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This vignette can be referred to by citing the package:

citation("see")
#> 
#> To cite package 'see' in publications use:
#> 
#>   Lüdecke et al., (2021). see: An R Package for Visualizing Statistical
#>   Models. Journal of Open Source Software, 6(64), 3393.
#>   https://doi.org/10.21105/joss.03393
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {{see}: An {R} Package for Visualizing Statistical Models},
#>     author = {Daniel Lüdecke and Indrajeet Patil and Mattan S. Ben-Shachar and Brenton M. Wiernik and Philip Waggoner and Dominique Makowski},
#>     journal = {Journal of Open Source Software},
#>     year = {2021},
#>     volume = {6},
#>     number = {64},
#>     pages = {3393},
#>     doi = {10.21105/joss.03393},
#>   }

Introduction

correlation is an easystats package focused on correlation analysis. It’s lightweight, easy to use, and allows for the computation of many different types of correlation, including:

Pearson’s correlation
Spearman’s rank correlation
Kendall’s rank correlation
Biweight midcorrelation
Distance correlation
Percentage bend correlation
Shepherd’s Pi correlation
Blomqvist’s coefficient
Hoeffding’s D
Gamma correlation
Gaussian rank correlation
Point-Biserial and biserial correlation
Winsorized correlation
Polychoric correlation
Tetrachoric correlation
Multilevel correlation

An overview and description of these correlations types is available here. Moreover, many of these correlation types are available as partial or within a Bayesian framework.

Correlation Scatterplot

(related function documentation)

It is easy to visualize correlation tests with correlation and see.

result <- cor_test(iris, "Sepal.Length", "Petal.Width")

plot(result)

We can even customize that to make it more beautiful:

plot(result, 
     point = list(aes = list(color = "Petal.Width", size = "Sepal.Length"),
                  alpha = 0.66),
     smooth = list(color = "black", se = FALSE)) +
  see::theme_modern() +
  see::scale_color_material_c(palette = "rainbow", guide = "none") +
  scale_size_continuous(guide = "none")

Correlation Matrix

(related function documentation)

The default output for correlation() is a detailed overview including test statistic, p-values and confidence intervals. A shorter summary in matrix-layout can be obtained by using summary().

result <- correlation(iris)

result
#> # Correlation Matrix (pearson-method)
#> 
#> Parameter1   |   Parameter2 |     r |         95% CI | t(148) |         p
#> -------------------------------------------------------------------------
#> Sepal.Length |  Sepal.Width | -0.12 | [-0.27,  0.04] |  -1.44 | 0.152    
#> Sepal.Length | Petal.Length |  0.87 | [ 0.83,  0.91] |  21.65 | < .001***
#> Sepal.Length |  Petal.Width |  0.82 | [ 0.76,  0.86] |  17.30 | < .001***
#> Sepal.Width  | Petal.Length | -0.43 | [-0.55, -0.29] |  -5.77 | < .001***
#> Sepal.Width  |  Petal.Width | -0.37 | [-0.50, -0.22] |  -4.79 | < .001***
#> Petal.Length |  Petal.Width |  0.96 | [ 0.95,  0.97] |  43.39 | < .001***
#> 
#> p-value adjustment method: Holm (1979)
#> Observations: 150

summary(result)
#> # Correlation Matrix (pearson-method)
#> 
#> Parameter    | Petal.Width | Petal.Length | Sepal.Width
#> -------------------------------------------------------
#> Sepal.Length |     0.82*** |      0.87*** |       -0.12
#> Sepal.Width  |    -0.37*** |     -0.43*** |            
#> Petal.Length |     0.96*** |              |            
#> 
#> p-value adjustment method: Holm (1979)

The result from summary() can be used to create a plot.

s <- summary(result)
plot(s)

To change the style of geoms, use the show_data-argument.

plot(s, show_data = "points")

And a “redundant” summary can be plotted as well:

s <- summary(result, redundant = TRUE)

s
#> # Correlation Matrix (pearson-method)
#> 
#> Parameter    | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width
#> ----------------------------------------------------------------------
#> Sepal.Length |      1.00*** |       -0.12 |      0.87*** |     0.82***
#> Sepal.Width  |        -0.12 |     1.00*** |     -0.43*** |    -0.37***
#> Petal.Length |      0.87*** |    -0.43*** |      1.00*** |     0.96***
#> Petal.Width  |      0.82*** |    -0.37*** |      0.96*** |     1.00***
#> 
#> p-value adjustment method: Holm (1979)

plot(s)

The corrlation function also provides a convenient way to change names for selected variables:

plot(summary(correlation(
  data = select(mtcars, wt, mpg, drat),
  rename = c("weight", "miles per gallon", "rear axle ratio")
)))

Gaussian Graphical Models (GGMs)

To create a Gaussian Graphical Models plot, the library ggraph needs to be loaded first.

