vis_numeric: Visualize Numeric Relationships: Regression or Correlation...

In visStatistics: Automated Selection and Visualisation of Statistical Hypothesis Tests

Visualize Numeric Relationships: Regression or Correlation Analysis

Description

This function provides unified visualization for numeric relationships between two continuous variables. It can perform either regression analysis (with confidence and prediction bands) or Spearman rank correlation analysis with appropriate visualizations and statistical output. For regression, statistical assumptions are checked and warnings are issued if violated, but analysis proceeds.

Usage

vis_numeric(
  y,
  x,
  correlation = FALSE,
  conf.level = 0.95,
  name_of_factor = character(),
  name_of_sample = character()
)

Arguments

y	Numeric vector. The response variable (dependent variable) for regression analysis, or the y-axis variable for correlation analysis.
x	Numeric vector. The predictor variable (independent variable) for regression analysis, or the x-axis variable for correlation analysis. Must have the same length as y.
correlation	Logical. If FALSE (default), performs regression analysis with confidence and prediction bands. If TRUE, performs Spearman rank correlation analysis.
conf.level	Numeric. Confidence level for statistical tests and intervals. Must be between 0 and 1. Default is 0.95 (95 percent confidence level).
name_of_factor	Character string. Label for the x-axis (independent variable). If empty, defaults to the variable name.
name_of_sample	Character string. Label for the y-axis (dependent variable). If empty, defaults to the variable name.

Details

Statistical Assumptions Checked: Regression: Normality of residuals (Shapiro-Wilk test) and homoscedasticity (Breusch-Pagan test). All regression analyses proceed even if assumptions are violated, but appropriate warnings are issued. Correlation: Spearman rank correlation requires no distributional assumptions.

Value

A list containing analysis results and assumption checks. Content depends on analysis type. For regression analysis: analysis_type, summary_regression, assumptions, warnings, r_squared, adj_r_squared. For correlation analysis: analysis_type, correlation_test, correlation_coefficient, assumptions, warnings, method_used.

Author(s)

Sabine Schilling

See Also

cor.test, lm, shapiro.test

Examples

## Not run: 
# Generate sample data
set.seed(123)
x <- rnorm(50, mean = 10, sd = 2)
y <- 2 * x + rnorm(50, mean = 0, sd = 1)

# Regression analysis (default)
result1 <- vis_numeric(y, x, 
                      name_of_factor = "Predictor", 
                      name_of_sample = "Response")

# Spearman rank correlation
result2 <- vis_numeric(y, x, correlation = TRUE)

## End(Not run)

visStatistics documentation built on May 13, 2026, 1:08 a.m.