plot_ep {gestate} | R Documentation |
Plot event prediction output
Description
This function plots the output from event_prediction().
By default, produces a plot of predicted events over time, with confidence intervals if available.
Alternatively, produces a plot with the fitting CI over time with same percentage as prediction interval.
By default, both conditional and unconditional trajectories are plotted (if conditional event prediction is available).
Options are available to customise inclusion.
Usage
plot_ep(
data,
trajectory = c("both", "conditional", "unconditional"),
which_PI = c("both", "conditional", "unconditional", "none"),
prediction_fitting = c("prediction", "fitting"),
observed = NULL,
target = NULL,
max_time = NULL,
max_E = NULL,
legend_position = c("top_left", "bottom_right"),
no_legend = FALSE,
...
)
Arguments
data |
Full output list from event_prediction(). |
trajectory |
String, choice of "both","conditional","unconditional", for which trajectory to plot. (Default="both") |
which_PI |
String, choice of "both","conditional","unconditional","none", for which PIs to plot. (Default="both") |
prediction_fitting |
String, choice of "prediction" or "fitting" (Default = "prediction"). Determines the nature of the intervals; PIs are relevant for prediction of the observation of future trajectories (sample level), whereas fitting CIs concern the interval for the mean trajectory itself (population level). |
observed |
Optional trajectory of observed event numbers. If vector specified, will plot values at integer times starting from 1. If 2-column matrix specified, will take x-values from column 1 and y-values from column 2. (Default=NULL; not plotted). |
target |
Optional target number of events to plot. (Default=NULL; not plotted) |
max_time |
Optional maximum time to plot up to if you do not want to plot full trajectory. (Default=NULL; maximum time determined by input data) |
max_E |
Optional maximum number of events to plot up to. (Default=NULL; maximum event number is the number of patients) |
legend_position |
String with any of "top_left", or "bottom_right", corresponding to legend position in power plot. (Default="top_left"). |
no_legend |
Boolean to turn off legend. Default is FALSE; legend shown. |
... |
Additional graphical parameters. |
Value
Returns NULL
Author(s)
James Bell
Examples
recruit <- PieceR(matrix(c(rep(1,12),10,15,25,30,45,60,55,50,65,60,55,30),ncol=2),1)
trial_long <- simulate_trials(active_ecurve=Weibull(50,0.8),control_ecurve=Weibull(50,0.8),
rcurve=recruit,fix_events=200, iterations=1,seed=12345,detailed_output=TRUE)
trial_short <- set_assess_time(data=trial_long,time=10,detailed_output = FALSE)
maxtime <- max(ceiling(trial_long[,"Assess"]))
events <- rep(NA,maxtime)
for (i in 1:maxtime){events[i] <- sum(1-set_assess_time(trial_long,i)[,"Censored"])}
predictions <- event_prediction(data=trial_short, Event="Censored", censoringOne=TRUE,
type="Weibull", rcurve=recruit, max_time=60, cond_Events=49, cond_NatRisk=451,
cond_Time=10, units="Months")
plot_ep(predictions,trajectory="conditional",which_PI="conditional",max_time=40,observed=events,
target=200,max_E=200)
plot_ep(predictions,trajectory="unconditional",which_PI="unconditional",max_time=40,
observed=events,target=200,max_E=200)
plot_ep(predictions,trajectory="conditional",which_PI="none",observed=events[1:10],max_time=20,
max_E=150)