defaults: Default values passed to RotMat*

In ODRF: Oblique Decision Random Forest for Classification and Regression

Default values passed to RotMat*

Description

Given the parameter list and the categorical map this function populates the values of the parameter list accoding to our 'best' known general use case parameters.

Usage

defaults(
  paramList,
  split = "entropy",
  dimX = NULL,
  weights = NULL,
  catLabel = NULL
)

Arguments

paramList	A list (possibly empty), to be populated with a set of default values to be passed to a RotMat* function.
split	The criterion used for splitting the variable. 'gini': gini impurity index (classification, default), 'entropy': information gain (classification) or 'mse': mean square error (regression).
dimX	An integer denoting the number of columns in the design matrix X.
weights	A vector of length same as data that are positive weights.(default NULL)
catLabel	A category labels of class list in predictors. (default NULL, for details see Examples of ODT)

Value

Default parameters of the RotMat* function.

• dimX An integer denoting the number of columns in the design matrix X.

• dimProj Number of variables to be projected, default dimProj="Rand": random from 1 to ncol(X).

• numProj the number of projection directions.(default ceiling(sqrt(dimX)))

• catLabel A category labels of class list in prediction variables, for details see Examples of ODRF.

• weights A vector of length same as data that are positive weights.(default NULL)

• lambda Parameter of the Poisson distribution (default 1).

• sparsity A real number in (0,1) that specifies the distribution of non-zero elements in the random matrix. When sparsity="pois" means that non-zero elements are generated by the p(lambda) Poisson distribution.

• prob A probability \in (0,1) used for sampling from.

• randDist Parameter of the Poisson distribution (default 1).

• split The criterion used for splitting the variable. 'gini': gini impurity index (classification, default), 'entropy': information gain (classification) or 'mse': mean square error (regression).

• model Model for projection pursuit. (see PPO)

See Also

RotMatPPO RotMatRand RotMatRF RotMatMake

Examples

set.seed(1)
paramList <- list(dimX = 8, numProj = 3, sparsity = 0.25, prob = 0.5)
(paramList <- defaults(paramList, split = "entropy"))

ODRF documentation built on May 31, 2023, 8:22 p.m.