Create watercolor plot to visualize weighted regression.
Usage
vwReg(
formula,
data,
title = "",
B = 1000,
shade = TRUE,
shade.alpha = 0.1,
spag = FALSE,
spag.color = "darkblue",
mweight = TRUE,
show.lm = FALSE,
show.median = TRUE,
median.col = "white",
shape = 21,
show.CI = FALSE,
method = loess,
bw = FALSE,
slices = 200,
palette = colorRampPalette(c("#FFEDA0", "#DD0000"), bias = 2)(20),
ylim = NULL,
quantize = "continuous",
add = FALSE,
...
)
Arguments
- formula
regression model.
- data
dataset.
- title
plot title.
- B
number of bootstrapped smoothers.
- shade
whether to plot the shaded confidence region.
- shade.alpha
whether to fade out the confidence interval shading at the edges (by reducing alpha; 0 = no alpha decrease, 0.1 = medium alpha decrease, 0.5 = strong alpha decrease).
- spag
whether to plot spaghetti lines.
- spag.color
the fitting function for the spaghettis; default:
loess
.- mweight
logical indicating whether to make the median smoother visually weighted.
- show.lm
logical indicating whether to plot the linear regression line.
- show.median
logical indicating whether to plot the median smoother.
- median.col
color of the median smoother.
- shape
shape of points.
- show.CI
logical indicating whether to plot the 95% confidence interval limits.
- method
color of spaghetti lines.
- bw
logical indicating whether to use a b&w palette; default:
TRUE
.- 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 would not set
slices
to more than 500.- palette
provide a custom color palette for the watercolors.
- ylim
restrict range of the watercoloring.
- quantize
either
continuous
, orSD
. In the latter case, we get three color regions for 1, 2, and 3 SD (an idea of John Mashey).- add
if
add == FALSE
, a new ggplot is returned. Ifadd == TRUE
, only the elements are returned, which can be added to an existing ggplot (with the+
operator).- ...
further parameters passed to the fitting function, in the case of loess, for example,
span = .9
, orfamily = "symmetric"
.
See also
https://www.nicebread.de/visually-weighted-regression-in-r-a-la-solomon-hsiang/
https://www.nicebread.de/visually-weighted-watercolor-plots-new-variants-please-vote/
http://www.fight-entropy.com/2012/07/visually-weighted-regression.html
http://www.fight-entropy.com/2012/08/visually-weighted-confidence-intervals.html
http://www.fight-entropy.com/2012/08/watercolor-regression.html
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2265501
Other plot:
addText()
,
plot2WayInteraction()
,
ppPlot()
,
semPlotInteraction()
Other correlations:
addText()
,
cor.table()
,
crossTimeCorrelation()
,
crossTimeCorrelationDF()
,
partialcor.table()
Examples
# \donttest{
# Prepare Data
data("mtcars")
df <- data.frame(x = mtcars$hp, y = mtcars$mpg)
## Visually Weighted Regression
# Default
vwReg(y ~ x, df)
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
# Shade
vwReg(y ~ x, df, shade = TRUE, show.lm = TRUE, show.CI = TRUE,
quantize = "continuous")
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
#> `geom_smooth()` using formula = 'y ~ x'
vwReg(y ~ x, df, shade = TRUE, show.lm = TRUE, show.CI = TRUE,
quantize = "SD")
#> Computing boostrapped smoothers ...
#> Build ggplot figure ...
#> `geom_smooth()` using formula = 'y ~ x'
# Spaghetti
vwReg(y ~ x, df, shade = FALSE, spag = TRUE, show.lm = TRUE, show.CI = TRUE)
#> Computing boostrapped smoothers ...
#> Build ggplot figure ...
#> `geom_smooth()` using formula = 'y ~ x'
#> Warning: Removed 21579 rows containing missing values or values outside the scale range
#> (`geom_path()`).
vwReg(y ~ x, df, shade = FALSE, spag = TRUE)
#> Computing boostrapped smoothers ...
