View source: R/pcoxtimeplots.R
plot.Score | R Documentation |
Plots predictive performance of pcoxtime
in comparison to other models. It uses risk scoring from Score
. pcoxtime
also supports performance measure scoring by R package pec
. See examples.
## S3 method for class 'Score' plot(x, ..., type = c("roc", "auc", "brier"), pos = 0.3)
x |
|
... |
for future implementations. |
type |
metric to return. Choices are |
pos |
spacing between the lines. |
Implements plot method for Score
for time-dependent Brier score, AUC and ROC. However, currently, no support for time-dependent covariate models.
a ggplot
object.
if (packageVersion("survival")>="3.2.9") { data(cancer, package="survival") } else { data(veteran, package="survival") } # pcoxtime lam <- 0.1 alp <- 1 pfit1 <- pcoxtime(Surv(time, status) ~ factor(trt) + karno + diagtime + age + prior , data = veteran , lambda = lam , alpha = alp ) # coxph cfit1 <- coxph(Surv(time, status) ~ factor(trt) + karno + diagtime + age + prior , data = veteran , method = "breslow" , x = TRUE , y = TRUE ) # Evaluate model performance at 90, 180, 365 time points score_obj <- Score(list("coxph" = cfit1, "pcox" = pfit1) , Surv(time, status) ~ 1 , data = veteran , plots = "roc" , metrics = c("auc", "brier") , B = 10 , times = c(90, 180, 365) ) # Plot AUC plot(score_obj, type = "auc") # Plot ROC plot(score_obj, type = "roc") # Plot brier plot(score_obj, type = "brier") # Prediction error using pec package ## Not run: if (require("pec")) { pec_fit <- pec(list("coxph" = cfit1, "pcox" = pfit1) , Surv(time, status) ~ 1 , data = veteran , splitMethod = "Boot632plus" , keep.matrix = TRUE ) plot(pec_fit) } ## End(Not run)
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