OVL: Overlap quantification for LPS object

In LPS: Linear Predictor Score, for Binary Inference from Multiple Continuous Variables

Description

Quantify the overlap between gaussian distributions of the two group scores, to assess model efficiency (best models should not overlap, to prevent from false discovery).

Usage

  OVL(means, sds, cutoff=1e-4, n=1e4)

Arguments

means	Numeric vector of two values, the means of the gaussian distributions.
sds	Numeric vector of two values, the standard deviations of the gaussian distributions.
cutoff	Single numeric value, minimal quantile for integration range definition (distributions will be considered between their cutoff and 1 - cutoff quantiles only). The lesser it is, the more precise the returned value will be.
n	Single integer value, the amount of equi-distant points to use for the computation. The greater it is, the more precise the returned value will be.

Value

Returns the proportion of the overlap between the two gaussian distributions N1 and N2, i.e. min(N1, N2) / (N1 + N2).

Author(s)

Sylvain Mareschal

See Also

LPS-class, LPS, link{dnorm}

Examples

  # Full overlap between identical distributions
  OVL(c(0,0), c(1,1))
  
  # Increasing shift
  OVL(c(0,1), c(1,1))
  OVL(c(0,2), c(1,1))
  OVL(c(0,3), c(1,1))
  OVL(c(0,10), c(1,1))

LPS documentation built on May 29, 2021, 5:07 p.m.