sim.eucscale: Similarity method. Euclidean distance followed by exponential...

In netDx: Network-based patient classifier

Description

Computes Euclidean distance between patients. A scaled exponential similarity kernel is used to determine edge weight. The exponential scaling considers the K nearest neighbours, so that similarities between non-neighbours is set to zero. Alpha is a hyperparameterthat determines decay rate of the exponential. For details, see Wang et al. (2014). Nature Methods 11:333.

Usage

sim.eucscale(dat, K = 20, alpha = 0.5)

Arguments

dat	(data.frame) Patient data; rows are measures, columns are patients.
K	(integer) Number of nearest neighbours to consider (K of KNN)
alpha	(numeric) Scaling factor for exponential similarity kernel. Recommended range between 0.3 and 0.8.

Value

symmetric matrix of size ncol(dat) (number of patients) containing pairwise patient similarities

Examples

data(xpr)
sim <- sim.eucscale(xpr)

netDx documentation built on Dec. 11, 2020, 2:01 a.m.