prepareData

Matrix with quantitative variables in the first $c$ columns, unordered factors in the next columns, 
  and finally unordered factors. The latter two need to have levels from $1$ to $j$.

Specify columns in $Z$ that correspond to unordered factors.

fact

Specify columns in $Z$ that correspond to ordered factors.

ordfact

Vector of the same length as <code>ordfact</code>. Specifies ordering of ordered factors: <code>"i"</code> means
  that the coefficients of the corresponding ordered factor are estimated in non-decreasing order and <code>"d"</code> means
  non-increasing order. See the examples in <code>ordFacReg</code> for details.

ordering

If <code>TRUE</code>, an intercept (= column of all 1's) is added to the design matrix.

intercept

This function takes a matrix consisting of quantitative variables, unordered, and ordered factors
and generates the corresponding matrix of dummy variables, and some further quantities that are used
by the active set algorithm in <code><a rd-options="" href="/link/ordFacReg?package=OrdFacReg&version=1.0.6" data-mini-rdoc="OrdFacReg::ordFacReg">ordFacReg</a></code> and <code><a rd-options="" href="/link/ordFacRegCox?package=OrdFacReg&version=1.0.6" data-mini-rdoc="OrdFacReg::ordFacRegCox">ordFacRegCox</a></code>.


In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.

prepareData: Prepare input data to be used in active set algorithm

Description

Usage

Arguments

Value

References

See Also