[참고] https://www.r-bloggers.com/the-ulimate-package-for-correlations-by-easystats/

1 The ulimate package for correlations (by easystats)

1.1 packages

library(correlation)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(BayesFactor)

## Loading required package: coda

## Loading required package: Matrix

## ************
## Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
## 
## Type BFManual() to open the manual.
## ************

library(psych)
library(see) 
library(ggraph)

## Loading required package: ggplot2

## 
## Attaching package: 'ggplot2'

## The following objects are masked from 'package:psych':
## 
##     %+%, alpha

1.2 Correlation details and matrix

data(iris)
cor <- correlation(iris)
cor

## Parameter1   |   Parameter2 |     r |     t |  df |      p |         95% CI |  Method | n_Obs
## ---------------------------------------------------------------------------------------------
## Sepal.Length |  Sepal.Width | -0.12 | -1.44 | 148 | 0.152  | [-0.27,  0.04] | Pearson |   150
## Sepal.Length | Petal.Length |  0.87 | 21.65 | 148 | < .001 | [ 0.83,  0.91] | Pearson |   150
## Sepal.Length |  Petal.Width |  0.82 | 17.30 | 148 | < .001 | [ 0.76,  0.86] | Pearson |   150
## Sepal.Width  | Petal.Length | -0.43 | -5.77 | 148 | < .001 | [-0.55, -0.29] | Pearson |   150
## Sepal.Width  |  Petal.Width | -0.37 | -4.79 | 148 | < .001 | [-0.50, -0.22] | Pearson |   150
## Petal.Length |  Petal.Width |  0.96 | 43.39 | 148 | < .001 | [ 0.95,  0.97] | Pearson |   150

summary(cor)

## 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*** |              |

as.table(cor)

## 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***

1.3 Grouped dataframes

The function also supports stratified correlations, all within the tidyverse workflow!

iris %>% 
  select(Species, Petal.Width, Sepal.Length, Sepal.Width) %>%
  group_by(Species) %>% 
  correlation()

## Group      |   Parameter1 |   Parameter2 |    r |    t | df |      p |        95% CI |  Method | n_Obs
## ------------------------------------------------------------------------------------------------------
## setosa     |  Petal.Width | Sepal.Length | 0.28 | 2.01 | 48 | 0.101  | [ 0.00, 0.52] | Pearson |    50
## setosa     |  Petal.Width |  Sepal.Width | 0.23 | 1.66 | 48 | 0.104  | [-0.05, 0.48] | Pearson |    50
## setosa     | Sepal.Length |  Sepal.Width | 0.74 | 7.68 | 48 | < .001 | [ 0.59, 0.85] | Pearson |    50
## versicolor |  Petal.Width | Sepal.Length | 0.55 | 4.52 | 48 | < .001 | [ 0.32, 0.72] | Pearson |    50
## versicolor |  Petal.Width |  Sepal.Width | 0.66 | 6.15 | 48 | < .001 | [ 0.47, 0.80] | Pearson |    50
## versicolor | Sepal.Length |  Sepal.Width | 0.53 | 4.28 | 48 | < .001 | [ 0.29, 0.70] | Pearson |    50
## virginica  |  Petal.Width | Sepal.Length | 0.28 | 2.03 | 48 | 0.048  | [ 0.00, 0.52] | Pearson |    50
## virginica  |  Petal.Width |  Sepal.Width | 0.54 | 4.42 | 48 | < .001 | [ 0.31, 0.71] | Pearson |    50
## virginica  | Sepal.Length |  Sepal.Width | 0.46 | 3.56 | 48 | 0.002  | [ 0.20, 0.65] | Pearson |    50

1.4 Bayesian Correlations

It is very easy to switch to a Bayesian framework.

correlation(iris, bayesian=TRUE)

## Parameter1   |   Parameter2 |   rho |         89% CI |     pd | % in ROPE |    BF |              Prior | n_Obs
## --------------------------------------------------------------------------------------------------------------
## Sepal.Length |  Sepal.Width | -0.11 | [-0.24,  0.01] | 91.62% |    43.03% |  0.51 | Cauchy (0 +- 0.33) |   150
## Sepal.Length | Petal.Length |  0.86 | [ 0.83,  0.89] |   100% |        0% | > 999 | Cauchy (0 +- 0.33) |   150
## Sepal.Length |  Petal.Width |  0.80 | [ 0.76,  0.85] |   100% |        0% | > 999 | Cauchy (0 +- 0.33) |   150
## Sepal.Width  | Petal.Length | -0.41 | [-0.52, -0.31] |   100% |        0% | > 999 | Cauchy (0 +- 0.33) |   150
## Sepal.Width  |  Petal.Width | -0.35 | [-0.46, -0.23] |   100% |     0.07% | > 999 | Cauchy (0 +- 0.33) |   150
## Petal.Length |  Petal.Width |  0.96 | [ 0.95,  0.97] |   100% |        0% | > 999 | Cauchy (0 +- 0.33) |   150

correlation(iris, include_factors = TRUE, method = "auto")

