ForwardBoost: Computation of the ForwardBoost Algorithm
In lqa: Penalized Likelihood Inference for GLMs

Description Usage Arguments Details Value Author(s) References See Also

This function fits a GLM based on penalized likelihood inference by the ForwardBoost algorithm. However, it is primarily intended for internal use. You can access it via the argument setting method = "ForwardBoost" in lqa, cv.lqa or plot.lqa.

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   ForwardBoost (x, y, family = NULL, penalty = NULL, intercept = 
        TRUE, weights = rep (1, nobs), control = lqa.control (), 
        nu = 1, monotonic = TRUE, ...)

`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 `family()` for further details.
`penalty`	a description of the penalty to be used in the fitting procedure, e.g. `penalty = lasso (lambda = 1.7)`.
`intercept`	a logical object indicating whether the model should include an intercept (this is recommended) or not. The default value is `intercept = TRUE`.
`weights`	some additional weights for the observations.
`control`	a list of parameters for controlling the fitting process. See `lqa.control`.
`nu`	parameter ν from the ForwardBoost algorithm. This parameter manages the update step length. See Ulbricht (2010).
`monotonic`	a logical variable. If `TRUE` then the number of active regressors increases monotonically during the iterations. This is in line with the ForwardBoost algorithm and hence recommended.
`...`	further arguments.

The ForwardBoost algorithm has been described in Ulbricht (2010). So see there for a more detailed technical description.

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 `TRUE` if the algorithm has indeed converged.
`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.