ickde {ICE}R Documentation

Interval-Censored Kernel Density Estimation

Description

Iterated conditional expectation kernel density estimation using a local constant. The bandwidth is assumed fixed. (See the example for a way to get a quick ballpark estimate of the bandwidth.) The gaussian, epanechnikov and biweight kernels can be used. Note that the bandwidth estimate would have to be adjusted before using with epanechnikov or biweight.

Usage

ickde(I, h, f, m, n.iterations = 10, x1, xm, right.limit = 10000,kernel="gaussian")

Arguments

I A matrix with two columns, consisting of left and right endpoints of the interval data
h A scalar bandwidth
f An initial estimate of the density at a sequence of grid points (optional; if this is used, do not specify m)
m The number of (equally-spaced) grid points at which the density is to be estimated
n.iterations The maximum number of iterations allowed
x1 The left-most grid point (optional)
xm The right-most grid point (optional)
right.limit For right-censored data, the value to be used as an artificial right endpoint for the intervals
kernel character argument indicated choice of kernel; current choices are "gaussian", "epanechnikov", "biweight"

Value

An object of class IC

Author(s)

W.J. Braun

References

Braun, J., Duchesne, T. and Stafford, J.E. (2005) Local likelihood density estimation for interval censored data. Canadian Journal of Statistics 33: 39-60.

See Also

dpik

Examples

 tmp <- apply(ICHemophiliac, 1, mean)
 h <- try(dpik(tmp), silent=T) # dpik() will work if KernSmooth is loaded
 if (class(h) !="numeric" ) h <- .9  # this makes the example work 
                       # if KernSmooth is not loaded
 estimate <- ickde(ICHemophiliac, m=200, h=h)
 plot(estimate, type="l")

[Package ICE version 0.61 Index]