internal: Internal functions for ordered factor regression functions

In OrdFacReg: Least Squares, Logistic, and Cox-Regression with Ordered Predictors

Description

Internal functions for ordered factor regression functions.

Details

These functions are not intended to be called by users directly.

• AbetaFunction A(\bold{β}) in Rufibach (2010) that collects the indices of the inequalities violated by \bold{β}.

• constraintMatsFunction that computes the matrices \bold{B} (collects the basis vectors given in Theorem 3.1 of Duembgen et al. (2007)) and \bold{V} (collects the vectors \bold{v}_i that make up the cone K in Section 3.1 of Duembgen et al. (2007)).

• coxDerivComputes gradient of (pseudo-)log-likelihood function in Cox-regression.

• coxLoglikComputes value of (pseudo-)log-likelihood function in Cox-regression.

• coxSubspaceComputes maximizer on subspace, denoted by \widetilde{ψ}(A) in Table 1 of Duembgen et al. (2007).

• dummyGenerate a matrix of dummy variables corresponding to the levels of the inputed factor. The dummy variable corresponding to the lowest level of the factor is omitted.

• expandBetaAfter computation of \bold{β} on subspace “blow up” this vector again to original dimension.

• indexDummyCompute column numbers of the dummy variables of the ordered factor(s).

• lmLSECompute value of least squares criterion and least squares estimate.

• lmSSCompute value of least squares criterion and its gradient.

• logRegDerivGradient of log-likelihood function in logistic regression.

• logRegLoglikCompute value of log-likelihood function in logistic regression.

• logRegSubspaceComputes maximizer on subspace, denoted by \widetilde{ψ}(A) in Table 1 of Duembgen et al. (2007).

• LSEsubspaceComputes maximizer on subspace, denoted by \widetilde{ψ}(A) in Table 1 of Duembgen et al. (2007).

• maxStepCompute maximal permissible steplength, denoted by t in Table 1 in Duembgen et al. (2007).

• phi_jlFunction φ in Rufibach (2010) that maps the original indices (i, j) to the inequality index i.

• setminusRemove elements in vector B from vector A.

• shrinkBetaCollapse \bold{β} according to the active constraints specified by the set A.

Author(s)

Kaspar Rufibach (maintainer)
[e-mail]
http://www.kasparrufibach.ch

References

Duembgen, L., Huesler, A. and Rufibach, K. (2010). Active set and EM algorithms for log-concave densities based on complete and censored data. Technical report 61, IMSV, Univ. of Bern, available at http://arxiv.org/abs/0707.4643.

Rufibach, K. (2010). An Active Set Algorithm to Estimate Parameters in Generalized Linear Models with Ordered Predictors. Comput. Statist. Data Anal., 54, 1442-1456.

See Also

All these functions are used by the ordered factor computation functions ordFacReg and ordFacRegCox.

OrdFacReg documentation built on May 1, 2019, 10:06 p.m.