moPropen {DynTxRegime} | R Documentation |

## Defining the moPropen Input Variable

### Description

Several of the statistical methods implemented in package
DynTxRegime use propensity score modeling.
This section details how this
input is to be defined.

### Details

For input `moPropen`

, the method specified to obtain predictions
MUST return the prediction on the scale of the probability,
i.e., predictions must be in the range (0,1). In
addition, `moPropen`

differs from standard `"modelObj"`

objects in that an additional element may be required in
`predict.args`

. Recall, `predict.args`

is the list of control
parameters passed to the prediction method. An additional control
parameter, `propen.missing`

can be included. `propen.missing`

takes value "smallest" or "largest". It will be required if the
prediction method returns predictions for only a subset of the
treatment data; e.g., predict.glm(). `propen.missing`

indicates if
it is the smallest or the largest treatment value that is missing
from the returned predictions.

For example, fitting a binary treatment (A in {0,1}) using

moPropen <- buildModelObj(model = ~1,
solver.method = 'glm',
solver.args = list('family'='binomial'),
predict.method = 'predict.glm',
predict.args = list(type='response'))

returns only P(A=1). P(A=0) is "missing," and thus

moPropen <- buildModelObj(model = ~1,
solver.method = 'glm',
solver.args = list('family'='binomial'),
predict.method = 'predict.glm',
predict.args = list(type='response',
propen.missing = 'smallest'))

If the dimension of the value returned by the prediction method is
less than the number of treatment options and no value is provided
in `propen.missing`

, it is assumed that the smallest valued treatment
option is missing. Here, 'smallest' indicates the lowest value
integer if treatment is an integer, or the 'base' level if treatment
is a factor.

[Package

*DynTxRegime* version 4.9

Index]