ScatterHist: Plot a scatter plot with marginals.

Description

Plot a scatter plot with optional smoothing curves or contour lines, and marginal histogram/density plots. Based on https://win-vector.com/2015/06/11/wanted-a-perfect-scatterplot-with-marginals/. See also ggExtra::ggMarginal.

Usage

ScatterHist(
  frame,
  xvar,
  yvar,
  title,
  ...,
  smoothmethod = "lm",
  estimate_sig = FALSE,
  minimal_labels = TRUE,
  binwidth_x = NULL,
  binwidth_y = NULL,
  adjust_x = 1,
  adjust_y = 1,
  point_alpha = 0.5,
  contour = FALSE,
  point_color = "black",
  hist_color = "gray",
  smoothing_color = "blue",
  density_color = "blue",
  contour_color = "blue"
)

Arguments

frame

data frame to get values from

xvar

name of the independent (input or model) column in frame

yvar

name of the dependent (output or result to be modeled) column in frame

title

title to place on plot

...

no unnamed argument, added to force named binding of later arguments.

smoothmethod

(optional) one of 'auto', 'loess', 'gam', 'lm', 'identity', or 'none'.

estimate_sig

logical if TRUE and smoothmethod is 'identity' or 'lm', report goodness of fit and significance of relation.

minimal_labels

logical drop some annotations

binwidth_x

numeric binwidth for x histogram

binwidth_y

numeric binwidth for y histogram

adjust_x

numeric adjust x density plot

adjust_y

numeric adjust y density plot

point_alpha

numeric opaqueness of the plot points

contour

logical if TRUE add a 2d contour plot

point_color

color for scatter plots

hist_color

fill color for marginal histograms

smoothing_color

color for smoothing line

density_color

color for marginal density plots

contour_color

color for contour plots

Value

plot grid

Details

If smoothmethod is:

'auto', 'loess' or 'gam': the appropriate smoothing curve is added to the scatterplot.
'lm' (the default): the best fit line is added to the scatterplot.
'identity': the line x = y is added to the scatterplot. This is useful for comparing model predictions to true outcome.
'none': no smoothing line is added to the scatterplot.

If estimate_sig is TRUE and smoothmethod is:

'lm': the R-squared of the linear fit is reported.
'identity': the R-squared of the exact relation between xvar and yvar is reported.

Note that the identity R-squared is NOT the square of the correlation between xvar and yvar (which includes an implicit shift and scale). It is the coefficient of determination between xvar and yvar, and can be negative. See https://en.wikipedia.org/wiki/Coefficient_of_determination for more details. If xvar is the output of a model to predict yvar, then the identity R-squared, not the lm R-squared, is the correct measure.

If smoothmethod is neither 'lm' or 'identity' then estimate_sig is ignored.

Examples

Run this code

# NOT RUN {
set.seed(34903490)
x = rnorm(50)
y = 0.5*x^2 + 2*x + rnorm(length(x))
frm = data.frame(x=x,y=y)
WVPlots::ScatterHist(frm, "x", "y",
  title= "Example Fit",
  smoothmethod = "gam",
  contour = TRUE)

# Same plot with custom colors
WVPlots::ScatterHist(frm, "x", "y",
  title= "Example Fit",
  smoothmethod = "gam",
  contour = TRUE,
  point_color = "#006d2c", # dark green
  hist_color = "#6baed6", # medium blue
  smoothing_color = "#54278f", # dark purple
  density_color = "#08519c", # darker blue
  contour_color = "#9e9ac8") # lighter purple
# }