Correlation

Source: R/correlation-methods.R

Correlation

correlation(x, y, ...)

# S4 method for numeric,numeric
correlation(x, y, method = c("pearson", "kendall", "spearman"))

# S4 method for matrix,missingOrNULL
correlation(x, y = NULL, method = c("pearson", "kendall", "spearman"))

# S4 method for matrix,matrix
correlation(x, y, method = c("pearson", "kendall", "spearman"))

# S4 method for Matrix,missingOrNULL
correlation(x, y = NULL, method = c("pearson", "kendall", "spearman"))

# S4 method for Matrix,Matrix
correlation(x, y, method = c("pearson", "kendall", "spearman"))

# S4 method for SummarizedExperiment,missingOrNULL
correlation(
  x,
  y = NULL,
  i = 1L,
  j = NULL,
  method = c("pearson", "kendall", "spearman")
)

# S4 method for SummarizedExperiment,SummarizedExperiment
correlation(x, y, i = 1L, method = c("pearson", "kendall", "spearman"))

Arguments

x

Object.
y

Object.
method

a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.
i

integer(1) or character(1). For SummarizedExperiment, primary assay.
j

integer(1), character(1), or NULL. For SummarizedExperiment, optional secondary assay. If NULL, calculates correlation matrix only on the primary assay.
...

Additional arguments.

Value

numeric(1) or matrix.

Note

Updated 2019-11-08.

See also

Examples

data(correlation, package = "acidtest")
list <- correlation
rm(correlation)
#> Warning: object 'correlation' not found

## vector ====
x <- list[["vector_x"]]
y <- list[["vector_y"]]

head(x)
#> [1] 1 5 4 2 0 2
head(y)
#> [1]  1.6343433  3.6762875  4.7308222  0.5660087 -0.8572071  3.5346193

correlation(x = x, y = y)
#> Calculating pearson correlation on 100 values.
#> [1] 0.9634786

## matrix ====
x <- list[["matrix_x"]]
y <- list[["matrix_y"]]

head(x)
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,]    1    1    1    0    7    1    3   11    3    10
#> [2,]    5    8    5    2   11   20    3    0   11     0
#> [3,]    4   11    2    0    5    0    6    0    2     7
#> [4,]    2    6    7   12    4    8    0    1    2     8
#> [5,]    0    3    2    4    3    2    0    5    2     1
#> [6,]    2    6    0    4   11    1    0    0    2     0
head(y)
#>            [,1]       [,2]       [,3]       [,4]      [,5]      [,6]       [,7]
#> [1,]  1.6343433 -0.5999092  1.8159738  1.1496279  7.170519  1.561246  2.8229171
#> [2,]  3.6762875  8.7288806  3.4820749  1.4867973 11.290924 18.468396  4.9713020
#> [3,]  4.7308222 11.8516005  1.9901926 -0.5939147  6.326446  1.852269  5.9681443
#> [4,]  0.5660087  4.7761165  5.1564128 12.2934493  4.591485  7.871963  0.7907310
#> [5,] -0.8572071  3.0976105  3.3745135  4.4976942  1.294703  2.176753 -0.3036267
#> [6,]  3.5346193  7.8470870 -0.5275027  5.3964767 10.936949  2.177984  0.8971997
#>            [,8]     [,9]      [,10]
#> [1,]  9.6431737 3.811118 11.0378596
#> [2,] -0.1783549 9.242401 -1.5963026
#> [3,]  1.1793192 2.091185  7.2866099
#> [4,]  2.5901375 1.540074  7.1869350
#> [5,]  4.5118578 3.426261 -0.6193615
#> [6,] -1.7009159 1.383546 -1.7171582

