regression_vis: Correlation and cross product visualization
In jbryer/VisualStats: Visualizations for Statistical Tests

regression_vis

R Documentation

Correlation and cross product visualization

Description

Correlation and cross product visualization

Usage

regression_vis(
  df,
  x_var = names(df)[1],
  y_var = names(df)[2],
  plot_x_mean = TRUE,
  plot_y_mean = TRUE,
  plot_regression = FALSE,
  regression_line_color = "grey30",
  plot_x_deviations = NULL,
  plot_y_deviations = NULL,
  x_deviation_color = "darkblue",
  y_deviation_color = "darkblue",
  deviation_size = 1.5,
  plot_positive_cross_products = FALSE,
  plot_negative_cross_products = FALSE,
  plot_cross_products = NULL,
  positive_cross_product_fill = "lightblue",
  negative_cross_product_fill = "darkred",
  positive_cross_product_color = cross_product_color,
  negative_cross_product_color = cross_product_color,
  cross_product_color = "grey30",
  cross_product_fill = "#F5C710",
  cross_product_alpha = 0.1,
  plot_residuals = NULL,
  plot_residuals_squared = NULL,
  residuals_squared_color = "grey30",
  residuals_squared_fill = "green",
  residuals_squared_alpha = 0.1,
  residual_color = "darkred",
  residual_size = 1.5
)

Arguments

`df`	data.frame with the data to plot.
`x_var`	the name of the variable for the x-axis.
`y_var`	the name of the variable fro teh y-axis.
`plot_x_mean`	plot a line for the mean of x.
`plot_y_mean`	plot a line for the mean of y.
`plot_regression`	whether to plot the linear regression line.
`regression_line_color`	color for the regression line.
`plot_x_deviations`	either a logical or numerical vector for the rows in `df` to plot the x deviations.
`plot_y_deviations`	either a logical or numerical vector for the rows in `df` to plot the y deviations.
`x_deviation_color`	the color of the line for x deviations.
`y_deviation_color`	the color of the line for y deviations.
`deviation_size`	of the line when plotting deviations.
`plot_positive_cross_products`	plot all the cross products in the 1st and 3rd quadrants.
`plot_negative_cross_products`	plot all the cross products in the 2nd and 4th quandrants.
`plot_cross_products`	either a logicial or numerical vector for the rows in `df` to plot the cross products.
`positive_cross_product_fill`	the fill for the positive cross products.
`negative_cross_product_fill`	the fill for the negative cross products.
`positive_cross_product_color`	the color of the square for positive cross products.
`negative_cross_product_color`	the color of the square for negative cross products.
`cross_product_color`	the color of the square for cross products.
`cross_product_fill`	the fill for the cross products plotted by `plot_cross_products`.
`cross_product_alpha`	the alpha level (transparency) for the cross product fill.
`plot_residuals`	whether to plot the residuals.
`plot_residuals_squared`	whether to plot the squared residuals.
`residuals_squared_color`	color for the residuals.
`residuals_squared_fill`	fill color for the squared residuals.
`residuals_squared_alpha`	alpha (transparency) level for the squared residuals.
`residual_color`	color for the residuals.
`residual_size`	size of the residuals.

Value

a ggplot2 expression.

Examples

df <- mtcars[,c('wt', 'mpg')]
cross_products <- abs(df[,1] * df[,2])
cross_products == max(cross_products) # Find the largest cross product

# Scatter plot with arrows showing the largest cross product
regression_vis(df,
				plot_x_mean = TRUE,
				plot_y_mean = TRUE,
				plot_positive_cross_products = FALSE,
				plot_negative_cross_products = FALSE,
				plot_x_deviations = cross_products == max(cross_products),
				plot_y_deviations = cross_products == max(cross_products))

# Scatter plot with the largest cross product as a rectangl
regression_vis(df,
				plot_x_mean = TRUE,
				plot_y_mean = TRUE,
				plot_positive_cross_products = FALSE,
				plot_negative_cross_products = FALSE,
				plot_cross_products = cross_products == max(cross_products),
				cross_product_alpha = 0.5)

# Scatter plot with all the cross products.
regression_vis(df,
				plot_x_mean = TRUE,
				plot_y_mean = TRUE,
				plot_positive_cross_products = TRUE,
				plot_negative_cross_products = TRUE)

jbryer/VisualStats documentation built on Feb. 27, 2025, 6:19 p.m.