plot.calib_mlr | R Documentation |
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.
## 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 = "Observed risk"
)
x |
Object of class |
... |
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 |
marg.density.type |
What type of marginal plot to show (see |
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 |
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
.
# 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)
# These are then plotted
plot(dat.calib, combine = TRUE, nrow = 2, ncol = 3)
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