Description Usage Arguments Value Note Author(s) References See Also Examples

View source: R/cvm_prioritylasso.R

Runs prioritylasso for a list of block specifications and gives the best results in terms of cv error.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |

`X` |
a (nxp) matrix or data frame of predictors with observations in rows and predictors in columns. |

`Y` |
n-vector giving the value of the response (either continuous, numeric-binary 0/1, or |

`weights` |
observation weights. Default is 1 for each observation. |

`family` |
should be "gaussian" for continuous |

`type.measure` |
The accuracy/error measure computed in cross-validation. It should be "class" (classification error) or "auc" (area under the ROC curve) if |

`blocks.list` |
list of the format |

`max.coef.list` |
list of |

`block1.penalization` |
whether the first block should be penalized. Default is TRUE. |

`lambda.type` |
specifies the value of lambda used for the predictions. |

`standardize` |
logical, whether the predictors should be standardized or not. Default is TRUE. |

`nfolds` |
the number of CV procedure folds. |

`foldid` |
an optional vector of values between 1 and nfold identifying what fold each observation is in. |

`cvoffset` |
logical, whether CV should be used to estimate the offsets. Default is FALSE. |

`cvoffsetnfolds` |
the number of folds in the CV procedure that is performed to estimate the offsets. Default is 10. Only relevant if |

`...` |
Other arguments that can be passed to the function |

object of class `prioritylasso`

with the following elements. If these elements are lists, they contain the results for each penalized block of the best result.

`lambda.ind`

list with indices of lambda for

`lambda.type`

.`lambda.type`

type of lambda which is used for the predictions.

`lambda.min`

list with values of lambda for

`lambda.type`

.`min.cvm`

list with the mean cross-validated errors for

`lambda.type`

.`nzero`

list with numbers of non-zero coefficients for

`lambda.type`

.`glmnet.fit`

list of fitted

`glmnet`

objects.`name`

a text string indicating type of measure.

`block1unpen`

if

`block1.penalization = FALSE`

, the results of either the fitted`glm`

or`coxph`

object.`best.blocks`

character vector with the indices of the best block specification.

`best.max.coef`

vector with the number of maximal coefficients corresponding to

`best.blocks`

.`coefficients`

coefficients according to the results obtained with

`best.blocks`

.`call`

the function call.

The function description and the first example are based on the R package `ipflasso`

.

Simon Klau

Maintainer: Simon Klau (simonklau@ibe.med.uni-muenchen.de)

Klau, S., Jurinovic, V., Hornung, R., Herold, T., Boulesteix, A.-L. (2018). Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data. BMC Bioinformatics 19, 322

`pl_data`

, `prioritylasso`

, `cvr2.ipflasso`

1 2 3 4 5 6 7 8 9 10 11 | ```
cvm_prioritylasso(X = matrix(rnorm(50*500),50,500), Y = rnorm(50), family = "gaussian",
type.measure = "mse", lambda.type = "lambda.min", nfolds = 5,
blocks.list = list(list(bp1=1:75, bp2=76:200, bp3=201:500),
list(bp1=1:75, bp2=201:500, bp3=76:200)))
## Not run:
cvm_prioritylasso(X = pl_data[,1:1028], Y = pl_data[,1029], family = "binomial",
type.measure = "auc", standardize = FALSE, block1.penalization = FALSE,
blocks.list = list(list(1:4, 5:9, 10:28, 29:1028),
list(1:4, 5:9, 29:1028, 10:28)),
max.coef.list = list(c(Inf, Inf, Inf, 10), c(Inf, Inf, 10, Inf)))
## End(Not run)
``` |

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