surCoin(), starting from a data frame, generates a list (an object of class
netCoin) containing the nodes, links, and options resulting from the coincidence analysis. This object can be plotted to generate an interactive graph.
For this example we will use the
ess sample data which is loaded with the package. This data frame contains a simple random sample of 1,000 people with a small subset of the variables from the 8th round of the European Social Survey (ESS) in Europe:
The most simple way to run a coincidence analysis is to use
surCoin() including the data and a vector (
set) with the names of the variables to be used in the analysis. In this case we add
Social participation and
set <- c("Social participation", "Political participation", "Gender", "Age") essCoin <- surCoin(data = ess, variables = set) essCoin #> #> Nodes(12): #> name % variable #> Social participation:No 60.08024 Social participation #> Social participation:Yes 39.91976 Social participation #> Political participation:No 72.21665 Political participation #> Political participation:Yes 27.78335 Political participation #> Gender:Female 53.56068 Gender #> Gender:Male 46.43932 Gender #> ... #> #> Links(24): #> Source Target Haberman p(Z) #> Social participation:No Political participation:No 11.755060 0.00000000 #> Social participation:No Gender:Female 1.837566 0.03321192 #> Social participation:No Age:60-69 2.053822 0.02012663 #> Social participation:No Age:70 and + 1.673124 0.04730833 #> Social participation:Yes Political participation:Yes 11.755060 0.00000000 #> Social participation:Yes Gender:Male 1.837566 0.03321192 #> ...
An interactive plot of the coincidence analysis can be produced using the
plot() function. Note that the output is an html page that will open in the default browser.
For binary variables we may want to represent only one category and hide the counterpart. For instance, the variable about social participation (
Social participation) has two categories and we want just to represent the cases who have participated socially:
surCoin() allows for the use of weights. Also different procedures can be used to assess the strength of the coincidences, the default is
haberman or adjusted residuals. A full list of the measures available can be found in the function specification. In this case we will set the weight to
cweight and ask for three different measures: frequencies (
f), Conditional relative frequencies (
i) and adjusted residuals (
Some aspects of the output can be customised, for example, we may want to use the argument
exogenous to exclude the relationships amongst the categories of a variable or supress those categories without any relation with others with the argument
degreeFilter. In this case we will set gender (
Gender) and age (
Age) as exogenous.
To customise the coincidence analysis you can use any of the
netCoin() arguments. Even more you can use the netCoin function with the previous
essCoin object as input, instead of
variables. For instance, we may want to use the aesthetics color to differentiate the nodes. Each node will take a different fill color if we set the argument
color to the variable
"name". In addition, we can also establish the size of the nodes based on the relative freqencies, to do this the argument size must equal
"%" The variable
name in the nodes dataset refers to the name of each node, a combination of the variable name and the category. You can access the nodes data frame from the surCoin object:
You may want to differentiate the nodes from their degree using an aesthetics like color or shape. To do this we need to write “degree” in the aesthetics, as the column
degree is present automatically in the nodes dataset.
You may want to save the output of
surCoin() or transform the object to be used in igraph.
To save the output we use the argument
dir to set the directory where we want the html page to be stored.