prune: R-squared pruning

In SFSI: Sparse Family and Selection Index

R-squared pruning

Description

Pruning features using an R-squared threshold and maximum distance

Usage

Prune(X, alpha = 0.95, 
      pos = NULL, d.max = NULL, 
      centered = FALSE, scaled = FALSE,
      verbose = FALSE) 

Arguments

X	(numeric matrix) A matrix with observations in rows and features (e.g., SNPs) in columns
alpha	(numeric) R-squared threshold used to determine connected sets
pos	(numeric vector) Optional vector with positions (e.g., bp) of features
d.max	(numeric) Maximum distance that connected sets are apart
centered	TRUE or FALSE whether columns in X are centered with mean zero
scaled	TRUE or FALSE whether columns in X are scaled with unit standard deviation
verbose	TRUE or FALSE to whether show progress

Details

The algorithm identifies sets of connected features as those that share an R² > α and retains only one feature (first appearance) for each set.

The sets can be limited to lie within a distance less or equal to a d.max value.

Value

Returns a list object that contains the elements:

• prune.in: (vector) indices of selected (unconnected) features.

• prune.out: (vector) indices of dropped out features.

Examples

  require(SFSI)
  data(wheatHTP)
  
  index = c(154:156,201:205,306:312,381:387,540:544)
  X = M[,index]          # Subset markers
  colnames(X) = 1:ncol(X)
  
  # See connected sets using R^2=0.8
  R2thr = 0.8
  R2 = cor(X)^2
  nw1 = net(R2, delta=R2thr) 
  plot(nw1, show.names=TRUE)

  # Get pruned features
  res = Prune(X, alpha=R2thr)

  # See selected (unconnected) features
  nw2 = net(R2[res$prune.in,res$prune.in], delta=R2thr) 
  nw2$xy = nw1$xy[res$prune.in,]
  plot(nw2, show.names=TRUE)

SFSI documentation built on Nov. 18, 2023, 9:06 a.m.