ickde {ICE} | R Documentation |
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.
ickde(I, h, f, m, n.iterations = 10, x1, xm, right.limit = 10000,kernel="gaussian")
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" |
An object of class IC
W.J. Braun
Braun, J., Duchesne, T. and Stafford, J.E. (2005) Local likelihood density estimation for interval censored data. Canadian Journal of Statistics 33: 39-60.
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")