Plot fitted survival, cumulative hazard or hazard from a parametric model against nonparametric estimates to diagnose goodness-of-fit. Alternatively plot a user-defined function of the model parameters against time.
# S3 method for flexsurvreg
plot(x, newdata = NULL, X = NULL, type = "survival",
fn = NULL, t = NULL, start = 0, est = TRUE, ci = NULL, B = 1000,
cl = 0.95, col.obs = "black", lty.obs = 1, lwd.obs = 1, col = "red",
lty = 1, lwd = 2, col.ci = NULL, lty.ci = 2, lwd.ci = 1,
ylim = NULL, add = FALSE, ...)Output from flexsurvreg or
flexsurvspline, representing a fitted survival model object.
Data frame containing covariate values to produce fitted
values for. See summary.flexsurvreg.
If there are only factor covariates in the model, then Kaplan-Meier (or
nonparametric hazard...) curves are plotted for all distinct groups, and
by default, fitted curves are also plotted for these groups. To plot
Kaplan-Meier and fitted curves for only a subset of groups, use
plot(survfit()) followed by lines.flexsurvreg().
If there are any continuous covariates, then a single population Kaplan-Meier curve is drawn. By default, a single fitted curve is drawn with the covariates set to their mean values in the data - for categorical covariates, the means of the 0/1 indicator variables are taken.
Alternative way to supply covariate values, as a model matrix.
See summary.flexsurvreg. newdata is an easier way.
"survival" for survival, to be plotted against
Kaplan-Meier estimates from plot.survfit.
"cumhaz" for cumulative hazard, plotted against transformed
Kaplan-Meier estimates from plot.survfit.
"hazard" for hazard, to be plotted against smooth nonparametric
estimates from muhaz. The nonparametric estimates
tend to be unstable, and these plots are intended just to roughly indicate
the shape of the hazards through time. The min.time and
max.time options to muhaz may sometimes need to
be passed as arguments to plot.flexsurvreg to avoid an error
here.
Ignored if "fn" is specified.
Custom function of the parameters to summarise against time. The
first two arguments of the function must be t representing time, and
start representing left-truncation points, and any remaining
arguments must be parameters of the distribution. It should return a
vector of the same length as t.
Vector of times to plot fitted values for, see
summary.flexsurvreg.
Left-truncation points, see summary.flexsurvreg.
Plot fitted curves (TRUE or FALSE.)
Plot confidence intervals for fitted curves. By default, this is
TRUE if one observed/fitted curve is plotted, and FALSE if
multiple curves are plotted.
Number of simulations controlling accuracy of confidence
intervals, as used in summary.
Decrease for greater speed at the expense of accuracy, or set B=0 to
turn off calculation of CIs.
Width of confidence intervals, by default 0.95 for 95% intervals.
Colour of the nonparametric curve.
Line type of the nonparametric curve.
Line width of the nonparametric curve.
Colour of the fitted parametric curve(s).
Line type of the fitted parametric curve(s).
Line width of the fitted parametric curve(s).
Colour of the fitted confidence limits, defaulting to the same as for the fitted curve.
Line type of the fitted confidence limits.
Line width of the fitted confidence limits.
y-axis limits: vector of two elements.
If TRUE, add lines to an existing plot, otherwise new
axes are drawn.
Other options to be passed to plot.survfit or
muhaz, for example, to control the smoothness of the
nonparametric hazard estimates. The min.time and max.time
options to muhaz may sometimes need to be changed from
the defaults.