# DiscretizeData: Discretize the available data set In NPHazardRate: Nonparametric Hazard Rate Estimation

## Description

Defines equispaced disjoint intervals based on the range of the sample and calculates empirical hazard rate estimates at each interval center

## Usage

 1 DiscretizeData(xin, xout) 

## Arguments

 xin A vector of input values xout Grid points where the function will be evaluated

## Details

The function defines the subinterval length Δ = (0.8\max(X_i) - \min(X_i))/N where N is the sample size. Then at each bin (subinterval) center, the empirical hazard rate estimate is calculated by

c_i = \frac{f_i}{Δ(N-F_i +1) }

where f_i is the frequency of observations in the ith bin and F_i = ∑_{j≤q i} f_j is the empirical cummulative distribution estimate.

## Value

A vector with the values of the function at the designated points xout or the random numbers drawn.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 x<-seq(0, 5,length=100) #design points where the estimate will be calculated SampleSize<-100 #amount of data to be generated ti<- rweibull(SampleSize, .6, 1) # draw a random sample ui<-rexp(SampleSize, .2) # censoring sample cat("\n AMOUNT OF CENSORING: ", length(which(ti>ui))/length(ti)*100, "\n") x1<-pmin(ti,ui) # observed data cen<-rep.int(1, SampleSize) # initialize censoring indicators cen[which(ti>ui)]<-0 # 0's correspond to censored indicators a.use<-DiscretizeData(ti, x) # discretize the data BinCenters<-a.use$BinCenters # get the data centers ci<-a.use$ci # get empircal hazard rate estimates Delta=a.use\$Delta # Binning range 

NPHazardRate documentation built on May 2, 2019, 10:24 a.m.