stats::cor(x)
#>             [,1]        [,2]        [,3]        [,4]         [,5]         [,6]
#>  [1,]  1.0000000  0.11769600  0.40067107 -0.19869260  0.432248547  0.205519098
#>  [2,]  0.1176960  1.00000000 -0.09731989 -0.31952037  0.153613416 -0.103203311
#>  [3,]  0.4006711 -0.09731989  1.00000000  0.39624791 -0.192037439  0.176212444
#>  [4,] -0.1986926 -0.31952037  0.39624791  1.00000000 -0.209640163  0.246450494
#>  [5,]  0.4322485  0.15361342 -0.19203744 -0.20964016  1.000000000 -0.002892377
#>  [6,]  0.2055191 -0.10320331  0.17621244  0.24645049 -0.002892377  1.000000000
#>  [7,] -0.2156807  0.59139279  0.13859641 -0.40092438 -0.414228998 -0.079212164
#>  [8,] -0.3018880 -0.19825248 -0.03367939 -0.40314191 -0.138046668 -0.444760974
#>  [9,]  0.3690050 -0.11110492  0.69602694 -0.05780996 -0.147455094  0.508497704
#> [10,] -0.3851162 -0.14703686  0.12601100  0.23068886 -0.295237109 -0.215708752
#>              [,7]        [,8]        [,9]      [,10]
#>  [1,] -0.21568066 -0.30188805  0.36900501 -0.3851162
#>  [2,]  0.59139279 -0.19825248 -0.11110492 -0.1470369
#>  [3,]  0.13859641 -0.03367939  0.69602694  0.1260110
#>  [4,] -0.40092438 -0.40314191 -0.05780996  0.2306889
#>  [5,] -0.41422900 -0.13804667 -0.14745509 -0.2952371
#>  [6,] -0.07921216 -0.44476097  0.50849770 -0.2157088
#>  [7,]  1.00000000  0.25945424  0.36872314  0.1762733
#>  [8,]  0.25945424  1.00000000 -0.03329687  0.2664217
#>  [9,]  0.36872314 -0.03329687  1.00000000 -0.1398469
#> [10,]  0.17627333  0.26642168 -0.13984694  1.0000000
correlation(x)
#> Calculating pearson correlation matrix.
#>             [,1]        [,2]        [,3]        [,4]         [,5]         [,6]
#>  [1,]  1.0000000  0.11769600  0.40067107 -0.19869260  0.432248547  0.205519098
#>  [2,]  0.1176960  1.00000000 -0.09731989 -0.31952037  0.153613416 -0.103203311
#>  [3,]  0.4006711 -0.09731989  1.00000000  0.39624791 -0.192037439  0.176212444
#>  [4,] -0.1986926 -0.31952037  0.39624791  1.00000000 -0.209640163  0.246450494
#>  [5,]  0.4322485  0.15361342 -0.19203744 -0.20964016  1.000000000 -0.002892377
#>  [6,]  0.2055191 -0.10320331  0.17621244  0.24645049 -0.002892377  1.000000000
#>  [7,] -0.2156807  0.59139279  0.13859641 -0.40092438 -0.414228998 -0.079212164
#>  [8,] -0.3018880 -0.19825248 -0.03367939 -0.40314191 -0.138046668 -0.444760974
#>  [9,]  0.3690050 -0.11110492  0.69602694 -0.05780996 -0.147455094  0.508497704
#> [10,] -0.3851162 -0.14703686  0.12601100  0.23068886 -0.295237109 -0.215708752
#>              [,7]        [,8]        [,9]      [,10]
#>  [1,] -0.21568066 -0.30188805  0.36900501 -0.3851162
#>  [2,]  0.59139279 -0.19825248 -0.11110492 -0.1470369
#>  [3,]  0.13859641 -0.03367939  0.69602694  0.1260110
#>  [4,] -0.40092438 -0.40314191 -0.05780996  0.2306889
#>  [5,] -0.41422900 -0.13804667 -0.14745509 -0.2952371
#>  [6,] -0.07921216 -0.44476097  0.50849770 -0.2157088
#>  [7,]  1.00000000  0.25945424  0.36872314  0.1762733
#>  [8,]  0.25945424  1.00000000 -0.03329687  0.2664217
#>  [9,]  0.36872314 -0.03329687  1.00000000 -0.1398469
#> [10,]  0.17627333  0.26642168 -0.13984694  1.0000000

stats::cor(x = c(x), y = c(y))
#> [1] 0.9634786
correlation(x = x, y = y)
#> Calculating pearson correlation on 100 values.
#> [1] 0.9634786

## SummarizedExperiment ====
x <- list[["SummarizedExperiment_x"]]
y <- list[["SummarizedExperiment_y"]]

correlation(x = x, i = 1L)
#> Calculating pearson correlation matrix.
#>             [,1]        [,2]        [,3]        [,4]         [,5]         [,6]
#>  [1,]  1.0000000  0.11769600  0.40067107 -0.19869260  0.432248547  0.205519098
#>  [2,]  0.1176960  1.00000000 -0.09731989 -0.31952037  0.153613416 -0.103203311
#>  [3,]  0.4006711 -0.09731989  1.00000000  0.39624791 -0.192037439  0.176212444
#>  [4,] -0.1986926 -0.31952037  0.39624791  1.00000000 -0.209640163  0.246450494
#>  [5,]  0.4322485  0.15361342 -0.19203744 -0.20964016  1.000000000 -0.002892377
#>  [6,]  0.2055191 -0.10320331  0.17621244  0.24645049 -0.002892377  1.000000000
#>  [7,] -0.2156807  0.59139279  0.13859641 -0.40092438 -0.414228998 -0.079212164
#>  [8,] -0.3018880 -0.19825248 -0.03367939 -0.40314191 -0.138046668 -0.444760974
#>  [9,]  0.3690050 -0.11110492  0.69602694 -0.05780996 -0.147455094  0.508497704
#> [10,] -0.3851162 -0.14703686  0.12601100  0.23068886 -0.295237109 -0.215708752
#>              [,7]        [,8]        [,9]      [,10]
#>  [1,] -0.21568066 -0.30188805  0.36900501 -0.3851162
#>  [2,]  0.59139279 -0.19825248 -0.11110492 -0.1470369
#>  [3,]  0.13859641 -0.03367939  0.69602694  0.1260110
#>  [4,] -0.40092438 -0.40314191 -0.05780996  0.2306889
#>  [5,] -0.41422900 -0.13804667 -0.14745509 -0.2952371
#>  [6,] -0.07921216 -0.44476097  0.50849770 -0.2157088
#>  [7,]  1.00000000  0.25945424  0.36872314  0.1762733
#>  [8,]  0.25945424  1.00000000 -0.03329687  0.2664217
#>  [9,]  0.36872314 -0.03329687  1.00000000 -0.1398469
#> [10,]  0.17627333  0.26642168 -0.13984694  1.0000000
correlation(x = x, i = 1L, j = 2L)
#> Calculating pearson correlation on 100 values.
#> [1] 0.9634786
correlation(x = x, y = y)
#> Calculating pearson correlation on 100 values.
#> [1] 0.9634786