Boxplot risk quantiles
Retrospective boxplots of risk quantiles conditional on outcome
## S3 method for class 'Score' boxplot( x, model, reference, type = "risk", timepoint, overall = 1L, lwd = 3, xlim, xlab = "", main, outcome.label, outcome.label.offset = 0, event.labels, refline = (type != "risk"), add = FALSE, ... )
x |
Score object obtained by calling function |
model |
Choice of risk prediction model |
reference |
Choice of reference risk prediction model for calculation of risk differences. |
type |
Either |
timepoint |
time point specifying the prediction horizon |
overall |
Logical. Tag to be documented. |
lwd |
line width |
xlim |
x-axis limits |
xlab |
x-axis label |
main |
title of plot |
outcome.label |
Title label for column which shows the outcome status |
outcome.label.offset |
Vertical offset for outcome.label |
event.labels |
Labels for the different events (causes). |
refline |
Logical, for |
add |
Logical. Tag to be documented. |
... |
not used |
# binary outcome
library(data.table)
library(prodlim)
db=sampleData(40,outcome="binary")
fitconv=glm(Y~X3+X5,data=db,family=binomial)
fitnew=glm(Y~X1+X3+X5+X6+X7,data=db,family=binomial)
x=Score(list(new=fitnew,conv=fitconv),
formula=Y~1,contrasts=list(c(2,1)),
data=db,plots="box",null.model=FALSE)
boxplot(x)
# survival outcome
library(survival)
ds=sampleData(40,outcome="survival")
fit=coxph(Surv(time,event)~X6+X9,data=ds,x=TRUE,y=TRUE)
## Not run:
scoreobj=Score(list("Cox"=fit),
formula=Hist(time,event)~1, data=ds,
metrics=NULL, plots="box",
times=c(1,5),null.model=FALSE)
boxplot(scoreobj,timepoint=5)
boxplot(scoreobj,timepoint=1)
## End(Not run)
# competing risks outcome
library(survival)
data(Melanoma, package = "riskRegression")
fit = CSC(Hist(time,event,cens.code="censored")~invasion+age+sex,data=Melanoma)
scoreobj=Score(list("CSC"=fit),
formula=Hist(time,event,cens.code="censored")~1,
data=Melanoma,plots="box",times=5*365.25,null.model=FALSE)
par(mar=c(4,12,4,4))
boxplot(scoreobj,timepoint=5*365.25)
# more than 2 competing risks
m=lava::lvm(~X1+X2+X3)
lava::distribution(m, "eventtime1") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "eventtime2") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "eventtime3") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "censtime") <- lava::coxWeibull.lvm(scale = 1/100)
lava::regression(m,eventtime2~X3)=1.3
m <- lava::eventTime(m,
time ~ min(eventtime1 = 1, eventtime2 = 2, eventtime3 = 3, censtime = 0), "event")
set.seed(101)
dcr=as.data.table(lava::sim(m,101))
fit = CSC(Hist(time,event)~X1+X2+X3,data=dcr)
scoreobj=Score(list("my model"=fit),
formula=Hist(time,event)~1,
data=dcr,plots="box",times=5,null.model=FALSE)
boxplot(scoreobj)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.