NPHazardRate (version 0.1)

lambdahat: Discrete non parametric mle hazard rate estimator

Description

Implementation of the purely nonparametric discrete hazard rate estimator lambdahat discussed among others in Patil and Bagkavos (2012). lambdahat is also used as the nonparametric component in the implementation of SemiparamEst.

Usage

lambdahat(xin, cens, xout)

Arguments

xin

A vector of data points. Missing values not allowed.

cens

Censoring indicators as a vector of 1s and zeros, 1's indicate uncensored observations, 0's correspond to censored obs.

xout

The grid points where the estimates will be calculated.

Value

Returns a vector with the values of the hazard rate estimates at \(x=xout\).

Details

The discrete - crude - hazard rate estimator (NPMLE) in Patil and Bagkavos (2012) is given by $$\hat \lambda(t_k) = \frac{n^0_k}{m_k+1} $$

References

Patil and Bagkavos (2012), Semiparametric smoothing of discrete failure time data, Biometrical Journal, 54, (2012), 5--19.

See Also

SemiparamEst

Examples

# NOT RUN {
options(echo=FALSE)
xin<-c(7,34,42,63,64, 74, 83, 84, 91, 108, 112,129, 133,133,139,140,140,146,
      149,154,157,160,160,165,173,176,185, 218,225,241, 248,273,277,279,297,
      319,405,417,420,440, 523,523,583, 594, 1101, 1116, 1146, 1226, 1349,
      1412, 1417)
cens<-c(1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,
        0,1,0,1,1,1,1,1,0,1,1,1,0,1)
xin<-xin/30.438    #Adjust the data
storage.mode(xin)<-"integer"  # turn the data to integers
xout<-seq(1,47, by=1)  # define the grid points to evaluate the estimate
arg<-TutzPritscher(xin,cens,xout)   #Discrete kernel estimate
plot(xout, arg, type="l", ylim=c(0, .35), lty=2,  col=6) # plot the estimate
argSM<-lambdahat(xin, cens, xout)  #crude nonparametric estimate
lines(xout, argSM, lty=3, col=5) # plot the crude estimate
# }