sbrier_ltrc {LTRCforests} | R Documentation |
Model fit evaluation for LTRC forests.
Description
Compute the (integrated) Brier score to evaluate the model fit for (left-truncated) right-censored survival data with time-varying covariates, as well as left-truncated right-censored data with time-invariant covariates.
Usage
sbrier_ltrc(obj, id = NULL, pred, type = c("IBS", "BS"))
Arguments
obj |
an object of class |
id |
an optional vector as subject identifiers for |
pred |
a list. This should contain 1) either a matrix
or a list of survival probabilies named |
type |
a character string denoting the type of scores returned. If |
Value
If type = "IBS"
, this returns the integrated Brier score.
If type = "BS"
, this returns BScore
, the Brier scores
and Time
, the time points at which the scores are computed.
Examples
### Example with dataset pbcsample
library(survival)
Formula = Surv(Start, Stop, Event) ~ age + alk.phos + ast + chol + edema
## Fit an LTRC conditional inference forest on time-varying data
LTRCCIFobj = ltrccif(formula = Formula, data = pbcsample, id = ID, mtry = 3, ntree = 50L)
# Time points
tpnt = seq(0, 6000, by = 100)
# Set different upper time limits for each of the subjects
tau = seq(4001, 6200, length.out = length(unique(pbcsample$ID)))
## Obstain estimation at time points tpnt
Predobj = predictProb(object = LTRCCIFobj, time.eval = tpnt, time.tau = tau)
## Compute the integrated Brier score:
pbcobj = Surv(pbcsample$Start, pbcsample$Stop, pbcsample$Event)
IBS = sbrier_ltrc(obj = pbcobj, id = pbcsample$ID, pred = Predobj, type = "IBS")
## Compute the Brier score at each value of tpnt
BS = sbrier_ltrc(obj = pbcobj, id = pbcsample$ID, pred = Predobj, type = "BS")
## Plot the Brier scores
plot(BS$Time, BS$BScore, pch = 20, xlab = "Time", ylab = "Brier score", col = 2)
## As one can see, the Brier scores are returned at all tpnt up to 4000,
## this is because the algorithm set the last evaluation time point
## to be 4000 based on the value of time.eval and time.tau
## (max(tpnt[tpnt <= min(tau)]) == 4000).