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
for matching.
Arguments
- object
fitted model object determining scores to be generated.
- newdata
(optional) data frame containing variables with which scores are produced.
- ...
additional arguments passed to
predict.
Details
Like 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 type="response".)
In contrast to predict, if 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 glm, its
newdata is the same data frame that glm evaluates that formula
in, as opposed to the model frame associated with object. See
Examples.
The handling of missing independent variables also differs from that of
predict in two ways. First, if the data used to generate object
has NA values, they're mean-imputed using
fill.NAs. Secondly, if newdata (either the explicit
argument, or the implicit data generated from object) has NA
values, they're likewise mean-imputed using fill.NAs. Also,
missingness flags are added to the formula of object, which is then
re-fit, using fill.NAs, prior to calling predict.
If newdata is specified and contains no missing data, scores
returns the same value as predict.
References
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.
Examples
data(nuclearplants)
pg <- lm(cost~., data=nuclearplants, subset=(pr==0))
# The following two lines produce identical results.
ps1 <- glm(pr~cap+date+t1+bw+predict(pg, newdata=nuclearplants),
data=nuclearplants)
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
ps2 <- glm(pr~cap+date+t1+bw+scores(pg), data=nuclearplants)
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases