Object of the penalty class to handle the adaptive lasso penalty (Zou, 2006).
● Data Source:
CranContrib
● Keywords:
● Alias: adaptive.lasso
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penalty
(Package: lqa) :
Penalty Objects
Penalty objects provide a convenient way to specify the details of the penalty terms used by functions for penalized regression problems as in lqa. See the documentation for lqa for the details on how such model fitting takes place.
● Data Source:
CranContrib
● Keywords: classes
● Alias: penalty, print.penalty
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Object of the penalty to handle the elastic net (enet) penalty (Zou & Hastie, 2005)
● Data Source:
CranContrib
● Keywords:
● Alias: enet
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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.
● Data Source:
CranContrib
● Keywords:
● Alias: GBlockBoost
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This function plots coefficient build-ups for GLMs that can be estimated with lqa.
● Data Source:
CranContrib
● Keywords: hplot
● Alias: plot.lqa
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Object of the penalty to handle the correlation-based penalty (Tutz & Ulbricht, 2009).
● Data Source:
CranContrib
● Keywords:
● Alias: penalreg
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The lqa package is designed to fit Generalized Linear Models (GLMs) based on penalized likelihood inference. That is we assume our objective to be
● Data Source:
CranContrib
● Keywords: package
● Alias: lqa-package
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This function computes optimal tuning parameters for GLMs with non-negative garrote penalization that can be fitted by the algorithm described in Ulbricht (2010), Subsection 3.4.1. The optimal tuning parameter minimizes the loss function you have specified in the argument loss.func. However, to find the optimal one this function evaluates model performance for different tuning parameter candidates given in the argument lambda.candidates.
● Data Source:
CranContrib
● Keywords: methods
● Alias: cv.nng, nng.update, nnls, nnls2
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Object of the penalty class to handle the ridge penalty (Hoerl & Kennard, 1970).
● Data Source:
CranContrib
● Keywords:
● Alias: ridge
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icb
(Package: lqa) :
Object of the penalty class to handle the Improved Correlation-Based (ICB) Penalty (Ulbricht, 2010).
● Data Source:
CranContrib
● Keywords:
● Alias: icb
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providing 
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