confirm_edges function

Confirm Edges

Confirmatory hypothesis testing of edges that were initially detected with data-driven model selection.

confirm_edges(object, Rnew, method, alpha)

Arguments

• object: An object of class ggmncv
• Rnew: Matrix. A correlation matrix of dimensions p by p.
• method: Character string. A correction method for multiple comparison (defaults to fdr). Can be abbreviated. See p.adjust .
• alpha: Numeric. Significance level (defaults to 0.05).

Returns

An object of class ggmncv, including:

• P: Matrix of confirmed edges (partial correlations)
• adj: Matrix of confirmed edges (adjacency)

Details

The basic idea is to merge exploration with confirmation if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_citeOnly(keys="@see for example,@rodriguez_williams_rast_mulder_2020",package="GGMncv",cached_env=.Rdpack.currefs) . This is accomplished by testing those edges (in an independent dataset) that were initially detected via data driven model selection.

Confirmatory hypothesis testing follows the approach described in if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_citeOnly(keys="jankova2015confidence;textual",package="GGMncv",cached_env=.Rdpack.currefs) : (1) graphical lasso is computed with lambda fixed to , (2) the de-sparsified estimator is computed, and then (3) p-values are obtained for the de-sparsified estimator.

Examples

Y <- na.omit(bfi[,1:25])

Y_explore <- Y[1:1000,]

Y_confirm <- Y[1001:nrow(Y),]

fit <- ggmncv(cor(Y_explore),
              n = nrow(Y_explore),
              progress = FALSE)

confirm <- confirm_edges(fit,
                         Rnew = cor(Y_confirm),
                         method = "fdr",
                         alpha = 0.05)

References

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