cond.prob.analysis: Conditional probability of deviation from means

In influential: Identification and Classification of the Most Influential Nodes

Conditional probability of deviation from means

Description

This function calculates the conditional probability of deviation of two centrality measures (or any two other continuous variables) from their corresponding means in opposite directions.

Usage

cond.prob.analysis(data, nodes.colname, Desired.colname, Condition.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).
Desired.colname	The character format (quoted) name of the column containing the values of the desired variable.
Condition.colname	The character format (quoted) name of the column containing the values of the condition variable.

Value

A list of two objects including the conditional probability of deviation of two centrality measures (or any two other continuous variables) from their corresponding means in opposite directions based on both the entire network and the split-half random sample of network nodes.

See Also

Other centrality association assessment functions: double.cent.assess.noRegression(), double.cent.assess()

Examples

## Not run: 
MyData <- centrality.measures
My.conditional.prob <- cond.prob.analysis(data = MyData,
                                          nodes.colname = rownames(MyData),
                                          Desired.colname = "BC",
                                          Condition.colname = "NC")
                                          
## End(Not run)

influential documentation built on May 29, 2024, 11:49 a.m.