wle.negativebinomial
is used to robust estimate the proportion parameters via Weighted Likelihood.
1 2 3  wle.negativebinomial(x, size, boot=30, group, num.sol=1,
raf="HD", tol=10^(6), equal=10^(3),
max.iter=500, verbose=FALSE)

x 
a vector contain the number of failures which occur in a sequence
of Bernoulli trials before a target number of successes 
size 
target number of successes. 
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:

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 
max.iter 
maximum number of iterations. 
verbose 
if 
wle.negativebinomial
returns an object of class
"wle.negativebinomial"
.
Only print method is implemented for this class.
The object returned by wle.negativebinomial
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. 
f.density 
the nonparametric density estimation. 
m.density 
the smoothed model. 
delta 
the Pearson residuals. 
call 
the match.call(). 
tot.sol 
the number of solutions found. 
not.conv 
the number of starting points that does not converge after the 
Claudio Agostinelli
Markatou, M., Basu, A., and Lindsay, B.G., (1997) Weighted likelihood estimating equations: The discrete case with applications to logistic regression, Journal of Statistical Planning and Inference, 57, 215232.
Agostinelli, C., (1998) Inferenza statistica robusta basata sulla funzione di verosimiglianza pesata: alcuni sviluppi, Ph.D Thesis, Department of Statistics, University of Padova.
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