modeest (version 1.06)

hrm: Half-range Mode

Description

This function computes Bickel's half range mode estimator described in Bickel (2002).

Usage

hrm(x, 
    bw = NULL, 
    ...)

Arguments

x

numeric. Vector of observations

bw

numeric. The bandwidth to be used. Should belong to (0, 1]. This gives the fraction of the observations to consider at each step of the iterative algorithm.

...

further arguments.

Value

• A numeric value is returned, the mode estimate.

Details

The mode estimator is computed by iteratively identifying densest half ranges. A densest half range is an interval whose width equals half the current range, and which contains the maximal number of observations. The subset of observations falling in the selected densest half range is then used to compute a new range, and the procedure is iterated.

References

• Bickel D.R. (2002). Robust estimators of the mode and skewness of continuous data.Computational Statistics and Data Analysis,39:153-163.
• Hedges S.B. and Shah P. (2003). Comparison of mode estimation methods and application in molecular clock analysis.BMC Bioinformatics,4:31-41.
• Bickel D.R. et Fr�hwirth R. (2006). On a Fast, Robust Estimator of the Mode: Comparisons to Other Robust Estimators with Applications.Computational Statistics and Data Analysis,50(12):3500-3530.

See Also

mlv for general mode estimation; hsm for the half sample mode; venter for the Venter mode estimate

Examples

# Unimodal distribution
x <- rgamma(1000, shape = 31.9)
## True mode
gammaMode(shape = 31.9)
## Estimate of the mode
hrm(x, bw = 0.4)
M <- mlv(x, method = "hrm", bw = 0.4)
print(M)
plot(M)