Plots calibration scatter plots for objects of class calib_mlr estimated using using calib_msm.

Plots calibration scatter plots for the transition probabilities of a multistate model estimated using the MLR-IPCW approach.

Usage

# S3 method for class 'calib_mlr'
plot(
  x,
  ...,
  combine = TRUE,
  ncol = NULL,
  nrow = NULL,
  size_point = 0.5,
  size_text = 12,
  transparency_plot = 0.25,
  marg_density = FALSE,
  marg_density_size = 5,
  marg_density_type = "density",
  marg_rug = FALSE,
  marg_rug_transparency = 0.1,
  titles_include = TRUE,
  titles = NULL,
  axis_titles_x = NULL,
  axis_titles_text_x = "Predicted risk",
  axis_titles_y = NULL,
  axis_titles_text_y = "Predicted-observed risk"
)

Arguments

x: Object of class calib_mlr generated from calib_msm
...: Other
combine: Whether to combine into one plot using ggarrange, or return as a list of individual plots
ncol: Number of columns for combined calibration plot
nrow: Number of rows for combined calibration plot
size_point: Size of points in scatter plot
size_text: Size of text in plot
transparency_plot: Degree of transparency for points in the calibration scatter plot
marg_density: Whether to produce marginal density plots TRUE/FALSE
marg_density_size: Size of the main plot relative to the density plots (see ggMarginal)
marg_density_type: What type of marginal plot to show (see ggMarginal)
marg_rug: Whether to produce marginal rug plots TRUE/FALSE
marg_rug_transparency: Degree of transparency for the density rug plot along each axis
titles_include: Whether to include titles for each individual calibration plots
titles: Vector of titles for the calibration plots_ Defaults to "State k" for each plot_
axis_titles_x: Position of plots for which to include title on x-axis
axis_titles_text_x: x-axis title
axis_titles_y: Position of plots for which to include title on y-axis
axis_titles_text_y: y-axis title

Value

If combine = TRUE, returns an object of classes gg, ggplot, and ggarrange, as all ggplots have been combined into one object. If combine = FALSE, returns an object of class list, each element containing an object of class gg and ggplot.

Examples

# Using competing risks data out of initial state (see vignette: ... -in-competing-risk-setting).
# Estimate and plot MLR-IPCW calibration scatter plots for the predicted transition
# probabilities at time t = 1826, when predictions were made at time
# s = 0 in state j = 1. These predicted transition probabilities are stored in tp_cmprsk_j0.

# To minimise example time we reduce the datasets to 150 individuals.
# Extract the predicted transition probabilities out of state j = 1 for first 150 individuals
tp_pred <- tp_cmprsk_j0 |>
 dplyr::filter(id %in% 1:150) |>
 dplyr::select(any_of(paste("pstate", 1:6, sep = "")))
# Reduce ebmtcal to first 150 individuals
ebmtcal <- ebmtcal |> dplyr::filter(id %in% 1:150)
# Reduce msebmtcal_cmprsk to first 150 individuals
msebmtcal_cmprsk <- msebmtcal_cmprsk |> dplyr::filter(id %in% 1:150)

# Now estimate the observed event probabilities for each possible transition.
dat_calib <-
calib_msm(data_ms = msebmtcal_cmprsk,
 data_raw = ebmtcal,
 j=1,
 s=0,
 t = 1826,
 tp_pred = tp_pred,
 calib_type = "mlr",
 w_covs = c("year", "agecl", "proph", "match"),
 mlr_ps_int = 2,
 mlr_degree = 2)
#> Warning: In the landmark cohort of individuals uncensored and in state j at time s,
#>     there are some states have less than 50 people at the time at which calibration is being assessed (t).
#>     Warnings and errors may occur when the models are fitted to estimate the calibration curves due to small sample size.
#>     This warning has been written to try and intercept some uninformative error messages when the underlying statistical models fail.
#>     The number to flag this warning (50) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.

 # These are then plotted
 plot(dat_calib, combine = TRUE, nrow = 2, ncol = 3)