Correlation Test

CORTEST() performs a correlation test for one-to-one variable relationships. If CORTEST is supplied within the lazy-loaded pipeline, supply CORTEST as a function i.e. prepare_test(.test = CORTEST) call.

Usage

CORTEST(.var_id = NULL, .data = NULL, ...)

Arguments

.var_id

A variable mapper <var_id> for CORTEST(), e.g. rel(). When supplied, the test executes immediately.

.data

A data frame. Only used on the standalone path.

...

Additional arguments passed to the implementation. See the Arguments section of each implementation page.

Value

A cld_exec object (in conclude()), or a test_spec object when .var_id = NULL. The default correlation test class for most paths is class_corr_two.

Supported variable mapper <var_id>s

Each variable mapper <var_id> routes to a separate implementation. See the linked pages for full argument lists, variants, and correlation test class details:

• rel(): one-to-one correlation test. See details from cortest-rel.

• <formula>: one-to-many correlation test. See details from cortest-formula.

See also

cortest-rel, cortest-formula for per-implementation details. class_corr_two for correlation test class slots. via(), state_null(), conclude(), auto_tidy().

Examples

# eager
CORTEST(rel(speed, dist), cars)
#> -- 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    
#> ────────────────────────────────────
#> 
#> 

# grammatical syntax
cars |>
    define_model(rel(speed, dist)) |>
    prepare_test(CORTEST) |>
    conclude()
#> 
#> == Model ======================================================================= 
#> 
#> Variable Mapper : rel 
#> Args : speed ; dist 
#>     x_vars : 1 
#>     resp_vars : 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    
#> ────────────────────────────────────
#> 
#> 

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

# Spearman
suppressWarnings({
    cars |>
        define_model(rel(speed, dist)) |>
        prepare_test(CORTEST) |>
        via("spearman") |>
        conclude()
})
#> 
#> == Model ======================================================================= 
#> 
#> Variable Mapper : rel 
#> Args : speed ; dist 
#>     x_vars : 1 
#>     resp_vars : 1 
#> 
#> == Correlation Test · spearman ================================================= 
#> 
#> -- Summary ---------------------------------------------------------------------
#> 
#> ─────────────────────────────────────────────
#>       pair      estimate  statistic  p_val   
#> ─────────────────────────────────────────────
#>   dist ~ speed   0.830    3532.819   <0.001  
#> ─────────────────────────────────────────────
#> 
#> 

# Custom Hypothesis Expression
cars |>
    define_model(rel(speed, dist)) |>
    prepare_test(CORTEST) |>
    state_null(RHO(speed, dist) >= 0.8) |>
    conclude()
#> 
#> == Model ======================================================================= 
#> 
#> Variable Mapper : rel 
#> Args : speed ; dist 
#>     x_vars : 1 
#>     resp_vars : 1 
#> 
#> == Correlation Test ============================================================ 
#> 
#> -- Summary ---------------------------------------------------------------------
#> 
#> ────────────────────────────────────────────
#>       pair      estimate  statistic  p_val  
#> ────────────────────────────────────────────
#>   dist ~ speed   0.807      0.133    0.553  
#> ────────────────────────────────────────────
#> 
#> 
#> -- Confidence Interval ---------------------------------------------------------
#> 
#> ────────────────────────────────────
#>       pair      lower_95  upper_95  
#> ────────────────────────────────────
#>   dist ~ speed     -1      0.876    
#> ────────────────────────────────────
#> 
#>