This is a wrapper for
predict, adapted for use in matching. Given a
fitted model but no explicit
newdata to ‘predict’ from, it
constructs its own
newdata in a manner that's generally better suited
fitted model object determining scores to be generated.
(optional) data frame containing variables with which scores are produced.
additional arguments passed to
predict, its default predictions from a
glm are on
the scale of the linear predictor, not the scale of the response; see
Rosenbaum \& Rubin (1985). (This default can
be overridden by specifying
In contrast to
scores isn't given an explicit
newdata argument then it attempts to reconstruct one from the context
in which it is called, rather than from its first argument. For example, if
it's called within the
formula argument of a call to
newdata is the same data frame that
glm evaluates that formula
in, as opposed to the model frame associated with
The handling of missing independent variables also differs from that of
predict in two ways. First, if the data used to generate
NA values, they're mean-imputed using
fill.NAs. Secondly, if
newdata (either the explicit
argument, or the implicit data generated from
values, they're likewise mean-imputed using
missingness flags are added to the formula of
object, which is then
fill.NAs, prior to calling
newdata is specified and contains no missing data,
returns the same value as
P.~R. Rosenbaum and D.~B. Rubin (1985), ‘Constructing a control group using multivariate matched sampling methods that incorporate the propensity score’, The American Statistician, 39 33–38.
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