mgcViz (version 0.2.1)

zoom.qqGam: Efficiently zooming on GAM QQ-plots

Description

This function allows to zoom into a QQ-plot produced by qq.gamViz, in a computationally efficient manner.

Usage

# S3 method for qqGam
zoom(
  o,
  xlim = NULL,
  ylim = NULL,
  discrete = NULL,
  ngr = 1000,
  adGrid = TRUE,
  CI = FALSE,
  worm = FALSE,
  showReps = FALSE,
  a.qqpoi = list(),
  a.ablin = list(),
  a.cipoly = list(),
  a.replin = list(),
  ...
)

Arguments

o

the output of mgcViz::qq.gamViz.

xlim

if supplied then this pair of numbers are used as the x limits for the plot.

ylim

if supplied then this pair of numbers are used as the y limits for the plot.

discrete

if TRUE the QQ-plot is discretized into ngr bins before plotting, in order to save plotting time (when the number of observations is large). If left to NULL, the discretization is used if there are more than 10^4 observations.

ngr

number of bins to be used in the discretization.

adGrid

if TRUE the discretization grid is computed using the QQ-points falling within xlim. If FALSE, zoom.qqGam will compute ngr values using all the QQ-points used in the original qq.gamViz call (but only those falling within xlim and ylim will be plotted).

CI

if TRUE confidence intervals are plotted.

worm

if TRUE a worm-plot (a de-trended QQ-plot) is plotted, rather than a QQ-plot.

showReps

if TRUE all the QQ-lines corresponding to the simulated (model-based) QQ-plots.

a.qqpoi

list of arguments to be passed to ggplot2::geom_point, which plots the main QQ-plot.

a.ablin

list of arguments to be passed to ggplot2::geom_abline, which adds the reference line.

a.cipoly

list of arguments to be passed to ggplot2::geom_polygon, which add the confidence intervals.

a.replin

list of arguments to be passed to ggplot2::geom_line, which adds a line for each simulated QQ-plot.

...

currently unused.

Examples

library(mgcViz);
set.seed(0)
n.samp <- 500
dat <- gamSim(1,n=n.samp,dist="binary",scale=.33)
p <- binomial()$linkinv(dat$f) ## binomial p
n <- sample(c(1,3),n.samp,replace=TRUE) ## binomial n
dat$y <- rbinom(n,n,p)
dat$n <- n
lr.fit <- bam(y/n ~ s(x0) + s(x1) + s(x2) + s(x3)
              , family = binomial, data = dat,
              weights = n, method = "REML")
lr.fit <- getViz(lr.fit)

set.seed(414)
o <- qq(lr.fit, rep = 50, method = "simul1", CI = "normal")
o # This is the whole qqplot

# We can zoom in along x at little extra costs (most computation already done by qq.gamViz)
zoom(o, xlim = c(0, 1), showReps = TRUE, 
     a.replin = list(alpha = 0.1), a.qqpoi =  list(shape = 19))