cv: Cross Validation
In lsgl: Linear Multiple Output Sparse Group Lasso

Description Usage Arguments Value Author(s) Examples

Linear multiple output cross validation using multiple possessors

cv(x, y, intercept = TRUE, weights = NULL, grouping = NULL,
  groupWeights = NULL, parameterWeights = NULL, alpha = 1, lambda,
  d = 100, fold = 10L, cv.indices = list(), max.threads = NULL,
  use_parallel = FALSE, algorithm.config = lsgl.standard.config)

`x`	design matrix, matrix of size N \times p.
`y`	response matrix, matrix of size N \times K.
`intercept`	should the model include intercept parameters.
`weights`	sample weights, vector of size N \times K.
`grouping`	grouping of features, a factor or vector of length p. Each element of the factor/vector specifying the group of the feature.
`groupWeights`	the group weights, a vector of length m (the number of groups).
`parameterWeights`	a matrix of size K \times p.
`alpha`	the α value 0 for group lasso, 1 for lasso, between 0 and 1 gives a sparse group lasso penalty.
`lambda`	lambda.min relative to lambda.max or the lambda sequence for the regularization path.
`d`	length of lambda sequence (ignored if `length(lambda) > 1`)
`fold`	the fold of the cross validation, an integer larger than 1 and less than N+1. Ignored if `cv.indices != NULL`.
`cv.indices`	a list of indices of a cross validation splitting. If `cv.indices = NULL` then a random splitting will be generated using the `fold` argument.
`max.threads`	Deprecated (will be removed in 2018), instead use `use_parallel = TRUE` and registre parallel backend (see package 'doParallel'). The maximal number of threads to be used.
`use_parallel`	If `TRUE` the `foreach` loop will use `%dopar%`. The user must registre the parallel backend.
`algorithm.config`	the algorithm configuration to be used.

`Yhat`	the cross validation estimated response matrix
`Y.true`	the true response matrix, this is equal to the argument `y`
`cv.indices`	the cross validation splitting used
`features`	number of features used in the models
`parameters`	number of parameters used in the models.

Martin Vincent

set.seed(100) # This may be removed, it ensures consistency of the daily tests

## Simulate from Y=XB+E, the dimension of Y is N x K, X is N x p, B is p x K

N <- 50 #number of samples
p <- 25 #number of features
K <- 15  #number of groups

B<-matrix(sample(c(rep(1,p*K*0.1),rep(0, p*K-as.integer(p*K*0.1)))),nrow=p,ncol=K)
X1<-matrix(rnorm(N*p,1,1),nrow=N,ncol=p)
Y1 <-X1%*%B+matrix(rnorm(N*K,0,1),N,K)

## Do cross validation
fit.cv <- lsgl::cv(X1, Y1, alpha = 1, lambda = 0.1, intercept = FALSE)

## Cross validation errors (estimated expected generalization error)
Err(fit.cv)

## Do the same cross validation using 2 parallel units
cl <- makeCluster(2)
registerDoParallel(cl)

fit.cv <- lsgl::cv(X1, Y1, alpha = 1, lambda = 0.1, intercept = FALSE, use_parallel = TRUE)

stopCluster(cl)

Err(fit.cv)