print.boostmtree: Print a summary of a boostmtree fit or prediction object

In boostmtree: Boosted Multivariate Trees for Longitudinal Data

Print a summary of a boostmtree fit or prediction object

Description

Print a compact summary of a fitted boostmtree model or a compatible prediction object.

Usage

## S3 method for class 'boostmtree'
print(x, ...)

Arguments

x	An object of class c("boostmtree", "grow", ...) or c("boostmtree", "predict", ...).
...	Currently ignored. Included for S3 compatibility.

Details

The printed summary is intended as a quick check that the model you fit is the model you meant to fit. It reports the response family, the requested number of terminal nodes, the sample size, and the structure of the repeated-measures design.

When cross-validation or test-set error information is available, the summary also reports the selected stopping iteration and the corresponding RMSE. For longitudinal fits it can additionally report the estimated working-correlation and working-variance parameters at the selected stopping iteration.

Value

The input object, returned invisibly.

References

Pande A., Li L., Rajeswaran J., Ehrlinger J., Kogalur U.B., Blackstone E.H., Ishwaran H. (2017). Boosted multivariate trees for longitudinal data. Machine Learning, 106(2):277–305.

Pande A., Ishwaran H., Blackstone E.H., Rajeswaran J., and Gillanov M. (2022). Application of gradient boosting in evaluating surgical ablation for atrial fibrillation. SN Computer Science, 3:466.

Pande A., Ishwaran H., and Blackstone E.H. (2022). Boosting for multivariate longitudinal responses. SN Computer Science, 3:186.

See Also

boostmtree, plot.boostmtree, simLong

Examples

## Print a basic longitudinal fit.
set.seed(11)
sim.obj <- simLong(n = 15, n.time = 4, family = "continuous")
dta <- sim.obj$data.list

fit <- boostmtree(
  x = dta$features,
  tm = dta$time,
  id = dta$id,
  y = dta$y,
  family = "continuous",
  M = 8,
  verbose = FALSE
)

print(fit)

## Print a fit that includes out-of-bag stopping information.
set.seed(12)
sim.bin <- simLong(n = 15, n.time = 4, family = "binary")
dta.bin <- sim.bin$data.list

fit.bin <- boostmtree(
  x = dta.bin$features,
  tm = dta.bin$time,
  id = dta.bin$id,
  y = dta.bin$y,
  family = "binary",
  M = 10,
  cv.flag = TRUE,
  verbose = FALSE
)

print(fit.bin)

boostmtree documentation built on April 10, 2026, 9:10 a.m.