rsimsum (version 0.13.0)

autoplot.simsum: autoplot method for simsum objects

Description

autoplot can produce a series of plot to summarise results of simulation studies. See vignette("C-plotting", package = "rsimsum") for further details.

Usage

# S3 method for simsum
autoplot(
  object,
  type = "forest",
  stats = "nsim",
  target = NULL,
  fitted = TRUE,
  scales = "fixed",
  top = TRUE,
  density.legend = TRUE,
  zoom = 1,
  zip_ci_colours = "yellow",
  ...
)

Value

A ggplot object.

Arguments

object

An object of class simsum.

type

The type of the plot to be produced. Possible choices are: forest, lolly, zip, est, se, est_ba, se_ba, est_ridge, se_ridge, est_density, se_density, est_hex, se_hex, heat, nlp, with forest being the default.

stats

Summary statistic to plot, defaults to nsim (the number of replications with non-missing point estimates/SEs). See summary.simsum() for further details on supported summary statistics.

target

Target of summary statistic, e.g. 0 for bias. Defaults to NULL, in which case target will be inferred.

fitted

Superimpose a fitted regression line, useful when type = (est, se, est_ba, se_ba, est_density, se_density, est_hex, se_hex). Defaults to TRUE.

scales

Should scales be fixed (fixed, the default), free (free), or free in one dimension (free_x, free_y)?

top

Should the legend for a nested loop plot be on the top side of the plot? Defaults to TRUE.

density.legend

Should the legend for density and hexbin plots be included? Defaults to TRUE.

zoom

A numeric value between 0 and 1 signalling that a zip plot should zoom on the top x% of the plot (to ease interpretation). Defaults to 1, where the whole zip plot is displayed.

zip_ci_colours

A string with (1) a hex code to use for plotting coverage probability and its Monte Carlo confidence intervals (the default, with value zip_ci_colours = "yellow"), (2) a string vector of two hex codes denoting optimal coverage (first element) and over/under coverage (second element) or (3) a vector of three hex codes denoting optimal coverage (first), undercoverage (second), and overcoverage (third).

...

Not used.

Examples

data("MIsim", package = "rsimsum")
s <- rsimsum::simsum(
  data = MIsim, estvarname = "b", true = 0.5, se = "se",
  methodvar = "method", x = TRUE
)

library(ggplot2)
autoplot(s)
autoplot(s, type = "lolly")
autoplot(s, type = "est_hex")
autoplot(s, type = "zip", zoom = 0.5)

# Nested loop plot:
data("nlp", package = "rsimsum")
s1 <- rsimsum::simsum(
  data = nlp, estvarname = "b", true = 0, se = "se",
  methodvar = "model", by = c("baseline", "ss", "esigma")
)
autoplot(s1, stats = "bias", type = "nlp")