optmatch (version 0.9-17)

scores: Extract scores (propensity, prognostic,...) from a fitted model

Description

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.

Usage

scores(object, newdata = NULL, ...)

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.

Value

See individual predict functions.

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.

See Also

predict

Examples

# NOT RUN {
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)
ps2 <- glm(pr~cap+date+t1+bw+scores(pg), data=nuclearplants)
# }