lambda.mul.sdwd: Penalty parameters selection for the Multiway Sparse DWD

In lockEF/MultiwayClassification: Linear Classification of Multi-Way Data

Description

Conduct a k-fold cross-validation for mul.sdwd and returns the optimal pair of L1 and L2 parameters.

Usage

lambda.mul.sdwd(
  X,
  y,
  R = 1,
  lambda1.vec = c(0, 1e-04, 0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25),
  lambda2.vec = c(0.25, 0.5, 0.75, 1, 3, 5),
  nfolds = 10,
  convThresh = 10^(-7),
  nmax = 500
)

Arguments

X	A multiway array with dimensions N \times P_1 \times ... \times P_K. The first dimension (N) give the cases (e.g.subjects) to be classified.
y	A vector of length N with class label (-1 or 1) for each case.
R	Assumed rank of the coefficient array.
lambda1.vec	A vector of L1 candidates. Default is c(0, 1e-4, 0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25).
lambda2.vec	A vector of L2 candidates. Default is c(0.25, 0.50, 0.75, 1.00, 3, 5).
nfolds	The number of folds. Default value is 10.
convThresh	The algorithm stops when the distance between B_new and B_old is less than convThresh. Default is 1e-7.
nmax	Restrics how many iterations are allowed. Default is 500.

Value

A list of components

par Optimal pair of L1 and L2 and the maximum t test statistic.

tstats Test statistics for all L1 and L2 condidates.

Examples

## Load gene expression time course data (?IFNB_Data for more info)
data(IFNB_Data)
Class=2*(Class-1)-1 #redefine classes to -1 or 1
## Select penalty parameters by cross-validation
lambda.msdwd1 <- lambda.mul.sdwd(DataArray,y=Class,R=1,lambda1.vec=c(0, 1e-4, 0.001, 0.005, 0.01),
lambda2.vec=c(0.50, 1.00, 3, 5), nfolds = 10,convThresh=10^(-5),nmax=500)
lambda.msdwd1$par

lockEF/MultiwayClassification documentation built on Dec. 17, 2020, 11:01 a.m.