plot_regression: Visually Weighted Regression Plot

In microbiome/microbiome: Microbiome Analytics

Visually Weighted Regression Plot

Description

Draw regression curve with smoothed error bars with Visually-Weighted Regression by Solomon M. Hsiang; see http://www.fight-entropy.com/2012/07/visually-weighted-regression.html The R is modified from Felix Schonbrodt's original code http://www.nicebread.de/ visually-weighted-watercolor-plots-new-variants-please-vote

Usage

plot_regression(
  formula,
  data,
  B = 1000,
  shade = TRUE,
  shade.alpha = 0.1,
  spag = FALSE,
  mweight = TRUE,
  show.lm = FALSE,
  show.median = TRUE,
  median.col = "white",
  show.CI = FALSE,
  method = loess,
  slices = 200,
  ylim = NULL,
  quantize = "continuous",
  show.points = TRUE,
  color = NULL,
  pointsize = NULL,
  ...
)

Arguments

formula	formula
data	data
B	number bootstrapped smoothers
shade	plot the shaded confidence region?
shade.alpha	shade.alpha: should the CI shading fade out at the edges? (by reducing alpha; 0=no alpha decrease, 0.1=medium alpha decrease, 0.5=strong alpha decrease)
spag	plot spaghetti lines?
mweight	visually weight the median smoother
show.lm	plot the linear regression line
show.median	show median smoother
median.col	median color
show.CI	should the 95% CI limits be plotted?
method	the fitting function for the spaghettis; default: loess
slices	number of slices in x and y direction for the shaded region. Higher numbers make a smoother plot, but takes longer to draw. I wouldn't go beyond 500
ylim	restrict range of the watercoloring
quantize	either 'continuous', or 'SD'. In the latter case, we get three color regions for 1, 2, and 3 SD (an idea of John Mashey)
show.points	Show points.
color	Point colors
pointsize	Point sizes
...	further parameters passed to the fitting function, in the case of loess, for example, 'span=.9', or 'family='symmetric”

Value

ggplot2 object

Author(s)

Based on the original version from F. Schonbrodt. Modified by Leo Lahti [e-mail]

References

See citation('microbiome')

Examples

data(atlas1006)
pseq <- subset_samples(atlas1006,
   DNA_extraction_method == 'r' &
   sex == "female" &
   nationality == "UKIE",
   B=10, slices=10 # non-default used here to speed up examples
   )
p <- plot_regression(diversity ~ age, meta(pseq)[1:20,], slices=10, B=10)

microbiome/microbiome documentation built on Aug. 22, 2023, 7:12 a.m.