Predict outcome-adaptive lasso propensity scores for new data

Computes propensity scores (or the linear predictor) for new observations from the stored terms, factor levels, and ORIGINAL-scale coefficients of an oal() fit. Works without keep.fits = TRUE and for both refit modes; the training standardization is never re-estimated (the original-scale coefficients already absorb it). Missing values in newdata are an error.

Usage

# S3 method for class 'oal'
predict(object, newdata = NULL, type = c("ps", "link"), ...)

Arguments

object

An oal object.

newdata

A data frame containing the variables of the propensity score formula, or NULL (default) for the in-sample values.

type

"ps" (default): propensity scores clipped to the fit's clip; "link": the unclipped linear predictor.

...

Ignored.

Value

A numeric vector with one value per row, named by rownames. For newdata = NULL and type = "ps" this is exactly object$ps.

See also

oal(), coef.oal()