crf: Clustered random forest fitting

Description

Clustered random forest fitting

Usage

crf(
  formula,
  data,
  B = 500,
  L = 100,
  beta = 0.9,
  weight_optimiser = "Training MSE",
  correlation = "equicorr",
  maxdepth = 30,
  minbucket = 10,
  cp = 0,
  x0 = NULL,
  test_data = NULL,
  fixrho = FALSE,
  honesty = TRUE,
  verbose = TRUE,
  seed = NULL
)

Value

A clustered random forest fitted object

Arguments

formula: an object of class `formula` describing the model to fit.
data: training dataset for fitting the CRF. Note that group ID must be given by the column id.
B: the total number of trees (or trees per little bag if \(L\neq\)`NULL`). Default is 500.
L: the total number of little bags if providing a bootstrap of little bags estimate for inference. To not include set \(L=\)`NULL`. Default is `NULL`.
beta: the subsampling rate. Default is \(beta=0.9\).
weight_optimiser: the method used to construct weights. Options are `Pointwise variance`, `Training MSE` or `Test MSE`. Default is `Training MSE`.
correlation: the weight structure implemented. Currently supported options are `ar1` and `equicorr`. Default is `equicorr`.
maxdepth: the maximum depth of the decision tree fitting. Default is 30.
minbucket: the minbucket of the decision tree fitting. Default is 10.
cp: the complexity paramter for decision tree fitting. Default is 0.
x0: the covariate point to optimise weights towards if `weightoptimiser` set to `Pointwise variance`.
test_data: the test dataset to optimise weights towards if `weightoptimiser` set to `Test MSE`.
fixrho: fixes a pre-specified weight structure, given by the relevant `ar1` or `equicorr` parameter. Default is `FALSE` (optimise weights).
honesty: whether honest or dishonest trees to be fit. Default is `TRUE`.
verbose: Logical indicating whether or not to print computational progress. Default is `TRUE`.
seed: Random seed for sampling. Default is NULL.