Compute Correlation Matrix with P-values

Compute correlation matrix with p-values. Numeric columns in the data are detected and automatically selected for the analysis. You can also specify variables of interest to be used in the correlation analysis.

Usage

cor_mat(
  data,
  ...,
  vars = NULL,
  method = "pearson",
  alternative = "two.sided",
  conf.level = 0.95
)

cor_pmat(
  data,
  ...,
  vars = NULL,
  method = "pearson",
  alternative = "two.sided",
  conf.level = 0.95
)

cor_get_pval(x)

Arguments

data

a data.frame containing the variables.

...

One or more unquoted expressions (or variable names) separated by commas. Used to select a variable of interest.

vars

a character vector containing the variable names of interest.

method

a character string indicating which correlation coefficient is to be used for the test. One of "pearson", "kendall", or "spearman", can be abbreviated.

alternative

indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. "greater" corresponds to positive association, "less" to negative association.

conf.level

confidence level for the returned confidence interval. Currently only used for the Pearson product moment correlation coefficient if there are at least 4 complete pairs of observations.

x

an object of class cor_mat

Value

a data frame

Functions

• cor_mat(): compute correlation matrix with p-values. Returns a data frame containing the matrix of the correlation coefficients. The output has an attribute named "pvalue", which contains the matrix of the correlation test p-values.

• cor_pmat(): compute the correlation matrix but returns only the p-values of the tests.

• cor_get_pval(): extract a correlation matrix p-values from an object of class cor_mat(). P-values are not adjusted.

See also

cor_test(), cor_reorder(), cor_gather(), cor_select(), cor_as_symbols(), pull_triangle(), replace_triangle()

Examples

# Data preparation
#:::::::::::::::::::::::::::::::::::::::::::
mydata <- mtcars %>%
  select(mpg, disp, hp, drat, wt, qsec)
head(mydata, 3)
#>                mpg disp  hp drat    wt  qsec
#> Mazda RX4     21.0  160 110 3.90 2.620 16.46
#> Mazda RX4 Wag 21.0  160 110 3.90 2.875 17.02
#> Datsun 710    22.8  108  93 3.85 2.320 18.61

# Compute correlation matrix
#::::::::::::::::::::::::::::::::::::::::::
# Correlation matrix between all variables
cor.mat <- mydata %>% cor_mat()
cor.mat
#> # A tibble: 6 × 7
#>   rowname   mpg  disp    hp   drat    wt   qsec
#> * <chr>   <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1 mpg      1    -0.85 -0.78  0.68  -0.87  0.42 
#> 2 disp    -0.85  1     0.79 -0.71   0.89 -0.43 
#> 3 hp      -0.78  0.79  1    -0.45   0.66 -0.71 
#> 4 drat     0.68 -0.71 -0.45  1     -0.71  0.091
#> 5 wt      -0.87  0.89  0.66 -0.71   1    -0.17 
#> 6 qsec     0.42 -0.43 -0.71  0.091 -0.17  1    

# Specify some variables of interest
mydata %>% cor_mat(mpg, hp, wt)
#> # A tibble: 3 × 4
#>   rowname   mpg    hp    wt
#> * <chr>   <dbl> <dbl> <dbl>
#> 1 mpg      1    -0.78 -0.87
#> 2 hp      -0.78  1     0.66
#> 3 wt      -0.87  0.66  1   

# Or remove some variables in the data
# before the analysis
mydata %>% cor_mat(-mpg, -hp)
#> # A tibble: 4 × 5
#>   rowname  disp   drat    wt   qsec
#> * <chr>   <dbl>  <dbl> <dbl>  <dbl>
#> 1 disp     1    -0.71   0.89 -0.43 
#> 2 drat    -0.71  1     -0.71  0.091
#> 3 wt       0.89 -0.71   1    -0.17 
#> 4 qsec    -0.43  0.091 -0.17  1    

# Significance levels
#::::::::::::::::::::::::::::::::::::::::::
cor.mat %>% cor_get_pval()
#> # A tibble: 6 × 7
#>   rowname      mpg     disp           hp       drat        wt       qsec
#>   <chr>      <dbl>    <dbl>        <dbl>      <dbl>     <dbl>      <dbl>
#> 1 mpg     0        9.38e-10 0.000000179  0.0000178  1.29e- 10 0.0171    
#> 2 disp    9.38e-10 0        0.0000000714 0.00000528 1.22e- 11 0.0131    
#> 3 hp      1.79e- 7 7.14e- 8 0            0.00999    4.15e-  5 0.00000577
#> 4 drat    1.78e- 5 5.28e- 6 0.00999      0          4.78e-  6 0.620     
#> 5 wt      1.29e-10 1.22e-11 0.0000415    0.00000478 2.27e-236 0.339     
#> 6 qsec    1.71e- 2 1.31e- 2 0.00000577   0.620      3.39e-  1 0         


# Visualize
#::::::::::::::::::::::::::::::::::::::::::
# Insignificant correlations are marked by crosses
cor.mat %>%
  cor_reorder() %>%
  pull_lower_triangle() %>%
  cor_plot(label = TRUE)


# Gather/collapse correlation matrix into long format
#::::::::::::::::::::::::::::::::::::::::::
cor.mat %>% cor_gather()
#> # A tibble: 36 × 4
#>    var1  var2    cor        p
#>    <chr> <chr> <dbl>    <dbl>
#>  1 mpg   mpg    1    0       
#>  2 disp  mpg   -0.85 9.38e-10
#>  3 hp    mpg   -0.78 1.79e- 7
#>  4 drat  mpg    0.68 1.78e- 5
#>  5 wt    mpg   -0.87 1.29e-10
#>  6 qsec  mpg    0.42 1.71e- 2
#>  7 mpg   disp  -0.85 9.38e-10
#>  8 disp  disp   1    0       
#>  9 hp    disp   0.79 7.14e- 8
#> 10 drat  disp  -0.71 5.28e- 6
#> # ℹ 26 more rows