Description Usage Arguments Value Author(s) References See Also Examples
poissonMT
is used to fit generalized linear models by robust MT
method. The model is specified by the x
and y
components.
1 2 3 
x 
design matrix of dimension n * p. 
y 
vector of observations of length 
start 
starting values for the parameters in the linear predictor. 
weights 
an optional vector of weights to be used in the fitting process (in addition to the robustness weights computed in the fitting process). 
tol 
convergence tolerance for the parameter vector. 
maxit 
integer specifying the maximum number of IRWLS iterations. 
m.approx 
a function that return the value, for each linear predictor, that
makes the estimating equation Fisher consistent. If 
mprime.approx 
a function that return the value, for each linear predictor,
corresponding to the first derivative of 
psi 
the name of the 
cc 
tuning constant c for Tukey's bisquare psifunction. 
na.to.zero 
logical, should the eventual 
A list with the following components
coefficients 
a vector of coefficients. 
fitted.values 
the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function. 
linear.predictors 
the linear fit on link scale. 
residuals 
residuals on the transformed scale. 
weights 
the working weights, that is the weights in the final iteration of the IWLS fit. 
w.r 
robustness weights for each observations. 
prior.weights 
the weights initially supplied, a vector of

converged 
logical. Was the IWLS algorithm judged to have converged? 
iter 
the number of iterations used by the influence algorithm. 
obj 
value of the MT objective function at 
Claudio Agostinelli, Marina Valdora and Victor J. Yohai
C. Agostinelli, M. Valdora and V.J Yohai (2018) Initial Robust Estimation in Generalized Linear Models with a Large Number of Covariates. Submitted.
M. Valdora and V.J. Yohai (2014) Robust estimators for Generalized Linear Models. Journal of Statistical Planning and Inference, 146, 3148.
1 2 3 4 5  data(epilepsy)
x < model.matrix( ~ Age10 + Base4*Trt, data=epilepsy)
poissonMTsetwd(tempdir())
start < poissonMTinitial(x=x, y=epilepsy$Ysum)$coefficients
Efit3 < poissonMT(x=x, y=epilepsy$Ysum, start=start)

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