object of class numeric; see details

Methods

plot

signature(x = "AuerGervini", y = "missing"): ...

summary

signature(object = "AuerGervini"): ...

Author(s)

Kevin R. Coombes <[e-mail]>

References

[1] P Auer, D Gervini. Choosing principal components: a new graphical method based on Bayesian model selection. Communications in Statistics-Simulation and Computation 37 (5), 962-977.

[2] Wang M, Kornbla SM, Coombes KR. Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components. Preprint: bioRxiv, 2017. <doi://10.1101/237883>.

See Also

agDimFunction to get a complete list of the functions implementing different algorithms to separate the step lengths into two classes.

Examples

showClass("AuerGervini")
# simulate variances
lambda <- rev(sort(diff(sort(c(0, 1, runif(9))))))
# apply the Auer-Gervini method
ag <- AuerGervini(lambda, dd=c(3,10))
# Review the results
summary(ag)
agDimension(ag)
agDimension(ag, agDimKmeans)
# Look at the results graphically
plot(ag, agfun=list(agDimTwiceMean, agDimKmeans))