compareAgDimMethods | R Documentation |

Auer and Gervini developed a Bayesian graphical method to determine
the number *d* of significant principal components; a brief
overview is included in the help for the `AuerGervini`

class. The output of their method is a step function that displays
the maximum a posteriori (MAP) choice of *d* as a step function of
a one-parameter family of prior distributions, and they recommend
choosing the highest "long" step. The functions described here help
automate the process of dividing the step lengths into "long" and
"short" classes.

compareAgDimMethods(object, agfuns)

`object` |
An object of the |

`agfuns` |
A list of functions |

This method simply iterates over the list of functions that implement different algorithms/methods to determine the PC dimension.

Returns an integer vector of te same length as the list of
`agfuns`

, containing the number of significant principal
components computed by each method.

Kevin R. Coombes <krc@silicovore.com>, Min Wang <wang.1807@osu.edu>.

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

`AuerGervini`

, `agDimension`

.

# simulate variances lambda <- rev(sort(diff(sort(c(0, 1, runif(9)))))) # apply the Auer-Gervini method ag <- AuerGervini(lambda, dd=c(3,10)) # try different methods agfuns <- list(twice=agDimTwiceMean, km=agDimKmeans, cpt=agDimCPT) compareAgDimMethods(ag, agfuns)

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