README

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cacc: Conjunctive Analysis of Case Configurations

An R Package to compute Conjunctive Analysis of Case Configurations (CACC), Situational Clustering Tests, and Main Effects

Overview

This package contains a set of functions to conduct Conjunctive Analysis of Case Configurations (CACC) (Miethe, Hart & Regoeczi, 2008), to identify and quantify situational clustering in dominant case configurations (Hart, 2019), and to determine the main effects of specific variable values on the probabilities of outcome (Hart, Rennison & Miethe, 2017). Initially conceived as an exploratory technique for multivariate analysis of categorical data, CACC has developed to include formal statistical tests that can be applied in a wide variety of contexts. This technique allows examining composite profiles of different units of analysis in an alternative way to variable-oriented methods.

Installation

# Check if the`devtools` package needs to be installed
if (!require("devtools")) install.package("devtools")

# Install the {cacc} package from GitHub
devtools::install_github("amoneva/cacc")

Examples

# Load {cacc} and the {tidyverse}
library(cacc)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
#> ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
#> ✔ tibble  3.1.8     ✔ dplyr   1.0.9
#> ✔ tidyr   1.2.0     ✔ stringr 1.4.1
#> ✔ readr   2.1.2     ✔ forcats 0.5.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()

# Explore the dataset
onharassment |> glimpse()
#> Rows: 4,174
#> Columns: 12
#> $ sex          <fct> male, male, male, female, female, female, male, female, m…
#> $ age          <fct> 15-17, 18-21, 18-21, 18-21, 18-21, 15-17, 12-14, 12-14, 1…
#> $ hours        <fct> 4-7, 4-7, 4-7, 4-7, 4-7, 4-7, <4, 4-7, 4-7, 4-7, <4, <4, …
#> $ snapchat     <fct> yes, no, no, yes, no, yes, yes, yes, no, no, no, no, no, …
#> $ instagram    <fct> yes, yes, yes, yes, yes, yes, yes, yes, yes, yes, no, yes…
#> $ facebook     <fct> no, no, no, yes, no, no, no, no, no, no, no, no, no, no, …
#> $ twitter      <fct> yes, yes, no, yes, no, no, no, no, no, yes, no, no, no, n…
#> $ name         <fct> no, yes, no, no, yes, yes, yes, yes, yes, yes, no, yes, n…
#> $ photos       <fct> no, no, no, no, no, yes, yes, yes, yes, yes, no, no, no, …
#> $ privacy      <fct> no, no, no, no, no, no, no, no, no, no, no, no, no, no, n…
#> $ rep_victim   <fct> 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, …
#> $ rep_offender <fct> 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, …

CACC

# Calculate the CACC matrix
cacc_matrix <- onharassment |> 
  cacc(
    ivs = sex:privacy, 
    dv = rep_victim
  )
#> Joining, by = c("sex", "age", "hours", "snapchat", "instagram", "facebook",
#> "twitter", "name", "photos", "privacy")

# Look at the first few rows
cacc_matrix |> head()
#> # A tibble: 6 × 12
#>   sex    age   hours snapchat insta…¹ faceb…² twitter name  photos privacy  freq
#>   <fct>  <fct> <fct> <fct>    <fct>   <fct>   <fct>   <fct> <fct>  <fct>   <int>
#> 1 female 15-17 4-7   yes      yes     no      no      yes   yes    no         11
#> 2 female 12-14 <4    no       yes     no      no      yes   yes    yes        10
#> 3 female 15-17 4-7   no       yes     no      no      yes   yes    no         16
#> 4 female 15-17 4-7   no       yes     no      yes     yes   no     no         10
#> 5 female 18-21 4-7   no       yes     no      no      no    no     yes        10
#> 6 female 18-21 4-7   no       yes     yes     yes     no    no     yes        10
#> # … with 1 more variable: p <dbl>, and abbreviated variable names ¹instagram,
#> #   ²facebook

Situational Clustering Tests

# Compute a Chi-Square Goodness-of-Fit Test
cacc_matrix |> cluster_xsq()
#> 
#>  Chi-squared test for given probabilities
#> 
#> data:  obs
#> X-squared = 3378.2, df = 93, p-value < 2.2e-16

# Compute a Situational Clustering Index (SCI)
cacc_matrix |> cluster_sci()
#> [1] 0.4505963

# Plot a Lorenz Curve to visualize the SCI
cacc_matrix |> plot_sci()

Main Effects

# Compute the main effects for a specific variable value
cacc_matrix |> 
  main_effect(
    iv = sex,
    value = "female",
    # Set to `FALSE` for a numeric vector of effects
    summary = TRUE
  )
#> # A tibble: 1 × 5
#>   median  mean    sd    min   max
#>    <dbl> <dbl> <dbl>  <dbl> <dbl>
#> 1  0.037 0.041 0.117 -0.188 0.278

# Plot the distribution of the main effect
cacc_matrix |> 
  plot_effect(
    iv = sex,
    value = "female"
  )

References

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

cacc: Conjunctive Analysis of Case Configurations