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OrdFacReg (version 1.0.6)

internal: Internal functions for ordered factor regression functions

Description

Internal functions for ordered factor regression functions.

Arguments

Details

These functions are not intended to be called by users directly.
  • AbetaFunction $A(\bold{\beta})$ in Rufibach (2010) that collects the indices of the inequalities violated by $\bold{\beta}$.

  • 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 $\widetilde{\psi}(A)$ 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 $\bold{\beta}$ 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 $\widetilde{\psi}(A)$ in Table 1 of Duembgen et al. (2007).

  • LSEsubspaceComputes maximizer on subspace, denoted by $\widetilde{\psi}(A)$ 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 $\phi$ in Rufibach (2010) that maps the original indices $(i, j)$ to the inequality index $i$.

  • setminusRemove elements in vector $B$ from vector $A$.

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

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.