Outcome regression models are specified by the main effects components
(moMain) and the contrasts component (moCont).
Assuming that the
treatment is denoted as binary A, the full regression model is:
moMain + A*moCont. There are two ways to fit this model: (i)
in the full model formulation (moMain + A*moCont) or (ii) each
component, moMain and moCont, is fit separately.
iter specifies
if (i) or (ii) should be used.
iter >= 1 indicates that moMain and moCont
are to be
fit separately using an iterative algorithm.
iter is the maximum number of iterations.
Assume Y = Ymain + Ycont;
the iterative algorithm is as follows:
(1) hat(Ycont) = 0;
(2) Ymain = Y - hat(Ycont);
(3) fit Ymain ~ moMain;
(4) set Ycont = Y - hat(Ymain)
(5) fit Ycont ~ A*moCont;
(6) Repeat steps (2) - (5) until convergence or a maximum of iter iterations.
This choice allows the user to specify, for example, a linear main effects component and a non-linear contrasts component.
iter <= 0 indicates that the full model formulation is to be
used. The components moMain and moCont will be
combined in the package and fit as a single object.
Note that if iter <= 0, all non-model components of
moMain and moCont must be identical. Specifically,
the regression method and any non-default arguments
should be identical.
By default, the specifications in moMain are used.