Description Usage Arguments Details Value Author(s) References See Also
This function fits a GLM based on penalized likelihood inference by the GBlockBoost algorithm. However, it is primarily intended
for internal use. You
can access it via the argument setting method = "GBlockBoost"
in lqa
, cv.lqa
or plot.lqa
.
If you use componentwise = TRUE
then componentwise boosting will be applied.
1 2 3 |
x |
matrix of standardized regressors. This matrix does not need to include a first column of ones when a GLM with intercept is to be fitted. |
y |
vector of observed response values. |
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 function or the result of a call to a family function. See |
penalty |
a description of the penalty to be used in the fitting procedure, e.g. |
intercept |
a logical object indicating whether the model should include an intercept (this is recommended) or not. The default value is
|
weights |
some additional weights for the observations. |
control |
a list of parameters for controlling the fitting process. See |
componentwise |
if |
... |
further arguments. |
The GBlockBoost algorithm has been introduced in Ulbricht \& Tutz (2008). For a more detailed technical description, also for componentwise boosting, see Ulbricht (2010).
GBlockBoost
returns a list containing the following elements:
coefficients |
the vector of standardized estimated coefficients. |
beta.mat |
matrix containing the estimated coefficients from all iterations (rowwise). |
m.stop |
the number of iterations until AIC reaches its minimum. |
stop.at |
the number of iterations until convergence. |
aic.vec |
vector of AIC criterion through all iterations. |
bic.vec |
vector of BIC criterion through all iterations. |
converged |
a logical variable. This will be |
min.aic |
minimum value of AIC criterion. |
min.bic |
minimum value of BIC criterion. |
tr.H |
the trace of the hat matrix. |
tr.Hatmat |
vector of hat matrix traces through all iterations. |
dev.m |
vector of deviances through all iterations. |
Jan Ulbricht
Ulbricht, Jan (2010) Variable Selection in Generalized Linear Models. Ph.D. Thesis. LMU Munich.
Ulbricht, J. \& G. Tutz (2008) Boosting correlation based penalization in generalized linear models. In Shalabh \& C. Heumann (Eds.) Recent Advances in Linear Models and Related Areas. Heidelberg: Springer.
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