wle (version 0.5)

wle.binomial: Robust Estimation in the Binomial Model

Description

wle.binomial is used to robust estimate the proportion parameters via Weighted Likelihood.

Usage

wle.binomial(x, size, boot=30, group, num.sol=1, raf="HD", 
             tol=10^(-6), equal=10^(-3), max.iter=500)

Arguments

x

a vector contain the number of success in each size trials.

size

number of trials.

boot

the number of starting points based on boostrap subsamples to use in the search of the roots.

group

the dimension of the bootstap subsamples. The default value is $max(round(length(x)/4),2)$.

num.sol

maximum number of roots to be searched.

raf

type of Residual adjustment function to be use:

raf="HD": Hellinger Distance RAF,

raf="NED": Negative Exponential Disparity RAF,

raf="SCHI2": Symmetric Chi-Squared Disparity RAF.

tol

the absolute accuracy to be used to achieve convergence of the algorithm.

equal

the absolute value for which two roots are considered the same. (This parameter must be greater than tol).

max.iter

maximum number of iterations.

Value

• wle.binomial returns an object of class "wle.binomial".

  Only print method is implemented for this class.

  The object returned by wle.binomial are:

• p
  the estimator of the proportion parameter, one value for each root found.
• tot.weights
  the sum of the weights divide by the number of observations, one value for each root found.
• weights
  the weights associated to each observation, one column vector for each root found.
• call
  the match.call().
• tot.sol
  the number of solutions found.
• not.conv
  the number of starting points that does not converge after the max.iter iteration are reached.

Examples

library(wle)

set.seed(1234)

x_rbinom(20,p=0.2,size=10)
wle.binomial(x,size=10)

x_c(rbinom(20,p=0.2,size=10),rbinom(10,p=0.9,size=10))
wle.binomial(x,size=10)