Description Usage Arguments Details Value Source See Also Examples

Group-lasso with overlapping groups

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`X` |
matrix of size n*p |

`y` |
vector of size n. If loss = "logit", elements of y must be in -1,1 |

`var` |
vector containing the variable to use |

`group` |
vector containing the associated groups |

`lambda` |
lambda values for group lasso. If not provided, the function generates its own values of lambda |

`weight` |
a vector the weight for each group. Default is the square root of the size of each group |

`loss` |
a character string specifying the loss function to use, valid options are: "ls" least squares loss (regression) and "logit" logistic loss (classification) |

`intercept` |
should an intercept be included in the model ? |

`...` |
Others parameters for |

Use a group-lasso algorithm (see `gglasso`

) to solve a group-lasso with overlapping groups.
Each variable j of the original matrix `X`

is paste k(j) times in a new dataset with k(j) the number of different groups containing the variable j.
The new dataset is used to solve the group-lasso with overlapping groups running a group-lasso algorithm.

a MLGL object containing:

- lambda
lambda values

- b0
intercept values for

`lambda`

- beta
A list containing the values of estimated coefficients for each values of

`lambda`

- var
A list containing the index of selected variables for each values of

`lambda`

- group
A list containing the values index of selected groups for each values of

`lambda`

- nVar
A vector containing the number of non zero coefficients for each values of

`lambda`

- nGroup
A vector containing the number of non zero groups for each values of

`lambda`

- structure
A list containing 3 vectors. var: all variables used. group: associated groups. weight: weight associated with the different groups.

- time
computation time

- dim
dimension of

`X`

Laurent Jacob, Guillaume Obozinski, and Jean-Philippe Vert. 2009. Group lasso with overlap and graph lasso. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09).

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Least square loss
set.seed(42)
X <- simuBlockGaussian(50, 12, 5, 0.7)
y <- X[, c(2, 7, 12)] %*% c(2, 2, -2) + rnorm(50, 0, 0.5)
var <- c(1:60, 1:8, 7:15)
group <- c(rep(1:12, each = 5), rep(13, 8), rep(14, 9))
res <- overlapgglasso(X, y, var, group)
# Logistic loss
y <- 2 * (rowSums(X[, 1:4]) > 0) - 1
var <- c(1:60, 1:8, 7:15)
group <- c(rep(1:12, each = 5), rep(13, 8), rep(14, 9))
res <- overlapgglasso(X, y, var, group, loss = "logit")
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