idrbag: Compute IDR predictions with (su)bagging
In isodistrreg: Isotonic Distributional Regression (IDR)

idrbag

R Documentation

Compute IDR predictions with (su)bagging

Description

Computes IDR predictions with bootstrap aggregating (bagging) or subsample aggregation (subagging).

Usage

idrbag(y, X, y_observed = NULL, weights = NULL, decreasing = FALSE,
  groups = setNames(rep(1, ncol(X)), colnames(X)), orders = c("comp" = 1),
  stoch = "sd", pars = list(verbose = FALSE, eps_abs = 1e-5,
  eps_rel = 1e-5, max_iter = 10000L), n_jobs = 1, progress = TRUE, newdata,
  digits = NULL, interpolation = "linear", b, p, replace = FALSE,
  grid = NULL, seed = NULL)

Arguments

`y`	numeric vector (the response variable).
`X`	data frame of numeric or ordered factor variables (the regression covariates).
`y_observed`	vector of indicators (TRUE or 1 for observed, FALSE or 0 for right-censored). At least one observation must be uncensored. Default is all observed (`rep(TRUE, length(y))`).
`weights`	vector of finite, non-negative weights (same length as y), at least one of which must be positive; observations with zero weight are dropped from the fit. Default is all weights equal to one. Weights are processed in single precision; it is up to the caller to avoid extreme imbalance (as a rule of thumb, no weight below ~1e-7 of the total weight).
`decreasing`	boolean indicating whether `y` decreases with `X` (by default, it increases with `X`).
`groups`	named vector of length `ncol(X)` denoting groups of variables that are to be ordered with the same order (see 'Details'). Only relevant if `X` contains more than one variable. The same names as in `X` should be used.
`orders`	named vector giving for each group in `groups` the order that will be applied to this group. Only relevant if `X` contains more than one variable. The names of `orders` give the order, the entries give the group labels. Available options: `"comp"` for componentwise order, `"sd"` for stochastic dominance, `"icx"` for increasing convex order (see 'Details). Default is `"comp"` for all variables. The `"sd"` and `"icx"` orders can only be used with numeric variables, but not with ordered factors.
`stoch`	stochastic order constraint used for estimation. Default is `"sd"` for first order stochastic dominance. Use `"hazard"` for hazard rate order (experimental).
`pars`	parameters for quadratic programming optimization (only relevant if `X` has more than one column), a list with options "verbose" T / F (verbosity of solver), "eps_abs" positive float, "eps_rel" positive float, "max_iter" positive integer.
`n_jobs`	number of worker threads used to fit the individual subsamples in parallel. Only relevant when (su)bagging is active. Default is `1` (serial execution).
`progress`	display a progress bar while fitting (`TRUE`, `FALSE` or `1`, `0`). Default is `TRUE`; the bar is written to stderr and is best viewed in an interactive R session.
`newdata`	`data.frame` containing variables with which to predict. Ordered factor variables are converted to numeric for computation, so ensure that the factor levels are identical in `newdata` and in `X`.
`digits`	removed functionality, parameter kept for backwards compatibility but ignored with warning: number of decimal places for the predictive CDF, useful to keep the solution small across covariates.
`interpolation`	interpolation method for univariate data, ignored at this time (a warning is issued once per session if a value other than `"linear"` is supplied). Only linear is supported for single variate, multivariate uses midpoint.
`b`	number of (su)bagging samples.
`p`	size of (su)bagging samples relative to training data.
`replace`	draw samples with (`TRUE`, `1`) or without (`FALSE`, `0`) replacement?
`grid`	grid on which the predictive CDFs are evaluated. Default are the unique values of `y`.
`seed`	integer seed for the random number generator. Only relevant when (su)bagging is active.

Details

This function draws b times a random subsample of size ceiling(nrow(X)*p)) from the training data, fits IDR to each subsample, computes predictions for the new data supplied in newdata, and averages the predictions derived from the b subsamples. There are no default values for b and p.