ISCA (version 0.1.0)

ISCA_random_assignments: ISCA Random Assignments per Subgroup

Description

Function that calculates membership scores for each subgroup and assigns a cluster for a number of random draws.

Usage

ISCA_random_assignments(
  data,
  filter,
  majority_group,
  minority_group,
  cluster_vars,
  fuzzifier = 1.5,
  n_clusters,
  draws = 500
)

Value

The output is a dataframe with all original variables and a new column for every draw, each containing one random assignment. This dataframe is the foundation of the subsequent functions in the ISCA package.

Arguments

data

A dataset containing all relevant variables

filter

Specification of the variable name that contains information on majority / minority status.

majority_group

Specification of the value within the variable specified in the previous filter-argument indicating majority status. This could be either a numeric value or a character string.

minority_group

specification of the value(s) indicating minority status in the filter variable. This could be either a numeric value or a character string. It can be one single minority group or a vector of several minority groups.

cluster_vars

Vector specifying the variables that should be used to create the clusters.

fuzzifier

The fuzzifier is a value larger than 1 determining the extent of overlap between clusters. A value of 1 effectively makes fuzzy c-means equivalent to hard k-means. The default is 1.5.

n_clusters

Specification of the number of clusters to be created.

draws

Specification of the number of probabilistic draws. If not specified, the default is 500.

Examples

data(sim_data)
ISCA_step1 <- ISCA_random_assignments(data=sim_data,
filter=native, majority_group=1, minority_group=c(0), 
fuzzifier = 1.5, n_clusters=4, draws=5, 
cluster_vars= c("female", "age", "education", "income"));