Predict function for quantile_genlasso object — predict.quantile

Predict the conditional quantiles at a new set of predictor variables, using the generalized lasso coefficients at specified tau or lambda values.

# S3 method for quantile_genlasso
predict(
  object,
  newx,
  s = NULL,
  sort = FALSE,
  iso = FALSE,
  nonneg = FALSE,
  round = FALSE,
  ...
)

Arguments

object: The quantile_genlasso object.
newx: Matrix of new predictor variables at which predictions should be made.
s: Vector of integers specifying the tau and lambda values to consider for predictions; for each i in this vector, predictions are made at quantile level tau[i] and tuning parameter value lambda[i], according to the tau and lambda vectors stored in the given quantile_genlasso object obj. (Said differently, s specifies the columns of object$beta to use for the predictions.) Default is NULL, which means that all tau and lambda values will be considered.
sort: Should the returned quantile estimates be sorted? Default is FALSE. Note: this option only makes sense if the values in the stored tau vector are distinct, and sorted in increasing order.
iso: Should the returned quantile estimates be passed through isotonic regression? Default is FALSE; if TRUE, takes priority over sort. Note: this option only makes sense if the values in the stored tau vector are distinct, and sorted in increasing order.
nonneg: Should the returned quantile estimates be truncated at 0? Natural for count data. Default is FALSE.
round: Should the returned quantile estimates be rounded? Natural for count data. Default is FALSE.
...: Additional arguments (not used).