scar fits
scar object.
"predict"(object, newdata, type = c("link", "response"), rule=1, ...)scar object produced by scar.matrix of $d$ columns, with each row
specifying a location at which prediction is required. This argument can be
missing, in which case predictions are made at the same values of the
covariates used to compute the object.rule=1, then we use linear interpolation to get
the value of each fitted component function outside the range of observed
covariates. Otherwise if rule=2, then the value at the closest data
extreme is used. Note that if there is convex/concave component, the choice of
the first rule can lead to somewhat unsatifactary performance
on/outside the boundary of the data (when comparing to the second rule).scar, plot.scar## See examples for the function scar
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