The Meanshift mode estimator
The Meanshift mode estimator.
meanshift( x, bw = NULL, kernel = "gaussian", par = shorth(x), iter = 1000, tolerance = sqrt(.Machine$double.eps) )
x |
numeric. Vector of observations. |
bw |
numeric. The smoothing bandwidth to be used. |
kernel |
character. The kernel to be used. Available kernels are
|
par |
numeric. The initial value used in the meanshift algorithm. |
iter |
numeric. Maximal number of iterations. |
tolerance |
numeric. Stopping criteria. |
meanshift
returns a numeric value, the mode estimate,
with an attribute "iterations"
.
The number of iterations can be less than iter
if the stopping criteria specified by eps
is reached.
The user should preferentially call meanshift
through
mlv(x, method = "meanshift", ...)
.
Fukunaga, K. and Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21(1):32–40.
# Unimodal distribution x <- rweibull(100, shape = 12, scale = 0.8) ## True mode weibullMode(shape = 12, scale = 0.8) ## Estimate of the mode mlv(x, method = "meanshift", par = mean(x))
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