Plot smooths of a GAMLSS model estimated by GJRM::gamlss

Provides a draw() method for GAMLSS (distributional GAMs) fitted by GJRM::gamlss().

Usage

# S3 method for class 'gamlss'
draw(
  object,
  scales = c("free", "fixed"),
  ncol = NULL,
  nrow = NULL,
  guides = "keep",
  widths = NULL,
  heights = NULL,
  ...,
  n = 100,
  envir = NULL
)

Arguments

object

a model, fitted by GJRM::gamlss()

scales

character; should all univariate smooths be plotted with the same y-axis scale? If scales = "free", the default, each univariate smooth has its own y-axis scale. If scales = "fixed", a common y axis scale is used for all univariate smooths.

Currently does not affect the y-axis scale of plots of the parametric terms.

ncol, nrow

numeric; the numbers of rows and columns over which to spread the plots

guides

character; one of "keep" (the default), "collect", or "auto". Passed to patchwork::plot_layout()

widths, heights

The relative widths and heights of each column and row in the grid. Will get repeated to match the dimensions of the grid. If there is more than 1 plot and widths = NULL, the value of widths will be set internally to widths = 1 to accommodate plots of smooths that use a fixed aspect ratio.

...

arguments passed to draw.gam()

n

numeric; the number of points over the range of the covariate at which to evaluate the smooth.

envir

an optional environment supplying functions and constants used in model expressions. The available model formula environment is used when NULL. Covariate observations should be supplied in data.

Note

Plots of smooths are not labelled with the linear predictor to which they belong.

Examples

if (suppressPackageStartupMessages(require("GJRM", quietly = TRUE))) {
  # follow example from ?GJRM::gamlss
  load_mgcv()
  suppressPackageStartupMessages(library("GJRM"))
  set.seed(0)
  n <- 100
  x1 <- round(runif(n))
  x2 <- runif(n)
  x3 <- runif(n)
  f1 <- function(x) cos(pi * 2 * x) + sin(pi * x)
  y1 <- -1.55 + 2 * x1 + f1(x2) + rnorm(n)
  dataSim <- data.frame(y1, x1, x2, x3)

  eq_mu <- y1 ~ x1 + s(x2)
  eq_s <- ~ s(x3, k = 6)
  fl <- list(eq_mu, eq_s)
  m <- gamlss(fl, data = dataSim)

  draw(m)
}