ahaz.tune.control | R Documentation |
Define the type of tuning method used for regularization. Currently only used by tune.ahazpen
.
# Cross-validation cv.control(nfolds=5, reps=1, foldid=NULL, trace=FALSE) # BIC-inspired bic.control(factor = function(nobs){log(nobs)})
nfolds |
Number of folds for cross-validation. Default is
|
reps |
Number of repetitions of cross-validation with
|
foldid |
An optional vector of values between 1 and |
trace |
Print progress of cross-validation. Default is |
factor |
Defines how strongly the number of nonzero penalty parameters penalizes the score in a BIC-type criterion; see the details. |
For examples of usage, see tune.ahazpen
.
The regression coefficients of the semiparametric additive hazards model are estimated by solving a linear system of estimating equations of the form D*beta = d with respect to beta. The natural loss function for such a linear function is of the least-squares type
L(beta) = beta' * D * beta - 2 * d' * beta
This loss function is used for cross-validation as described by Martinussen & Scheike (2008).
Penalty parameter selection via a BIC-inspired approach was described by Gorst-Rasmussen & Scheike (2011). With df is the degrees of freedom and n the number of observations, we consider a BIC inspired criterion of the form
BIC = kappa * L(beta) + df * factor(n)
where kappa is a scaling constant included to remove dependency on the
time scale and better mimick the behavior of a ‘real’ (likelihood) BIC. The default factor=function(n){log(n)}
has
desirable theoretical properties but may be conservative in practice.
An object with S3 class "ahaz.tune.control"
.
type |
Type of penalty. |
factor |
Function specified by |
getfolds |
A function specifying how folds are calculated, if applicable. |
rep |
How many repetitions of cross-validation, if applicable. |
trace |
Print out progress? |
Gorst-Rasmussen, A. & Scheike, T. H. (2011). Independent screening for single-index hazard rate models with ultra-high dimensional features. Technical report R-2011-06, Department of Mathematical Sciences, Aalborg University.
Martinussen, T. & Scheike, T. H. (2008). Covariate selection for the semiparametric additive risk model. Scandinavian Journal of Statistics; 36:602-619.
tune.ahazpen
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