#> Build ggplot figure ...
#> Warning: Removed 21838 rows containing missing values or values outside the scale range
#> (`geom_path()`).
# Black/white
vwReg(y ~ x, df, shade = TRUE, spag = FALSE, show.lm = TRUE, show.CI = TRUE,
bw = TRUE, quantize = "continuous")
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
#> `geom_smooth()` using formula = 'y ~ x'
vwReg(y ~ x, df, shade = TRUE, spag = FALSE, show.lm = TRUE, show.CI = TRUE,
bw = TRUE, quantize = "SD")
#> Computing boostrapped smoothers ...
#> Build ggplot figure ...
#> `geom_smooth()` using formula = 'y ~ x'
vwReg(y ~ x, df, shade = FALSE, spag = TRUE, show.lm = TRUE, show.CI = TRUE,
bw = TRUE, quantize = "SD")
#> Computing boostrapped smoothers ...
#> Build ggplot figure ...
#> `geom_smooth()` using formula = 'y ~ x'
#> Warning: Removed 23444 rows containing missing values or values outside the scale range
#> (`geom_path()`).
# Change the bootstrap smoothing
vwReg(y ~ x, df, family = "symmetric") # use an M-estimator for
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
# bootstrap smoothers. Usually yields wider confidence intervals
vwReg(y ~ x, df, span = 1.7) # increase the span of the smoothers
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
vwReg(y ~ x, df, span = 0.5) # decrease the span of the smoothers
#> Computing boostrapped smoothers ...
#> Warning: pseudoinverse used at 175
#> Warning: neighborhood radius 25
#> Warning: reciprocal condition number 1.1502e-16
#> Warning: pseudoinverse used at 175
#> Warning: neighborhood radius 25
#> Warning: reciprocal condition number 1.5592e-16
#> Warning: There are other near singularities as well. 900
#> Warning: pseudoinverse used at 123
#> Warning: neighborhood radius 27
#> Warning: reciprocal condition number 1.1268e-16
#> Warning: pseudoinverse used at 109
#> Warning: neighborhood radius 14
#> Warning: reciprocal condition number 0
#> Warning: pseudoinverse used at 113
#> Warning: neighborhood radius 10
#> Warning: reciprocal condition number 5.9634e-17
#> Warning: There are other near singularities as well. 169
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
# Change the color scheme
vwReg(y ~ x, df, palette = viridisLite::viridis(4)) # viridis
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
vwReg(y ~ x, df, palette = viridisLite::magma(4)) # magma
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
vwReg(y ~ x, df, palette = RColorBrewer::brewer.pal(9, "YlGnBu")) # change the
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
# color scheme, using a predefined ColorBrewer palette. You can see all
# available palettes by using this command:
# `library(RColorBrewer); display.brewer.all()`
vwReg(y ~ x, df, palette = grDevices::colorRampPalette(c("white","yellow",
"green","red"))(20)) # use a custom-made palette
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
vwReg(y ~ x, df, palette = grDevices::colorRampPalette(c("white","yellow",
"green","red"), bias = 3)(20)) # use a custom-made palette, with the
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
# parameter bias you can shift the color ramp to the “higher” colors
vwReg(y ~ x, df, bw = TRUE) # black and white version
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
vwReg(y ~ x, df, shade.alpha = 0, palette = grDevices::colorRampPalette(
c("black","grey30","white"), bias = 4)(20)) # Milky-Way Plot
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
vwReg(y ~ x, df, shade.alpha = 0, slices = 400, palette =
grDevices::colorRampPalette(c("black","green","yellow","red"),
bias = 5)(20), family = "symmetric") # Northern Light Plot/ fMRI plot
#> Computing boostrapped smoothers ...
#> Computing density estimates for each vertical cut ...
#>
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#> Build ggplot figure ...
vwReg(y ~ x, df, quantize = "SD") # 1-2-3-SD plot
#> Computing boostrapped smoothers ...
#> Build ggplot figure ...
# }