Description Usage Arguments Value Author(s) References See Also Examples
glmrobMT
is used to fit generalized linear models by robust MT
method. The model is specified by the x
and y
components and a description of the error distribution. Currently,
only implemented for family=poisson
.
1 2 3 4 |
x |
design matrix of dimension n * p. |
y |
vector of observations of length |
weights |
an optional vector of weights to be used in the fitting process (in addition to the robustness weights computed in the fitting process). |
start |
starting values for the parameters in the linear predictor.
Note that specifying |
offset |
this can be used to specify an a priori known component to be included in the linear predictor during fitting. At the moment it is not used. |
family |
a description of the error distribution and link function to
be used in the model. This can be a character string naming a
family function, a family |
weights.on.x |
a character string (can be abbreviated), a If If it is a |
control |
a list of parameters for controlling the fitting process.
See the documentation for |
intercept |
logical indicating if an intercept at the first column of |
trace.lev |
logical (or integer) indicating if intermediate results
should be printed; defaults to |
include.cubinf |
logical, if |
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 |
... |
At the moment it is not used. |
A list with the following components:
coefficients |
a named vector of coefficients. |
initial |
Initial vector of coefficients. |
family |
the |
residuals |
weighted Pearson residuals. |
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. |
cov |
the estimated asymptotic covariance matrix of the estimated coefficients. |
converged |
logical. Was the IWLS algorithm judged to have converged? |
iter |
the number of iterations used by the influence algorithm. |
cw |
the tuning constant c in Tukey's bisquare psi-function. |
weights.on.x |
how the weights on the design matrix |
w.x |
weights used to down-weight observations based on the position of the observation in the design space. |
w.r |
robustness weights for each observations. |
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, 31-48.
1 2 3 4 5 6 | data(epilepsy)
Efit1 <- glm(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy)
x <- model.matrix( ~ Age10 + Base4*Trt, data=epilepsy)
poissonMTsetwd(tempdir())
Efit2 <- glmrobMT(x=x, y=epilepsy$Ysum)
|
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