# predict.cox.adapt: Predict the survival or quantile function from the extreme... In extremefit: Estimation of Extreme Conditional Quantiles and Probabilities

## Description

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

## Usage

 ```1 2 3 4``` ```## S3 method for class '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. `M` Number of thresholds to be aggregated. `...` further arguments passed to or from other methods.

## 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.

## 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".

`cox.adapt`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32``` ```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) ```