results_pivot_longer {SimNPH} | R Documentation |
Functions for Plotting and Reporting Results
Description
Functions for Plotting and Reporting Results
Usage
results_pivot_longer(data, exclude_from_methods = c("descriptive"))
combined_plot(
data,
methods,
xvars,
yvar,
facet_x_vars = c(),
facet_y_vars = c(),
split_var = 1,
heights_plots = c(3, 1),
scale_stairs = NULL,
grid_level = 2,
scales = "fixed",
hlines = numeric(0),
use_colours = NULL,
use_shapes = NULL
)
Arguments
data |
for results_pivot_longer: simulation result as retured by SimDesign, for combined_plot: simulation results in long format, as returned by |
exclude_from_methods |
"methods" that should not be pivoted into long format |
methods |
methods to include in the plot |
xvars |
orderd vector of variable names to display on the x axis |
yvar |
variable name of the variable to be displayed on the y axis (metric) |
facet_x_vars |
vector of variable names to create columns of facets |
facet_y_vars |
vector of variable names to create rows of facets |
split_var |
where should the lines be split, see details |
heights_plots |
relative heights of the main plot and the stairs on the bottom |
scale_stairs |
this argument is deprecated and will be ignored |
grid_level |
depth of loops for which the grid-lines are drawn |
scales |
passed on to facet_grid |
hlines |
position of horizontal lines, passed as |
use_colours |
optional named vector of colours used in |
use_shapes |
optional named vector of shapes used in |
Details
With exclude_from_methods
descriptive statistics or results of
reference methods can be kept as own columns and used like the columns of
the simulation parameters.
use_colours
and use_shapes
both use the method
variable in their respective aesthetics.
split_var
break the lines after the 1st, 2nd, ... variable in xvars
. Use 0 for one continuous line per method.
Value
dataset in long format with one row per method and scenario and one column per metric
a ggplot/patchwork object containing the plots
Functions
-
results_pivot_longer()
: pivot simulation results into long format -
combined_plot()
: Nested Loop Plot with optional Facets
Examples
data("combination_tests_delayed")
combination_tests_delayed |>
results_pivot_longer() |>
head()
library("ggplot2")
library("patchwork")
data("combination_tests_delayed")
results_long <- results_pivot_longer(combination_tests_delayed)
# plot the rejection rate of two methods
combined_plot(
results_long,
c("logrank", "mwlrt", "maxcombo"),
c("hr", "n_pat_design", "delay", "hazard_ctrl", "recruitment"),
"rejection_0.025",
grid_level=2
)
# use custom colour and shape scales
# this can be used to group methods by shape or colour
# this is also helpful if methods should have the same aesthetics across plots
my_colours <- c(
logrank="black",
mwlrt="blue",
maxcombo="green"
)
my_shapes <- c(
logrank=1,
mwlrt=2,
maxcombo=2
)
combined_plot(
results_long,
c("logrank", "mwlrt", "maxcombo"),
c("hr", "n_pat_design", "delay", "hazard_ctrl", "recruitment"),
"rejection_0.025",
grid_level=2,
use_colours = my_colours,
use_shapes = my_shapes
)
# if one has a dataset of metadata with categories of methods
# one could uses those two definitions
# colours for methods, same shapes for methods of same category
metadata <- data.frame(
method = c("logrank", "mwlrt", "maxcombo"),
method_name = c("logrank test", "modestly weighed logrank test", "maxcombo test"),
category = c("logrank test", "combination test", "combination test")
)
my_colours <- ggplot2::scale_colour_discrete()$palette(n=nrow(metadata)) |>
sample() |>
setNames(metadata$method)
my_shapes <- metadata$category |>
as.factor() |>
as.integer() |>
setNames(metadata$method)
combined_plot(
results_long,
c("logrank", "mwlrt", "maxcombo"),
c("hr", "n_pat_design", "delay", "hazard_ctrl", "recruitment"),
"rejection_0.025",
grid_level=2,
use_colours = my_colours,
use_shapes = my_shapes
)