circumplex

The goal of circumplex is to provide a powerful, flexible, and user-friendly way to analyze and visualize circumplex data. It was created and is maintained by Jeffrey Girard; it was inspired by work from and was developed under advisement from Johannes Zimmermann and Aidan Wright. You can learn more about using this package through the vignette articles available on the package website or through ?circumplex.

Installation

# Install release version from CRAN
install.packages("circumplex")

# Install development version from GitHub
devtools::install_github("jmgirard/circumplex")

Usage

Example 1

data("jz2017")
results <- ssm_analyze(
  data = jz2017, 
  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), 
  angles = c(90, 135, 180, 225, 270, 315, 360, 45), 
  measures = c("NARPD", "ASPD"),
  measures_labels = c("Narcissistic PD", "Antisocial PD")
)
summary(results)
#> 
#> Statistical Basis:    Correlation Scores 
#> Bootstrap Resamples:  2000 
#> Confidence Level:     0.95 
#> Listwise Deletion:    TRUE 
#> Scale Displacements:  90 135 180 225 270 315 360 45 
#> 
#> 
#> # Profile [Narcissistic PD]:
#> 
#>                Estimate   Lower CI   Upper CI
#> Elevation         0.202      0.169      0.238
#> X-Value          -0.062     -0.094     -0.029
#> Y-Value           0.179      0.145      0.213
#> Amplitude         0.189      0.154      0.227
#> Displacement    108.967     98.633    118.537
#> Model Fit         0.957                      
#> 
#> 
#> # Profile [Antisocial PD]:
#> 
#>                Estimate   Lower CI   Upper CI
#> Elevation         0.124      0.087      0.158
#> X-Value          -0.099     -0.133     -0.064
#> Y-Value           0.203      0.170      0.239
#> Amplitude         0.226      0.191      0.264
#> Displacement    115.927    107.327    124.188
#> Model Fit         0.964

ssm_table(results, drop_xy = TRUE)

Correlation-based Structural Summary Statistics with 95% CIs

Profile	Elevation	Amplitude	Displacement	Fit
Narcissistic PD	0.20 (0.17, 0.24)	0.19 (0.15, 0.23)	109.0 (98.6, 118.5)	0.957
Antisocial PD	0.12 (0.09, 0.16)	0.23 (0.19, 0.26)	115.9 (107.3, 124.2)	0.964

ssm_plot_circle(results)

ssm_plot_curve(results)

Example 2

results2 <- ssm_analyze(
  data = jz2017, 
  scales = PANO(), 
  angles = octants(), 
  grouping = "Gender",
  contrast = TRUE
)
summary(results2)
#> 
#> Statistical Basis:    Mean Scores 
#> Bootstrap Resamples:  2000 
#> Confidence Level:     0.95 
#> Listwise Deletion:    TRUE 
#> Scale Displacements:  90 135 180 225 270 315 360 45 
#> 
#> 
#> # Profile [Female]:
#> 
#>                Estimate   Lower CI   Upper CI
#> Elevation         0.946      0.909      0.984
#> X-Value           0.459      0.421      0.498
#> Y-Value          -0.310     -0.355     -0.267
#> Amplitude         0.554      0.511      0.598
#> Displacement    325.963    322.182    330.063
#> Model Fit         0.889                      
#> 
#> 
#> # Profile [Male]:
#> 
#>                Estimate   Lower CI   Upper CI
#> Elevation         0.884      0.843      0.923
#> X-Value           0.227      0.192      0.262
#> Y-Value          -0.186     -0.225     -0.148
#> Amplitude         0.294      0.257      0.329
#> Displacement    320.685    313.462    327.843
#> Model Fit         0.824                      
#> 
#> 
#> # Contrast [Male - Female]:
#> 
#>                  Estimate   Lower CI   Upper CI
#> Δ Elevation        -0.062     -0.118     -0.005
#> Δ X-Value          -0.232     -0.283     -0.181
#> Δ Y-Value           0.124      0.067      0.183
#> Δ Amplitude        -0.261     -0.318     -0.202
#> Δ Displacement     -5.278    -13.353      2.755
#> Δ Model Fit        -0.066

ssm_table(results2, drop_xy = TRUE)

Correlation-based Structural Summary Statistics with 95% CIs

Contrast	Elevation	Amplitude	Displacement	Fit
Female	0.95 (0.91, 0.98)	0.55 (0.51, 0.60)	326.0 (322.2, 330.1)	0.889
Male	0.88 (0.84, 0.92)	0.29 (0.26, 0.33)	320.7 (313.5, 327.8)	0.824
Male - Female	-0.06 (-0.12, -0.01)	-0.26 (-0.32, -0.20)	-5.3 (-13.4, 2.8)	-0.066

ssm_plot_contrast(results2, drop_xy = TRUE)

Code of Conduct

Please note that the ‘circumplex’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

References

Girard, J. M., Zimmermann, J., & Wright, A. G. C. (2018). New tools for circumplex data analysis and visualization in R. Meeting of the Society for Interpersonal Theory and Research. Montreal, Canada.

Zimmermann, J., & Wright, A. G. C. (2017). Beyond description in interpersonal construct validation: Methodological advances in the circumplex Structural Summary Approach. Assessment, 24(1), 3–23.

Wright, A. G. C., Pincus, A. L., Conroy, D. E., & Hilsenroth, M. J. (2009). Integrating methods to optimize circumplex description and comparison of groups. Journal of Personality Assessment, 91(4), 311–322.