R: Estimation of the Mode

mlv {modeest}

R Documentation

Estimation of the Mode

Description

mlv is a generic function which enables to compute an estimate of the mode of a univariate distribution. Many different estimates (or methods) are provided:

mfv, which returns the most frequent value(s) in a given numerical vector,
the Lientz mode estimator, which is the value minimizing the Lientz function estimate,
the Chernoff mode estimator, also called naive mode estimator, which is defined as the center of the interval of given length containing the most observations,
the Venter mode estimator, including the shorth, i.e. the midpoint of the modal interval,
the Grenander mode estimator,
the half sample mode (HSM) and the half range mode (HRM), which are iterative versions of the Venter mode estimator,
Parzen's kernel mode estimator, which is the value maximizing the kernel density estimate,
the Tsybakov mode estimator, based on a gradient-like recursive algorithm,
the Asselin de Beauville mode estimator, based on a algorithm detecting chains and holes in the sample,
the Vieu mode estimator.

mlv can also be used to compute the mode of a given distribution, with mlv.character.

A 'plot' and a 'print' methods are provided.

Usage

mlv(x, 
    ...)
  
  ## Default S3 method:
mlv(x, 
    bw = NULL, 
    method, 
    na.rm = FALSE, 
    boot = FALSE, 
    R = 100, 
    B = length(x), 
    ...)
      
  ## S3 method for class 'factor'
mlv(x, 
    ...)
      
  ## S3 method for class 'integer'
mlv(x, 
    na.rm = FALSE, 
    ...)
      
  ## S3 method for class 'character'
mlv(x, 
    ...)
      
  ## S3 method for class 'density'
mlv(x, 
    all = TRUE, 
    abc = FALSE, 
    ...)
  
  ## S3 method for class 'mlv'
plot(x, 
     ...)
       
  ## S3 method for class 'mlv'
print(x, 
      digits = NULL, 
      ...)
        
  ## S3 method for class 'mlv'
as.numeric(x, 
           ...)

Arguments

`x`	numeric (vector of observations), or an object of class `"factor"`, `"integer"`, etc. For the function `as.numeric`, an object of class `"mlv"`.
`bw`	numeric. The bandwidth to be used. This may have different meanings regarding the `method` used.
`method`	character. One of the methods available for computing the mode estimate. See 'Details'.
`na.rm`	logical. Should missing values be removed?

`boot`	logical. Should bootstrap resampling be done?
`R`	numeric. If `boot = TRUE`, the number of bootstrap resampling rounds to use.
`B`	numeric. If `boot = TRUE`, the size of the bootstrap samples drawn from `x`. Default is to use a sample which is the same size as data. For large data sets, this may be slow and unnecessary.
`all`	logical.
`abc`	logical. If `FALSE` (the default), the estimate of the density function is maximised using `optim`.
`digits`	numeric. Number of digits to be printed.
`...`	Further arguments to be passed to the function called for computation. This function is related to the `method` argument.

Details

For the function mlv.default, available methods are "mfv", "lientz", "naive", "venter", "grenander", "hsm", "hrm", "parzen", "tsybakov", and "asselin". See the description above and the associated links.
If x is of class "factor" or "integer", the most frequent value found in x is returned.
If x is of class "character", x should be one of "beta", "cauchy", "gev", etc. i.e. a character for which a function 'x'Mode exists (for instance betaMode, cauchyMode, etc.). See distribMode for the available functions. The mode of the corresponding distribution is returned.
If x is of class "density", the value where the density is maximised is returned.
For the S3 function mlv.lientz, see Lientz for more details.

Value

mlv returns an object of class "mlv".
An object of class "mlv" is a list containing at least the following components:

`M`	the value of the mode
`skewness`	Bickel's measure of `skewness`
`x`	the argument `x`
`method`	the argument `method`
`bw`	the bandwidth

`boot`	the argument `boot`
`boot.M`	if `boot = TRUE`, the resampled values of the mode
`call`	the call which produced the result

Author(s)

P. Poncet

References

See the references on mode estimation on the modeest-package's page.

Examples

# Unimodal distribution
x <- rbeta(1000,23,4)

## True mode
betaMode(23, 4)
# or
mlv("beta", 23, 4)

## Estimate of the mode
mlv(x, method = "lientz", bw = 0.2)
mlv(x, method = "naive", bw = 1/3)
mlv(x, method = "venter", type = "shorth")
mlv(x, method = "grenander", p = 4)
mlv(x, method = "hrm", bw = 0.3)
mlv(x, method = "hsm")
mlv(x, method = "parzen", kernel = "gaussian")
mlv(x, method = "tsybakov", kernel = "gaussian")
mlv(x, method = "asselin", bw = 2/3)
mlv(x, method = "vieu")

## Bootstrap
M <- mlv(x, method = "kernel", boot = TRUE, R = 150)
print(M)
plot(M)
print(mean(M[["boot.M"]]))

[Package modeest version 2.1 Index]