Correlation Analysis with Significance

Computes correlation matrix with significance levels and generates publication-ready correlation table and plot.

Usage

correlation_analysis(
  data,
  method = "pearson",
  plot = TRUE,
  digits = 3,
  verbose = TRUE
)

Arguments

data

Data frame with numeric variables

method

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

plot

Logical, whether to generate correlation plot

digits

Number of decimal places

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List with correlation matrix and significance matrix

Author

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- data.frame(
  yield = c(1200, 1350, 1100, 1450, 1280),
  wue = c(4.2, 4.8, 3.9, 5.1, 4.5),
  protein = c(22.1, 23.5, 21.8, 24.2, 22.9)
)
correlation_analysis(data)
#> 
#> ==========================================================
#>           CORRELATION ANALYSIS
#> ==========================================================
#> Method: pearson 
#> Number of observations: 5 
#> Number of variables: 3 
#> 
#> --- Correlation Matrix ---
#> 
#>         yield   wue protein
#> yield   1.000 0.998   0.987
#> wue     0.998 1.000   0.993
#> protein 0.987 0.993   1.000
#> 
#> --- Significance Levels ---
#> *** p < 0.001, ** p < 0.01, * p < 0.05, . p < 0.1