cacc
:
Conjunctive Analysis of Case ConfigurationsAn R Package to compute Conjunctive Analysis of Case Configurations (CACC), Situational Clustering Tests, and Main Effects
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.
You can install the development version of cacc from GitHub with:
# Check if the`devtools` package needs to be installed
if (!require("devtools")) install.package("devtools")
# Install the {cacc} package from GitHub
::install_github("amoneva/cacc") devtools
# 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
|> glimpse()
onharassment #> 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, …
# Calculate the CACC matrix
<- onharassment |>
cacc_matrix 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
|> head()
cacc_matrix #> # 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
# Compute a Chi-Square Goodness-of-Fit Test
|> cluster_xsq()
cacc_matrix #>
#> 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)
|> cluster_sci()
cacc_matrix #> [1] 0.4505963
# Plot a Lorenz Curve to visualize the SCI
|> plot_sci() cacc_matrix
# 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"
)