boot_eip {GGMncv}

Bootstrapped Edge Inclusion 'Probabilities'

Description

Compute the number of times each edge was selected when performing a non-parametric bootstrap (see Figure 6.7, Hastie et al. 2009).

Usage

boot_eip(Y, method = "pearson", samples = 500, progress = TRUE, ...)

Arguments

Y

A matrix of dimensions n by p.

method

Character string. Which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman".

samples

Numeric. How many bootstrap samples (defaults to 500)?

progress

Logical. Should a progress bar be included (defaults to TRUE)?

...

Additional arguments passed to ggmncv.

Value

An object of class eip that includes the "probabilities" in a data frame.

Note

Although Hastie et al. (2009) suggests this approach provides probabilities, to avoid confusion with Bayesian inference, these are better thought of as "probabilities" (or better yet proportions).

References

Hastie T, Tibshirani R, Friedman J (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science \& Business Media.

Examples

# data (ptsd symptoms)
Y <- GGMncv::ptsd[,1:10]

# compute eip's
boot_samps <- boot_eip(Y, samples  = 100, progress = FALSE)

boot_samps