SimMeasure: Generation of Similarity Matrix from Sparse/ Low Information...

In NetComp: Network Generation and Comparison

Description

Returns a adjacency matrix with the similarity scores between individuals. If threshold is provided, values where the absolute value of the observation is less than the threshold are not considered. The similarity measure is based on the percent difference between the observations. Details of the algorithm can be found in the accompanying paper (see references).

Usage

SimMeasure(data,  threshold=NULL, ...)

Arguments

data	Matrix object containing observation data on which to calculate the similarity score.
threshold	The threshold value. Responses less than this value (absolute value considered)are not used in calculating the similarity score.
...	Other parameters.

Details

Data can contain NA but may not contain NULL values. This method is designed for datasets with high numbers of missing or uninformative values that can be removed by setting the threshold value. Note that the threshold value must be the same for all numbers.

Value

SimMeasure returns an adjacency matrix containing edges corresponding to the similarity of the observed values.

Author(s)

Shannon M. Bell

Examples

 #using the state.x77
 sim<-SimMeasure(t(state.x77), threshold=NULL)
 sim[1:5,1:15]

NetComp documentation built on May 2, 2019, 8:15 a.m.