JICO (version 0.1)

cv.continnum.iter: Fit JICO with cross-validation to tune hyperparameters

Description

This function performs K-fold cross validations to select the best tuning parameters for JICO.

Usage

cv.continnum.iter(
  X.list,
  Y.list,
  lambda = 0,
  parameter.set,
  nfolds = 10,
  maxiter = 100,
  center.X = TRUE,
  scale.X = TRUE,
  center.Y = TRUE,
  scale.Y = TRUE,
  orthIndiv = FALSE,
  plot = F,
  criteria = c("min", "1se"),
  sd = 0
)

Value

The parameter from the parameter.set that fit the training data the best.

Arguments

X.list

The list of feature matrices from multiple groups.

Y.list

The list of feature vectors from multiple groups.

lambda

Deprecated. Regularization parameter if L2 penalization is used for CR. JICO uses zero as default.

parameter.set

The set of parameters to be tuned on. Containing choices of rankJ, rankA and gamma.

nfolds

number of folds to perform CV

maxiter

The maximum number of iterations to conduct before algorithm convergence.

center.X

Boolean. If X should be preprocessed with centralization.

scale.X

Boolean. If X should be preprocessed with scaling.

center.Y

Boolean. If Y should be preprocessed with centralization.

scale.Y

Boolean. If Y should be preprocessed with scaline.

orthIndiv

Boolean. If we impose the orthogonality constraint on individual components.

plot

Boolean. If we want to plot the rMSE vs different parameters

criteria

criteria for selecting the best parameter. Use "min" to choose the parameter giving the best performance. Use "1se" to choose the simplest model that gives performance within 1se from the best one.

sd

The standard deviation used to generate random initial values for individual weight vectors.

Examples

set.seed(76)
X1 = MASS::mvrnorm(50, rep(0, 200), diag(200)) # covariates of the first group
X2 = MASS::mvrnorm(50, rep(0, 200), diag(200)) # covariates of the second group
X.list = list(X1, X2)

Y1 = matrix(stats::rnorm(50)) # responses for the first group
Y2 = matrix(stats::rnorm(50)) # responses for the second group
Y.list = list(Y1, Y2)

cv.parameter.set = parameter.set.G_2(
   maxrankA = 1, maxrankJ = 1, gamma = 1e10
) # enumerate the set of tuning parameters

cv.ml.JICO = cv.continnum.iter(
  X.list, Y.list, parameter.set = cv.parameter.set, 
  criteria = "min", nfold = 5, maxiter = 300
) # fit the model and use CV to find the best parameters