Help for package cacc

Title:

Conjunctive Analysis of Case Configurations

Version:

0.1.1

Date:

2024-10-03

Description:

A set of functions to conduct Conjunctive Analysis of Case Configurations (CACC) as described in Miethe, Hart, and Regoeczi (2008) <doi:10.1007/s10940-008-9044-8>, and identify and quantify situational clustering in dominant case configurations as described in Hart (2019) <doi:10.1177/0011128719866123>. 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.

License:

MIT + file LICENSE

Encoding:

UTF-8

RoxygenNote:

7.2.1

URL:

https://github.com/amoneva/cacc

BugReports:

https://github.com/amoneva/cacc/issues

Imports:

dplyr, ggplot2, rlang, stats, tibble, tidyr

Depends:

R (≥ 4.1)

LazyData:

true

NeedsCompilation:

Packaged:

2024-10-03 16:10:21 UTC; amone

Author:

Asier Moneva

[aut, cre], Miriam Esteve [aut], Timothy C. Hart [aut]

Maintainer:

Asier Moneva <amoneva@nscr.nl>

Repository:

CRAN

Date/Publication:

2024-10-03 16:20:02 UTC

Conjunctive Analysis of Case Configurations

Description

Computes a Conjunctive Analysis of Case Configurations (CACC).

Usage

cacc(data, ivs, dv)

Arguments

data

A data frame or a tibble.

ivs

A vector of names of the independent variables, without quotes. Variables must be categorical, either integer, character, or factor.

dv

Name of the dependent variable, without quotes. Variable must be a dichotomous integer or factor with values 0 (absence) and 1 (presence).

Value

Returns a tibble with the CACC matrix.

References

Miethe, T. D., Hart, T. C., & Regoeczi, W. C. (2008). The Conjunctive Analysis of Case Configurations: An Exploratory Method for Discrete Multivariate Analyses of Crime Data. Journal of Quantitative Criminology, 24, 227–241. https://doi.org/10.1007/s10940-008-9044-8

Examples

cacc(
  data = onharassment,
  ivs = c(sex, age, hours, snapchat, instagram, facebook, twitter, name, photos, privacy),
  dv = rep_victim
)
cacc(onharassment, ivs = sex:privacy, dv = rep_victim)

# Syntax with the native R pipe
onharassment |> cacc(ivs = sex:privacy, dv = rep_victim)

Situational Clustering Index

Description

Computes a Situational Clustering Index (SCI) to quantify the magnitude of the clustering of observations among dominant profiles in a cacc_matrix.

Usage

cluster_sci(cacc_matrix)

Arguments

cacc_matrix

A tibble. The output of the cacc function.

Value

Returns a numeric value.

References

Hart, T. C. (2019). Identifying Situational Clustering and Quantifying Its Magnitude in Dominant Case Configurations: New Methods for Conjunctive Analysis. Crime & Delinquency, 66(1), 143-159. https://doi.org/10.1177/0011128719866123

Examples

cluster_sci(cacc(onharassment, ivs = sex:privacy, dv = rep_victim))

Chi-Square Goodness-of-Fit Test

Description

Computes a Chi-Square Goodness-of-Fit Test to determine whether there is statistically significant clustering of observations among dominant profiles in a cacc_matrix.

Usage

cluster_xsq(cacc_matrix)

Arguments

cacc_matrix

A tibble. The output of the cacc function.

Value

Returns a list with the Chi-square results. This is the same object returned by the chisq.test function from the stats package.

References

Examples

cluster_xsq(cacc(onharassment, ivs = sex:privacy, dv = rep_victim))

Main effect

Description

Computes the main effect that a specific value of a variable produces on the outcome probability in a cacc_matrix.

Usage

main_effect(cacc_matrix, iv, value, summary = TRUE)

Arguments

cacc_matrix

A tibble. The output of the cacc function.

iv

A single variable name contained in a cacc_matrix.

value

A single numeric or character value the iv specified can take.

summary

Logical. Defaults to TRUE. Whether or not to return the summary statistics for the main effect.

Value

When summary = TRUE, returns a tibble with summary stats for the main effect. If summary = FALSE, returns a tibble containing a single numeric variable, ranging from 0 to 1, containing the main effects of the value of the selected iv on the probability of outcome.

References

Hart, T. C., Rennison, C. M., & Miethe, T. D. (2017). Identifying Patterns of Situational Clustering and Contextual Variability in Criminological Data: An Overview of Conjunctive Analysis of Case Configurations. Journal of Contemporary Criminal Justice, 33(2), 112–120. https://doi.org/10.1177/1043986216689746

Examples

main_effect(
  cacc_matrix = cacc(onharassment, ivs = sex:privacy, dv = rep_victim),
  iv = age,
  value = "15-17"
)
main_effect(
  cacc_matrix = cacc(onharassment, ivs = sex:privacy, dv = rep_victim),
  iv = age,
  value = "15-17",
  summary = FALSE
)

Profiles of 4174 Spanish students

Description

A dataset containing the demographics, online routines, and self-reported online harassment repeat victimization and offending of 4174 Spanish non-university education students.

Usage

onharassment

Format

A data frame with 4174 rows and 12 variables:

sex: Factor. The students' self-reported sex.
age: Factor. The students' self-reported age
hours: Factor. The students' self-reported number of daily hours spent online.
snapchat: Factor. Whether students report using the social media Snapchat on a daily basis.
instagram: Factor. Whether students report using the social media Instagram on a daily basis.
facebook: Factor. Whether students report using the social media Facebook on a daily basis.
twitter: Factor. Whether students report using the social media Twitter on a daily basis.
name: Factor. Whether students report using their real names on social media.
photos: Factor. Whether students report uploading personal photos to social media.
privacy: Factor. Whether students report restricting their social media access to contacts only.
rep_victim: Factor. Whether students report repeatedly suffering online harassment.
rep_offender: Factor. Whether students report repeatedly committing online harassment.

Source

Moneva, A., Miró-Llinares, F., & Hart, T. C. (2021). Hunter or Prey? Exploring the situational profiles that define repeated online harassment victims and offenders. Deviant Behavior, 42(11), 1366-1381. https://doi.org/10.1080/01639625.2020.1746135

Density Plot for the Main Effect

Description

Plots an annotated boxplot and kernel density estimate to visualize the distribution of the main effect that a specific value of a variable produces on the outcome probability in a cacc_matrix.

Usage

plot_effect(cacc_matrix, iv, value)

Arguments

cacc_matrix

A tibble. The output of the cacc function.

iv

A single variable name contained in a cacc_matrix.

value

A single numeric or character value the iv specified can take.

Value

Returns a ggplot object.

References

Examples

plot_effect(
  cacc_matrix = cacc(onharassment, ivs = sex:privacy, dv = rep_victim),
  iv = age,
  value = "15-17"
)

Lorenz Curve for the Situational Clustering Index

Description

Plots a Lorenz Curve for the Situational Clustering Index (SCI) to visualize the magnitude of the clustering of observations among dominant profiles in a cacc_matrix.

Usage

plot_sci(cacc_matrix)

Arguments

cacc_matrix

A tibble. The output of the cacc function.

Value

Returns a ggplot object.

References

Hart, T. C. (2019). Identifying Situational Clustering and Quantifying Its Magnitude in Dominant Case Configurations: New Methods for Conjunctive Analysis. Crime & Delinquency, 66(1), 143-159.

Examples

plot_sci(cacc_matrix = cacc(onharassment, ivs = sex:privacy, dv = rep_victim))