Correlation Test: Formula interface

The formula implementation performs pairwise correlation tests between a single response variable (LHS) and one or more independent variables (RHS).

1. y ~ x: one independent variable, one correlation test.

2. y ~ x1 + x2: multiple independent variables, one test per RHS term.

Use a formula directly as the model ID to select this implementation.

Arguments

The following arguments are passed via ... in CORTEST():

.cor_type

String. One of "pearson", "spearman", or "kendall". Default "pearson".

.alt

String. One of "two.sided", "greater", or "less". Default "two.sided".

.ci

Numeric. Confidence level. Default 0.95. Only used for Pearson; silently ignored for Kendall and Spearman.

Correlation test default class

As detailed by cortest-rel, it returns a class_corr_two object inheriting from class_stat_infer by default. You need to process outputs by:

• print(): Write it down through print from variant().

• tidy(): Use making_tidy() to register a tidy method if needed.

if the variants from this method pipeline doesn't return a class_corr_two object.

Hypothesis claims

Not supported. Use rel() with the base variant for state_null() with RHO().

See also

Other cortest-implementations: cortest-rel

Examples

cars |>
    define_model(dist ~ speed) |>
    prepare_test(CORTEST) |>
    conclude()
#> 
#> == Model ======================================================================= 
#> 
#> Variable Mapper : formula 
#> Args : dist ~ speed 
#>     left_var : 1 
#>     right_var : 1 
#> 
#> == Correlation Test ============================================================ 
#> 
#> -- Summary ---------------------------------------------------------------------
#> 
#> ─────────────────────────────────────────────────
#>       pair      estimate  statistic  df  p_val   
#> ─────────────────────────────────────────────────
#>   dist ~ speed   0.807      9.464    48  <0.001  
#> ─────────────────────────────────────────────────
#> 
#> 
#> -- Confidence Interval ---------------------------------------------------------
#> 
#> ────────────────────────────────────
#>       pair      lower_95  upper_95  
#> ────────────────────────────────────
#>   dist ~ speed   0.682     0.886    
#> ────────────────────────────────────
#> 
#> 

# multiple independent variables
mtcars |>
    define_model(mpg ~ wt + hp) |>
    prepare_test(CORTEST) |>
    conclude()
#> 
#> == Model ======================================================================= 
#> 
#> Variable Mapper : formula 
#> Args : mpg ~ wt + hp 
#>     left_var : 1 
#>     right_var : 2 
#> 
#> == Correlation Test ============================================================ 
#> 
#> -- Summary ---------------------------------------------------------------------
#> 
#> ─────────────────────────────────────────────
#>     pair    estimate  statistic  df  p_val   
#> ─────────────────────────────────────────────
#>   mpg ~ wt   -0.868    -9.559    30  <0.001  
#>   mpg ~ hp   -0.776    -6.742    30  <0.001  
#> ─────────────────────────────────────────────
#> 
#> 
#> -- Confidence Interval ---------------------------------------------------------
#> 
#> ────────────────────────────────
#>     pair    lower_95  upper_95  
#> ────────────────────────────────
#>   mpg ~ wt   -0.934    -0.744   
#>   mpg ~ hp   -0.885    -0.586   
#> ────────────────────────────────
#> 
#>