l_glyphs2D: Adding glyphs to 2D plots

In mfasiolo/mgcViz: Visualisations for Generalized Additive Models

Adding glyphs to 2D plots

Description

This layer adds glyphs or subplots to 2D plots. It is mainly meant to be used with check2D and to produce residuals checks.

Usage

l_glyphs2D(
  glyFun,
  ggLay = "geom_points",
  n = c(4, 4),
  mapping = NULL,
  data = NULL,
  polar = FALSE,
  height = ggplot2::rel(0.95),
  width = ggplot2::rel(0.95),
  y_scale = I,
  x_scale = I,
  ...
)

Arguments

glyFun	the function that produces the data needed to construct the glyphs. It will take a single argument (.d), which is a data.frame with columns "x", "y" and "z". When l_glyphs2D is used with check2D, then "x" and "y" will be the locations of the residual "z" in the relevant covariates. glyFun needs to output a data.frame that will be passed to the ggLay function, which does the plotting.
ggLay	the ggplot2 layer function (such as "geom_point") used to plot the glyphs. Its mapping needs to take at least argument "x", "y" and "group". See the mapping argument below.
n	vector of two positive integers, indicating the number of 2D grid cell along x and y in which the data is divided.
mapping	list of aesthetic mappings to be used by ggLay. By default it is aes(x=gx, y=gy, group = gid). Here gx and gy specify the x-y location of each data-point used to plot the glyphs, while gid specifies to which glyph each data-point belongs (there are n[1]*n[2] glyphs).
data	an optional data.frame to be used for computing the glyphs. It must have two variables called x and y. If left to NULL then the glyphs will be computed using the data in the plotSmooth object to which this layer is being added.
polar, height, width, y_scale, x_scale	see GGally::glyphs.
...	graphical arguments to be passed to ggLay function.

Value

An object of class gamLayer.

See Also

check2D.

Examples

library(mgcViz);
set.seed(4124)
n <- 1e4
dat <- data.frame("x1" = rnorm(n), "x2" = rnorm(n))

# Residuals are heteroscedastic w.r.t. x1
dat$y <- (dat$x1)^2 + (dat$x2)^2 + (1*abs(dat$x1) + 1)  * rnorm(n)
b <- bam(y ~ s(x1,k=30) + s(x2, k=30), data = dat, discrete = TRUE)
b <- getViz(b)

pl <- check2D(b, x1 = "x1", x2 = "x2", type = "tnormal") + 
  l_points(colour = "blue", alpha = 0.5)

# Look at distributions of residuals across x1 and x2
# Approach 1: using binned kernel density estimate
# Colour indicates whether we have more that 50 obs in that bin
glyFun <- function(.d){
  .r <- .d$z
  .qq <- as.data.frame( density(.r)[c("x", "y")], n = 100 )
  .qq$colour <- rep(ifelse(length(.r)>50, "black", "red"), nrow(.qq))
  return( .qq )
}

pl + l_glyphs2D(glyFun = glyFun, ggLay = "geom_path", n = c(8, 8),
                 mapping = aes(x=gx, y=gy, group = gid, colour = I(colour)), 
                 height=1.5, width = 1) 

# Approach 2: using binned worm-plots. These are simply rotated QQplots.
# An horizontal plot indicates well specified residual model. 
# Increasing (decreasing) worm indicates over (under) dispersion
glyFun <- function(.d){
  n <- nrow(.d)
  px <- qnorm( (1:n - 0.5)/(n) )
  py <- sort( .d$z )
  clr <- if(n > 50) { "black" } else { "red" }
  clr <- rep(clr, n)
  return( data.frame("x" = px, "y" = py - px, "colour" = clr))
}

pl + l_glyphs2D(glyFun = glyFun, ggLay = "geom_point", n = c(10, 10),
                mapping = aes(x=gx, y=gy, group = gid, colour = I(colour)),
                height=2, width = 1, size = 0.2) 

mfasiolo/mgcViz documentation built on April 14, 2024, 4:20 a.m.