Create plots of from screened predictNMB simulations.

Create plots of from screened predictNMB simulations.

Usage

# S3 method for predictNMBscreen
autoplot(
  object,
  x_axis_var = NULL,
  constants = list(),
  what = c("nmb", "inb", "cutpoints", "qalys", "costs"),
  inb_ref_col = NA,
  plot_range = TRUE,
  plot_conf_level = TRUE,
  plot_line = TRUE,
  plot_alpha = 0.5,
  dodge_width = 0,
  conf.level = 0.95,
  methods_order = NULL,
  rename_vector,
  ...
)

Arguments

object

A predictNMBscreen object.

x_axis_var

The desired screened factor to be displayed along the x axis. For example, if the simulation screen was used with many values for event rate, this could be "event_rate". Defaults to the first detected, varied input.

constants

Named vector. If multiple inputs were screened in this object, this argument can be used to modify the selected values for all those except the input that's varying along the x-axis. See the summarising methods vignette.

what

What to summarise: one of "nmb", "inb", "cutpoints", "qalys" or "costs". Defaults to "nmb".

inb_ref_col

Which cutpoint method to use as the reference strategy when calculating the incremental net monetary benefit. See do_nmb_sim for more information.

plot_range

logical. Whether or not to plot the range of the distribution as a thin line. Defaults to TRUE.

plot_conf_level

logical. Whether or not to plot the confidence region of the distribution as a thicker line. Defaults to TRUE.

plot_line

logical. Whether or not to connect the medians of the distributions for each method along the x-axis. Defaults to TRUE.

plot_alpha

Alpha value (transparency) of all plot elements. Defaults to 0.5.

dodge_width

The dodge width of plot elements. Can be used to avoid excessive overlap between methods. Defaults to 0.

conf.level

The confidence level of the interval. Defaults to 0.95 (coloured area of distribution represents 95% CIs).

methods_order

The order (left to right) to display the cutpoint methods.

rename_vector

A named vector for renaming the methods in the summary. The values of the vector are the default names and the names given are the desired names in the output.

...

Additional (unused) arguments.

Value

Returns a ggplot object.

Details

This plot method works with predictNMBscreen objects that are created using screen_simulation_inputs(). Can be used to visualise distributions from many different simulations and assign a varying input to the x-axis of the plot.

Examples

# \donttest{
get_nmb <- function() c("TP" = -3, "TN" = 0, "FP" = -1, "FN" = -4)
sim_screen_obj <- screen_simulation_inputs(
  n_sims = 50, n_valid = 10000, sim_auc = seq(0.7, 0.9, 0.1),
  event_rate = c(0.1, 0.2, 0.3),
  fx_nmb_training = get_nmb, fx_nmb_evaluation = get_nmb,
  cutpoint_methods = c("all", "none", "youden", "value_optimising")
)

autoplot(sim_screen_obj)
#> No value for 'x_axis_var' given.
#> Screening over sim_auc by default. Specify the variable in the 'x_axis_var' argument if you want to plot changes over:
#> event_rate
#> 
#> 
#> Varying simulation inputs, other than sim_auc, are being held constant:
#> event_rate: 0.1


autoplot(
  sim_screen_obj,
  x_axis_var = "event_rate",
  constants = c(sim_auc = 0.8),
  dodge_width = 0.02,
  rename_vector = c(
    "Value-Optimising" = "value_optimising",
    "Treat-None" = "none",
    "Treat-All" = "all",
    "Youden Index" = "youden"
  )
)
#> 
#> 
#> Varying simulation inputs, other than event_rate, are being held constant:
#> sim_auc: 0.8

# }