rndLambdaF: Principal Component Statistics Based on Randomization

In PCDimension: Finding the Number of Significant Principal Components

Principal Component Statistics Based on Randomization

Description

Implements randomization-based procedures to estimate the number of principal components.

Usage

rndLambdaF(data, B = 1000, alpha = 0.05)

Arguments

data	A numeric data matrix.
B	An integer; the number of times to scramble the data columns.
alpha	A real number between 0 and 1; the significance level.

Details

The randomization procedures implemented here were first developed by ter Brack [1,2]. In a simulation study, Peres-Neto and colleagues concluded that these methods were among the best [3]. Our own simulations on larger data matrices find that rnd-Lambda performs well (comparably to Auer-Gervini, though slower), but that rnd-F works poorly.

The test procedure is: (1) randomize the values with all the attribute columns of the data matrix; (2) perform PCA on the scrambled data matrix; and (3) compute the test statistics. All three steps are repeated a total of (B - 1) times, where B is large enough to guarantee accuracy when estimating p-values; in practice, B is usually set to 1000. In each randomization, two test statistics are computed: (1) the eigenvalue λ_k for the k-th principal component; and (2) a pseudo F-ratio computed as λ_k / ∑_{i=k+1}^n λ_i. Finally, the p-value for each k and each statistic of interest is estimated to be the proportion of the test statistics in all data sets that are greater than or equal to the one in the observed data matrix.

Value

A named vector of length two, containing the predicted number of principal components based on the rnd-Lambda and rnd-F statistics.

Author(s)

Kevin R. Coombes <[e-mail]>, Min Wang <[e-mail]>.

References

[1] ter Braak CFJ. CANOCO – a Fortran program for canonical community ordination by [partial] [detrended] [canonical] correspondence analysis, principal component analysis and redundancy analysis (version 2.1). Agricultural Mathematics Group, Report LWA-88- 02, Wageningen, 1988.

[2] ter Braak CFJ. Update notes: CANOCO (version 3.1). Agricultural Mathematics Group, Wageningen, 1990.

[3] Peres-Neto PR, Jackson DA and Somers KM. How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Computational Statistics and Data Analysis 2005; 49: 974–997.

See Also

AuerGervini-class

Examples

dataset <- matrix(rnorm(200*15, 6), ncol=15)
rndLambdaF(dataset)

PCDimension documentation built on July 1, 2022, 1:06 a.m.