Correlation Analysis with Plain-English Interpretation

Correlation Analysis with Plain-English Interpretation

Usage

cor_interpret(
  x,
  y,
  method = "pearson",
  conf.level = 0.95,
  var1_name = "Variable 1",
  var2_name = "Variable 2",
  context = NULL
)

Arguments

x

A numeric vector

y

A numeric vector

method

Correlation method: "pearson", "spearman", or "kendall". Default "pearson".

conf.level

Confidence level. Default 0.95.

var1_name

Optional name for first variable. Default "Variable 1"

var2_name

Optional name for second variable. Default "Variable 2"

context

Optional description of the study design or sampling method, echoed back in the printed report. Default NULL.

Value

An object of class statease_cor containing correlation results and interpretation. Use print() to display the formatted report.

Examples

x <- c(23, 45, 12, 67, 34, 89, 56, 43, 78, 90)
y <- c(19, 42, 15, 70, 30, 85, 52, 48, 80, 88)
result <- cor_interpret(x, y)
print(result)
#> 
#> -- statease Correlation Report -----------------------------------
#>   Method       : Pearson Product-Moment Correlation
#>   Variables    : Variable 1 & Variable 2
#>   N            : 10  |  Missing: 0
#> -----------------------------------------------------------------
#>   r            : 0.9911
#>   p-value      : 0.0000
#>   95% CI      : [0.9612, 0.9980]
#>   Strength     : very large
#>   Direction    : positive (as one variable increases, the other tends to increase)
#> -----------------------------------------------------------------
#>   Assumption Checks:
#>     Normality (x)           : PASSED   (Shapiro-Wilk p = 0.703, Pearson only)
#>     Normality (y)           : PASSED   (Shapiro-Wilk p = 0.426, Pearson only)
#>     Linearity               : NOTE     (visual inspection is recommended, not testable numerically)
#> 
#>   NOTE: Assumption checks are diagnostic tools and may be
#>   influenced by sample size and other characteristics of the
#>   data. Passing a check does not prove that an assumption is
#>   satisfied, and a warning does not automatically invalidate
#>   the analysis. Interpret these results alongside your
#>   knowledge of the data.
#> -----------------------------------------------------------------
#>   Interpretation:
#>   The correlation is statistically significant (p = 0.0000 < alpha 0.05).
#>   The relationship between Variable 1 and Variable 2 is
#>   very large and positive (as one variable increases, the other tends to increase) in direction.
#> -----------------------------------------------------------------
#>