Plot a scatter plot with marginals. xvar is the independent variable (input or model) and yvar is the dependent variable

Plot a scatter plot with marginals. xvar is the independent variable (input or model) and yvar is the dependent variable

ScatterHist(frame, xvar, yvar, title, ..., smoothmethod = "auto",
  annot_size = 5, minimal_labels = TRUE, binwidth_x = NULL,
  binwidth_y = NULL, adjust_x = 1, adjust_y = 1)

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' (the default), 'loess', 'gam', 'lm', or 'identity'. If smoothmethod is 'auto' or 'lm' a smoothing curve or line (respectively) is added and R-squared of the best linear fit of xvar to yvar is reported. If smoothmethod is 'identity' then the y=x line is added and the R-squared of xvar to yvar (without the linear transform used in the other smoothmethod modes) is reported.
annot_size

numeric scale annotation text (if present)
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

Examples


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")
#> `geom_smooth()` using method = 'loess'
#> `geom_smooth()` using method = 'loess'