## Warning in biserialc(x[, j], y[, i], j, i): For x = 1 y = 1 x seems to be
## dichotomous, not continuous

## Warning in biserialc(x[, j], y[, i], j, i): For x = 1 y = 1 x seems to be
## dichotomous, not continuous

## Parameter1         |         Parameter2 |     r |      t |  df |      p |         95% CI |      Method | n_Obs
## --------------------------------------------------------------------------------------------------------------
## Sepal.Length       |        Sepal.Width | -0.12 |  -1.44 | 148 | 0.304  | [-0.27,  0.04] |     Pearson |   150
## Sepal.Length       |       Petal.Length |  0.87 |  21.65 | 148 | < .001 | [ 0.83,  0.91] |     Pearson |   150
## Sepal.Length       |        Petal.Width |  0.82 |  17.30 | 148 | < .001 | [ 0.76,  0.86] |     Pearson |   150
## Sepal.Length       |     Species.setosa | -0.93 | -29.97 | 148 | < .001 | [-0.95, -0.90] |    Biserial |   150
## Sepal.Length       | Species.versicolor |  0.10 |   1.25 | 148 | 0.304  | [-0.06,  0.26] |    Biserial |   150
## Sepal.Length       |  Species.virginica |  0.82 |  17.66 | 148 | < .001 | [ 0.77,  0.87] |    Biserial |   150
## Sepal.Width        |       Petal.Length | -0.43 |  -5.77 | 148 | < .001 | [-0.55, -0.29] |     Pearson |   150
## Sepal.Width        |        Petal.Width | -0.37 |  -4.79 | 148 | < .001 | [-0.50, -0.22] |     Pearson |   150
## Sepal.Width        |     Species.setosa |  0.78 |  15.09 | 148 | < .001 | [ 0.71,  0.84] |    Biserial |   150
## Sepal.Width        | Species.versicolor | -0.60 |  -9.20 | 148 | < .001 | [-0.70, -0.49] |    Biserial |   150
## Sepal.Width        |  Species.virginica | -0.18 |  -2.16 | 148 | 0.130  | [-0.33, -0.02] |    Biserial |   150
## Petal.Length       |        Petal.Width |  0.96 |  43.39 | 148 | < .001 | [ 0.95,  0.97] |     Pearson |   150
## Petal.Length       |     Species.setosa | -1.00 |   -Inf | 148 | < .001 | [-1.00, -1.00] |    Biserial |   150
## Petal.Length       | Species.versicolor |  0.26 |   3.27 | 148 | 0.007  | [ 0.10,  0.40] |    Biserial |   150
## Petal.Length       |  Species.virginica |  0.93 |  31.09 | 148 | < .001 | [ 0.91,  0.95] |    Biserial |   150
## Petal.Width        |     Species.setosa | -1.00 |   -Inf | 148 | < .001 | [-1.00, -1.00] |    Biserial |   150
## Petal.Width        | Species.versicolor |  0.15 |   1.87 | 148 | 0.191  | [-0.01,  0.31] |    Biserial |   150
## Petal.Width        |  Species.virginica |  0.99 | 112.56 | 148 | < .001 | [ 0.99,  1.00] |    Biserial |   150
## Species.setosa     | Species.versicolor | -0.88 | -22.35 | 148 | < .001 | [-0.91, -0.84] | Tetrachoric |   150
## Species.setosa     |  Species.virginica | -0.88 | -22.35 | 148 | < .001 | [-0.91, -0.84] | Tetrachoric |   150
## Species.versicolor |  Species.virginica | -0.88 | -22.35 | 148 | < .001 | [-0.91, -0.84] | Tetrachoric |   150

1.5 Partial Correlations

It also supports partial correlations:

iris %>% 
  correlation(partial = TRUE) %>% 
  summary()

## Parameter    | Petal.Width | Petal.Length | Sepal.Width
## -------------------------------------------------------
## Sepal.Length |    -0.34*** |      0.72*** |     0.63***
## Sepal.Width  |     0.35*** |     -0.62*** |            
## Petal.Length |     0.87*** |              |

1.6 Gaussian Graphical Models (GGMs)

Such partial correlations can also be represented as Gaussian graphical models, an increasingly popular tool in psychology:

mtcars %>% 
  correlation(partial = TRUE) %>% 
  plot()

correlation

updragon

2020 3 19