NodeGeneralCorrelation function

Calculate dependence statistics on the network

It allows to compute different dependence statistics on the network for the given vector and for neighborhoods of distinct order. Such statistics are; correlation, covariance, Moran’s I and Geary’s C.

NodeGeneralCorrelation(
  obj,
  dep_type,
  lag_max,
  intensity,
  partial_neighborhood = TRUE
)

## S3 method for class 'intensitynet'
NodeGeneralCorrelation(
  obj,
  dep_type = c("correlation", "covariance", "moran", "geary"),
  lag_max,
  intensity,
  partial_neighborhood = TRUE
)

Arguments

• obj: intensitynet object
• dep_type: 'correlation', 'covariance', moran', 'geary'. The type of dependence statistic to be computed.
• lag_max: Maximum geodesic lag at which to compute dependence
• intensity: Vector containing the values to calculate the specified dependency in the network. Usually the node mean intensities.
• partial_neighborhood: use partial neighborhood (TRUE) or cumulative (FALSE). TRUE by default

Returns

A vector containing the dependence statistics (ascending from order 0).

Examples

data("und_intnet_chicago")
g <- und_intnet_chicago$graph
gen_corr <- NodeGeneralCorrelation(und_intnet_chicago, dep_type = 'correlation', lag_max = 2, 
                                   intensity = igraph::vertex_attr(g)$intensity)