This function assesses innate features and the association of two centrality measures (or any two other continuous variables) from the aspect of distribution mode, dependence, linearity, partial-moments based correlation, and conditional probability of deviating from corresponding means in opposite direction (centrality2 is used as the condition variable). This function doesn't consider which variable is dependent and which one is independent and no regression analysis is done. Also, the correlation between two variables is assessed via non-linear non-parametric statistics (NNS). For the conditional probability assessment, the centrality2 variable is considered as the condition variable.

double.cent.assess.noRegression(
data,
nodes.colname,
centrality1.colname,
centrality2.colname
)

## Arguments

data

A data frame containing the values of two continuous variables and the name of observations (nodes).

nodes.colname

The character format (quoted) name of the column containing the name of observations (nodes).

centrality1.colname

The character format (quoted) name of the column containing the values of the Centrality_1 variable.

centrality2.colname

The character format (quoted) name of the column containing the values of the Centrality_2 variable.

## Value

A list of nine objects including: - Summary of the basic statistics of two centrality measures (or any two other continuous variables). - The results of normality assessment of two variable (p-value > 0.05 imply that the variable is normally distributed). - Description of the normality assessment of the centrality1 (first variable). - Description of the normality assessment of the centrality2 (second variable). - The Hoeffding's D Statistic of dependence (ranging from -0.5 to 1). - Description of the dependence significance. - Correlation between variables based on the NNS method. - The last two objects are the conditional probability of deviation of two centrality measures from their corresponding means in opposite directions based on both the entire network and the split-half random sample of network nodes.

ad.test for Anderson-Darling test for normality, hoeffd for Matrix of Hoeffding's D Statistics, and NNS.dep for NNS Dependence

Other centrality association assessment functions: cond.prob.analysis(), double.cent.assess()

## Examples

if (FALSE) {
MyData <- centrality.measures
My.metrics.assessment <- double.cent.assess.noRegression(data = MyData,
nodes.colname = rownames(MyData),
centrality1.colname = "BC",
centrality2.colname = "NC")
}