iter {DynTxRegime} | R Documentation |

Several of the statistical methods implemented in package DynTxRegime allow for an iterative algorithm when completing an outcome regression. This section details how this input is to be defined.

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.

[Package *DynTxRegime* version 4.9 Index]