library(ggraph)
result <- correlation(mtcars, partial = TRUE)

result
#> # Correlation Matrix (pearson-method)
#> 
#> Parameter1 | Parameter2 |     r |         95% CI | t(30) |         p
#> --------------------------------------------------------------------
#> mpg        |        cyl | -0.02 | [-0.37,  0.33] | -0.13 | > .999   
#> mpg        |       disp |  0.16 | [-0.20,  0.48] |  0.89 | > .999   
#> mpg        |         hp | -0.21 | [-0.52,  0.15] | -1.18 | > .999   
#> mpg        |       drat |  0.10 | [-0.25,  0.44] |  0.58 | > .999   
#> mpg        |         wt | -0.39 | [-0.65, -0.05] | -2.34 | > .999   
#> mpg        |       qsec |  0.24 | [-0.12,  0.54] |  1.34 | > .999   
#> mpg        |         vs |  0.03 | [-0.32,  0.38] |  0.18 | > .999   
#> mpg        |         am |  0.26 | [-0.10,  0.56] |  1.46 | > .999   
#> mpg        |       gear |  0.10 | [-0.26,  0.43] |  0.52 | > .999   
#> mpg        |       carb | -0.05 | [-0.39,  0.30] | -0.29 | > .999   
#> cyl        |       disp |  0.27 | [-0.09,  0.56] |  1.51 | > .999   
#> cyl        |         hp |  0.17 | [-0.19,  0.49] |  0.97 | > .999   
#> cyl        |       drat | -0.27 | [-0.57,  0.09] | -1.55 | > .999   
#> cyl        |         wt | -0.11 | [-0.44,  0.25] | -0.62 | > .999   
#> cyl        |       qsec | -0.26 | [-0.56,  0.10] | -1.48 | > .999   
#> cyl        |         vs | -0.32 | [-0.60,  0.03] | -1.85 | > .999   
#> cyl        |         am | -0.25 | [-0.55,  0.11] | -1.39 | > .999   
#> cyl        |       gear | -0.35 | [-0.62,  0.00] | -2.04 | > .999   
#> cyl        |       carb |  0.23 | [-0.13,  0.53] |  1.27 | > .999   
#> disp       |         hp |  0.54 | [ 0.24,  0.75] |  3.52 | 0.072    
#> disp       |       drat |  0.10 | [-0.26,  0.43] |  0.53 | > .999   
#> disp       |         wt |  0.76 | [ 0.56,  0.88] |  6.43 | < .001***
#> disp       |       qsec | -0.31 | [-0.60,  0.04] | -1.80 | > .999   
#> disp       |         vs | -0.10 | [-0.44,  0.25] | -0.57 | > .999   
#> disp       |         am | -0.07 | [-0.41,  0.29] | -0.37 | > .999   
#> disp       |       gear |  0.06 | [-0.29,  0.40] |  0.34 | > .999   
#> disp       |       carb | -0.66 | [-0.82, -0.40] | -4.77 | 0.002**  
#> hp         |       drat | -0.06 | [-0.40,  0.29] | -0.34 | > .999   
#> hp         |         wt | -0.30 | [-0.59,  0.06] | -1.72 | > .999   
#> hp         |       qsec | -0.05 | [-0.39,  0.30] | -0.29 | > .999   
#> hp         |         vs |  0.27 | [-0.08,  0.57] |  1.55 | > .999   
#> hp         |         am |  0.10 | [-0.26,  0.43] |  0.55 | > .999   
#> hp         |       gear |  0.11 | [-0.25,  0.44] |  0.59 | > .999   
#> hp         |       carb |  0.50 | [ 0.19,  0.72] |  3.19 | 0.167    
#> drat       |         wt | -0.11 | [-0.44,  0.25] | -0.62 | > .999   
#> drat       |       qsec | -0.06 | [-0.40,  0.29] | -0.33 | > .999   
#> drat       |         vs |  0.03 | [-0.33,  0.37] |  0.14 | > .999   
#> drat       |         am |  0.12 | [-0.24,  0.45] |  0.68 | > .999   
#> drat       |       gear |  0.06 | [-0.29,  0.40] |  0.35 | > .999   
#> drat       |       carb |  0.21 | [-0.15,  0.52] |  1.18 | > .999   
#> wt         |       qsec |  0.55 | [ 0.24,  0.75] |  3.57 | 0.063    
#> wt         |         vs | -0.06 | [-0.40,  0.29] | -0.36 | > .999   
#> wt         |         am |  0.02 | [-0.33,  0.36] |  0.10 | > .999   
#> wt         |       gear | -0.13 | [-0.46,  0.23] | -0.71 | > .999   
#> wt         |       carb |  0.62 | [ 0.34,  0.80] |  4.30 | 0.009**  
#> qsec       |         vs |  0.35 | [ 0.00,  0.62] |  2.02 | > .999   
#> qsec       |         am | -0.32 | [-0.60,  0.03] | -1.84 | > .999   
#> qsec       |       gear | -0.10 | [-0.43,  0.26] | -0.56 | > .999   
#> qsec       |       carb | -0.25 | [-0.55,  0.11] | -1.40 | > .999   
#> vs         |         am | -0.21 | [-0.52,  0.15] | -1.18 | > .999   
#> vs         |       gear |  0.04 | [-0.31,  0.38] |  0.22 | > .999   
#> vs         |       carb | -0.09 | [-0.43,  0.27] | -0.50 | > .999   
#> am         |       gear |  0.28 | [-0.08,  0.57] |  1.58 | > .999   
#> am         |       carb | -0.05 | [-0.39,  0.31] | -0.25 | > .999   
#> gear       |       carb |  0.43 | [ 0.09,  0.68] |  2.59 | 0.727    
#> 
#> p-value adjustment method: Holm (1979)
#> Observations: 32

plot(result)