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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