netropy (version 0.2.0)

assoc_graph: Association Graphs

Description

Draws association graphs (graphical models) based on joint entropy values to detect and visualize different dependence structures among the variables in the dataframe.

Usage

assoc_graph(dat, cutoff = 0)

Value

A ggraph object with nodes representing all variables in dat and edges representing (the strength of) associations between them based on joint entropies.

Arguments

dat

dataframe with rows as observations and columns as variables. Variables must all be observed or transformed categorical with finite range spaces.

cutoff

the cutoff point for the edges to be drawn based on joint entropies. Default is 0 and draws all edges.

Author

Termeh Shafie

Details

Draws association graphs based on given thresholds of joint entropy values between pairs of variables represented as nodes. Thickness of edges between pairs of nodes/variables indicates the strength of dependence between them. Isolated nodes are completely independent and paths through certain nodes/variables indicate conditional dependencies.

References

Frank, O., & Shafie, T. (2016). Multivariate entropy analysis of network data. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 129(1), 45-63.

See Also

joint_entropy

Examples

library(ggraph)
# use internal data set
data(lawdata)
df.att <- lawdata[[4]]

# three steps of data editing:
# 1. categorize variables 'years' and 'age' based on
# approximately three equally size groups (values based on cdf)
# 2. make sure all outcomes start from the value 0 (optional)
# 3. remove variable 'senior' as it consists of only unique values (thus redundant)
df.att.ed <- data.frame(
    status = df.att$status,
    gender = df.att$gender,
    office = df.att$office - 1,
    years = ifelse(df.att$years <= 3, 0,
        ifelse(df.att$years <= 13, 1, 2)
    ),
    age = ifelse(df.att$age <= 35, 0,
        ifelse(df.att$age <= 45, 1, 2)
    ),
    practice = df.att$practice,
    lawschool = df.att$lawschool - 1
)

# association graph based on cutoff 0.15
assoc_graph(df.att.ed, 0.15)