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