backprojectFCnet: Back-projection of regression coefficients onto the original...
In EBlini/FCnet: Analysis of Functional Connectivity matrices through elastic NETs

backprojectFCnet

R Documentation

Back-projection of regression coefficients onto the original space

Description

This function projects - based on Loadings as provided by reduce_features_FC() - regression coefficients onto the original space (i.e. a square matrix or a volume). Coefficients can either be passed as a list (as created, for example, by FCnetLOO()), as a data.frame, or as a user-defined vector. If a single value for the coefficients is provided, then the function understands that the user wants to retrieve the edges' loadings for a specific coefficient, i.e. (possibly) a network. The function returns a square matrix or a 3-D array whose entries are the linear combination of the coefficients and the edge's loadings. Optional thresholding can be passed to the threshold parameter: an integer value must be specified in order to retrieve the k largest coefficients, in absolute value, only.

Usage

backprojectFCnet(
  coeffs,
  reduce_features_object,
  normthresh = TRUE,
  threshold = NULL
)

Arguments

`coeffs`	Coefficients can either be passed as a list (as created, for example, by `FCnetLOO()`), as a data.frame, or as a user-defined vector. If you fetch a list or a data.frame, then all the coefficients will be backprojected: you may want to select those that are significant following a permutation test instead (see `select_coefficients()`). If a single value for the coefficients is provided, then the function understands that the user wants to retrieve the edges' loadings for a specific coefficients, i.e. (possibly) a network.
`reduce_features_object`	An object created by `reduce_features_FC()`. If present, Loadings may not be provided, and is overwritten.
`normthresh`	If TRUE first back-projected coefficients are normalized within the -1 - 1 interval, and then only the values larger than 0.1 (in absolute value) are retained. Default is TRUE.
`threshold`	Optional. Prune the back-projection matrix by retaining only the threshold larger entries (in absolute value) if normthresh is FALSE. This is disregarded if normthresh is TRUE.