boot_eip: Bootstrapped Edge Inclusion 'Probabilities'

In GGMncv: Gaussian Graphical Models with Nonconvex Regularization

Description

\loadmathjax

Compute the number of times each edge was selected when performing a non-parametric bootstrap \insertCite@see Figure 6.7, @hastie2009elementsGGMncv.

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 \insertCitehastie2009elements;textualGGMncv suggests this approach provides probabilities, to avoid confusion with Bayesian inference, these are better thought of as "probabilities" (or better yet proportions).

References

\insertAllCited

Examples

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

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

boot_samps

GGMncv documentation built on Dec. 15, 2021, 9:10 a.m.