Description Usage Arguments Value Author(s) Examples

Multinomial sparse group lasso cross validation, with or without parallel backend.

1 2 3 4 5 6 | ```
cv(x, classes, sampleWeights = NULL, grouping = NULL,
groupWeights = NULL, parameterWeights = NULL, alpha = 0.5,
standardize = TRUE, lambda, d = 100, fold = 10L,
cv.indices = list(), intercept = TRUE, sparse.data = is(x,
"sparseMatrix"), max.threads = NULL, use_parallel = FALSE,
algorithm.config = msgl.standard.config)
``` |

`x` |
design matrix, matrix of size |

`classes` |
classes, factor of length |

`sampleWeights` |
sample weights, a vector of length |

`grouping` |
grouping of features (covariates), a vector of length |

`groupWeights` |
the group weights, a vector of length
for all other weights. |

`parameterWeights` |
a matrix of size |

`alpha` |
the |

`standardize` |
if TRUE the features are standardize before fitting the model. The model parameters are returned in the original scale. |

`lambda` |
lambda.min relative to lambda.max or the lambda sequence for the regularization path. |

`d` |
length of lambda sequence (ignored if |

`fold` |
the fold of the cross validation, an integer larger than |

`cv.indices` |
a list of indices of a cross validation splitting.
If |

`intercept` |
should the model include intercept parameters |

`sparse.data` |
if TRUE |

`max.threads` |
Deprecated (will be removed in 2018),
instead use |

`use_parallel` |
If |

`algorithm.config` |
the algorithm configuration to be used. |

`link` |
the linear predictors – a list of length |

`response` |
the estimated probabilities - a list of length |

`classes` |
the estimated classes - a matrix of size |

`cv.indices` |
the cross validation splitting used. |

`features` |
number of features used in the models. |

`parameters` |
number of parameters used in the models. |

`classes.true` |
the true classes used for estimation, this is equal to the |

Martin Vincent

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
data(SimData)
# A quick look at the data
dim(x)
table(classes)
# Setup clusters
cl <- makeCluster(2)
registerDoParallel(cl)
# Run cross validation using 2 clusters
# Using a lambda sequence ranging from the maximal lambda to 0.7 * maximal lambda
fit.cv <- msgl::cv(x, classes, alpha = 0.5, lambda = 0.7, use_parallel = TRUE)
# Stop clusters
stopCluster(cl)
# Print some information
fit.cv
# Cross validation errors (estimated expected generalization error)
# Misclassification rate
Err(fit.cv)
# Negative log likelihood error
Err(fit.cv, type="loglike")
``` |

nielsrhansen/msgl documentation built on May 28, 2019, 11:05 a.m.

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