boot_eip function

Bootstrapped Edge Inclusion 'Probabilities'

Compute the number of times each edge was selected when performing a non-parametric bootstrap if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_citeOnly(keys="@see Figure 6.7,@hastie2009elements",package="GGMncv", cached_env=.Rdpack.currefs) .

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.

Returns

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

Note

Although if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_citeOnly(keys="hastie2009elements;textual",package="GGMncv",cached_env=.Rdpack.currefs) suggests this approach provides probabilities, to avoid confusion with Bayesian inference, these are better thought of as "probabilities" (or better yet proportions).

Examples

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

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

boot_samps

References

if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_all_ref(.Rdpack.currefs)