Description Usage Arguments Value Author(s) References Examples
wle.poisson
is used to robust estimate the lambda parameters in the poisson model via Weighted Likelihood.
1 2 3 4 |
x |
a vector contain the number of success. |
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:
|
tau |
this is to set the member inside the GKL and PWD family. It must be in [0,1] for GKL and in [-1, Inf] for PWD. |
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.poisson
returns an object of class
"wle.poisson"
.
Only print method is implemented for this class.
The object returned by wle.poisson
are:
lambda |
the estimator of the lambda 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 non-parametric 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, 215-232.
Agostinelli, C., (1998) Inferenza statistica robusta basata sulla funzione di verosimiglianza pesata: alcuni sviluppi, Ph.D Thesis, Department of Statistics, University of Padova.
1 2 3 4 5 6 7 8 9 | library(wle)
set.seed(1234)
x <- rpois(40,5)
wle.poisson(x)
x <- c(rpois(40,5),rpois(10,20))
wle.poisson(x)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.