msma (version 3.1)

cvmsma: Cross-Validation

Description

cross-validated method to evaluate the fit of "msma".

Usage

cvmsma(
  X,
  Y = NULL,
  Z = NULL,
  comp = 1,
  lambdaX,
  lambdaY = NULL,
  lambdaXsup = NULL,
  lambdaYsup = NULL,
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  inXsup = NULL,
  inYsup = NULL,
  muX = 0,
  muY = 0,
  nfold = 5,
  seed = 1,
  intseed = 1
)

Value

err

The mean cross-validated errors which has three elements consisting of the mean of errors for X and Y, the errors for X and for Y.

Arguments

X

a (list of) matrix, explanatory variable(s).

Y

a (list of) matrix, objective variable(s).

Z

a (list of) matrix, response variable(s).

comp

numeric scalar for the maximum number of componets to be considered.

lambdaX

numeric vector of regularized parameters for X with length equal to the number of blocks. If omitted, no regularization is conducted.

lambdaY

numeric vector of regularized parameters for Y with length equal to the number of blocks. If omitted, no regularization is conducted.

lambdaXsup

numeric vector of regularized parameters for the super weight of X with length equal to the number of blocks. If omitted, no regularization is conducted.

lambdaYsup

numeric vector of regularized parameters for the super weight of Y with length equal to the number of blocks. If omitted, no regularization is conducted.

eta

numeric scalar the parameter indexing the penalty family.

type

a character.

inX

a (list of) numeric vector to specify the variables of X which are always in the model.

inY

a (list of) numeric vector to specify the variables of X which are always in the model.

inXsup

a (list of) numeric vector to specify the blocks of X which are always in the model.

inYsup

a (list of) numeric vector to specify the blocks of Y which are always in the model.

muX

a numeric scalar for the weight of X for the supervised.

muY

a numeric scalar for the weight of Y for the supervised.

nfold

number of folds - default is 5.

seed

number of seed for the random number.

intseed

seed number for the random number in the parameter estimation algorithm.

Details

k-fold cross-validation for msma

Examples

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### One Component CV #####
cv1 = cvmsma(X, Y, comp = 1, lambdaX=2, lambdaY=1:3, nfold=5, seed=1)
cv1

##### Two Component CV #####
cv2 = cvmsma(X, Y, comp = 2, lambdaX=2, lambdaY=1:3, nfold=5, seed=1)
cv2