knitr::opts_chunk$set(out.width = "100%", cache = FALSE )
Occurring cliques in association graphs represent connected components of dependent variables, and by comparing the graphs for different thresholds, specific structural models of multivariate dependence can be suggested and tested. The function div_gof()
allows such hypothesis tests for pairwise independence of $X$ and $Y$: $X \bot Y$, and pairwise independence conditional a third variable $Z$: $X\bot Y|Z$.
library(netropy)
For the running example using
data(lawdata) adj.advice <- lawdata[[1]] adj.friend <- lawdata[[2]] adj.cowork <-lawdata[[3]] df.att <- lawdata[[4]]
att.var <- data.frame( status = df.att$status-1, gender = df.att$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$lawschool-1 ) dyad.status <- get_dyad_var(att.var$status, type = 'att') dyad.gender <- get_dyad_var(att.var$gender, type = 'att') dyad.office <- get_dyad_var(att.var$office, type = 'att') dyad.years <- get_dyad_var(att.var$years, type = 'att') dyad.age <- get_dyad_var(att.var$age, type = 'att') dyad.practice <- get_dyad_var(att.var$practice, type = 'att') dyad.lawschool <- get_dyad_var(att.var$lawschool, type = 'att') dyad.cwk <- get_dyad_var(adj.cowork, type = 'tie') dyad.adv <- get_dyad_var(adj.advice, type = 'tie') dyad.frn <- get_dyad_var(adj.friend, type = 'tie') dyad.var <- data.frame(cbind(status = dyad.status$var, gender = dyad.gender$var, office = dyad.office$var, years = dyad.years$var, age = dyad.age$var, practice = dyad.practice$var, lawschool = dyad.lawschool$var, cowork = dyad.cwk$var, advice = dyad.adv$var, friend = dyad.frn$var) )
head(dyad.var)
To test friend
$\bot$ cowork
$|$advice
, that is whether dyad variable friend
is independent of cowork
given advice
we use the function as shown below:
div_gof(dat = dyad.var, var1 = "friend", var2 = "cowork", var_cond = "advice")
Not specifying argument var_cond
would instead test friend
$\bot$cowork
without any conditioning.
Frank, O., & Shafie, T. (2016). Multivariate entropy analysis of network data. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 129(1), 45-63. link
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.