predict.cox.adapt: Predict the survival or quantile function from the extreme procedure for the Cox model

Description

Give the survival or quantile function from the extreme procedure for the Cox model

Usage

# S3 method for cox.adapt
predict(object, newdata = NULL, input = NULL,
  type = "quantile", aggregation = "none", AggInd = object$kadapt,
  M = 10, ...)

Arguments

object

output object of the function cox.adapt.

newdata

a data frame with which to predict.

input

optionnaly, the name of the variable to estimate.

type

either "quantile" or "survival".

aggregation

either "none", "simple" or "adaptive".

AggInd

Indices of thresholds to be aggregated.

Number of thresholds to be aggregated.

...

further arguments passed to or from other methods.

Value

The function provide the quantile assiociated to the adaptive model for the probability grid if type = "quantile". And the survival function assiociated to the adaptive model for the quantile grid if type = "survival".

Details

\(newdata\) must be a data frame with the co-variables from which to predict and a variable of probabilities with its name starting with a "p" if type = "quantile" or a variable of quantiles with its name starting with a "x" if type = "survival". The name of the variable from which to predict can also be written as \(input\).

Examples

Run this code

# NOT RUN {
library(survival)
data(bladder)

X <- bladder2$stop-bladder2$start
Z <- as.matrix(bladder2[, c(2:4, 8)])
delta <- bladder2$event

ord <- order(X)
X <- X[ord]
Z <- Z[ord,]
delta <- delta[ord]

cph<-coxph(Surv(X, delta) ~ Z)

ca <- cox.adapt(X, cph, delta, bladder2[ord,])

xgrid <- X
newdata <- as.data.frame(cbind(xgrid,bladder2[ord,]))

Plac <- predict(ca, newdata = newdata, type = "survival")
Treat <- predict(ca, newdata = newdata, type = "survival")

PlacSA <- predict(ca, newdata = newdata,
                          type = "survival", aggregation = "simple", AggInd = c(10,20,30,40))
TreatSA <- predict(ca, newdata = newdata,
                          type = "survival", aggregation = "simple", AggInd = c(10,20,30,40))


PlacAA <- predict(ca, newdata = newdata,
                          type = "survival", aggregation = "adaptive", M=10)
TreatAA <- predict(ca, newdata = newdata,
                          type = "survival", aggregation = "adaptive", M=10)

# }