horns_curve: Horn's Parallel Analysis

In anomalyDetection: Implementation of Augmented Network Log Anomaly Detection Procedures

Description

Computes the average eigenvalues produced by a Monte Carlo simulation that randomly generates a large number of nxp matrices of standard normal deviates.

Usage

horns_curve(data, n, p, nsim = 1000L)

Arguments

data	A matrix or data frame.
n	Integer specifying the number of rows.
p	Integer specifying the number of columns.
nsim	Integer specifying the number of Monte Carlo simulations to run. Default is 1000.

Value

A vector of length p containing the averaged eigenvalues. The values can then be plotted or compared to the true eigenvalues from a dataset for a dimensionality reduction assessment.

References

J. L. Horn, "A rationale and test for the number of factors in factor analysis," Psychometrika, vol. 30, no. 2, pp. 179-185, 1965.

Examples

# Perform Horn's Parallel analysis with matrix n x p dimensions
x <- matrix(rnorm(200 * 10), ncol = 10)
horns_curve(x)
horns_curve(n = 200, p = 10)
plot(horns_curve(x))  # scree plot

Example output

 [1] 1.4074757 1.2782029 1.1791177 1.0959289 1.0206053 0.9481376 0.8780448
 [8] 0.8090176 0.7389641 0.6557594
 [1] 1.4046595 1.2751735 1.1786636 1.0948319 1.0175945 0.9461657 0.8781202
 [8] 0.8102156 0.7399553 0.6566640

anomalyDetection documentation built on March 18, 2018, 1:21 p.m.