Generate the weights for a horizontally stacked set of Random Forest (RF) models given a set of heterogeneous datasets. For horizontal stacking some subset of samples must be common among all datasets. Subfunction of BSstack but can be used stand-alone.
BSHorizontalStack(T = 100, mtry = NA, nodesize = 5, iter = 100,
Xn = NULL, ECHO = TRUE, Cf = NULL, Y, X1, X2, ...)Number of trees for the individual RF models. (int)
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.
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.
The number of time to bootstrap sample the data. (int)
List containing each dataset to be stacked. If not supplied will be generated from X1, X2, ...
Bool, enable to provide output to the user in terms of overlapping samples and runtime for CV.
Character vector listing set of samples common among all given datasets. If not found will generate on it's own.
Nsample x 1 data table of responses for ALL samples. Must have matching rownames with each individual dataset.
Data table of first dataset to be stacked. Rownames should be contained within Y.
Data table of second dataset to be stacked. Rownames should be contained within Y.
Further data tables, X3, X4, ..., Xl.
Weights and offsets for each individual RF model.
Required Packages: dplyr, randomForest, foreach