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Boosting parameter path data object

Source: R/gg_boost_path.R
gg_boost_path.Rd

Extract the estimated correlation, variance, and penalized-spline smoothing parameters of a boosted multivariate tree fit as functions of the boosting iteration.

Usage

gg_boost_path(object, parameters = c("rho", "phi", "lambda"), ...)

Arguments

object

A fitted boostmtree object, or a fitted BoostMLR object.

parameters

Character vector naming the paths to extract. For a boostmtree fit, any of "rho", "phi", and "lambda", defaulting to all three. For a BoostMLR fit, "rho" and/or "phi" only, defaulting to both.

...

Not used; present for S3 consistency.

Value

A gg_boost_path data.frame with columns:

iteration

Integer boosting iteration, from 1.

value

Numeric parameter value at that iteration.

parameter

Factor naming the parameter.

response

Factor naming the response.

Details

boostmtree re-estimates three quantities at every boosting iteration: rho, the within-subject correlation; phi, the variance component; and lambda, the P-spline smoothing parameter for the time-covariate interaction. Their paths diagnose whether the variance structure settled or is still drifting when boosting stops.

Unlike the error path, these are recorded on every fit and do not require cv.flag = TRUE. A parameter absent from the fit is dropped silently; it is an error only when none of the requested parameters is present.

This figure also accepts a BoostMLR fit, which records only Rho and Phi as M-by-response matrices – the method's default parameters is c("rho", "phi") rather than all three. BoostMLR has no comparable lambda: its Lambda_List holds per-iteration basis coefficients rather than a scalar smoothing parameter per response, a different quantity, so requesting "lambda" from a BoostMLR fit is refused with that reason instead of silently dropped.

See also

plot.gg_boost_path, gg_boost_error

Examples

# \donttest{
sim <- boostmtree::simLong(n = 25, n.time = 4, model = 1)$data.list
fit <- boostmtree::boostmtree(
  x = sim$features, tm = sim$time, id = sim$id, y = sim$y,
  M = 50, cv.flag = TRUE, verbose = FALSE
)
plot(gg_boost_path(fit))

# }