scores function

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

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.

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.

Returns

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.

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)
ps2 <- glm(pr~cap+date+t1+bw+scores(pg), data=nuclearplants)

References

P.~R. Rosenbaum and D.~B. Rubin (1985), Constructing a control group using multivariate matched samplingmethods that incorporate the propensity score , The American Statistician, 39 33--38.

See Also

predict

Author(s)

Josh Errickson