BSVerticalStack {Sstack}

Vertical stacking Random Forest models.

Description

Generate the weights for a vertically stacked set of Random Forest (RF) models given a set of heterogeneous datasets. For vertical stacking at least one dataset must contain full record (all features). Subfunction of BSstack but can be used stand-alone.

Usage

BSVerticalStack(T = 50, mtry = NULL, nodesize = 5, iter = 25,
  ECHO = TRUE, Y, Xfull = NULL, Xn = NULL, X1, X2, ...)

Arguments

T

Number of trees for the individual RF models. (int)

mtry

Number of variables available for splitting at each tree node. If a scalar is given then all models use the given values. If a 1D array is given then each individual model uses the given value.

nodesize

Minimum size of terminal nodes. If a scalar is given then all models use the given values. If a 1D array is given then each individual model uses the given value. By default all models use 5.

iter

The number of time to bootstrap sample the data. (int)

ECHO

Bool, enable to provide output to the user in terms of overlapping samples and runtime for CV.

Y

Nsample x 1 data table of responses for ALL samples. Must have matching rownames with each individual dataset.

Xfull

Data table containing samples with full record. Used for generating the weights. Will attempt to find if not given.

Xn

List containing each dataset to be stacked. If not supplied will be generated from X1, X2, ...

X1

Data table of first dataset to be stacked. Rownames should be contained within Y.

X2

Data table of second dataset to be stacked. Rownames should be contained within Y.

...

Further data tables, X3, X4, ..., Xl.

Details

Required Packages: dplyr, randomForest, foreach

Value

Weights and offsets for each individual RF model.