Description Usage Arguments Value

View source: R/computeEnsembleWeight.R

Function to compute the weights of the ensemble models

1 2 | ```
computeEnsembleWeight(data, cvGroup, fits, method = "inverse",
imputationParameters)
``` |

`data` |
A data.table containing the data. |

`cvGroup` |
A vector of the same length as nrow(data). Entries of the vector should be integers from 1 to the number of cross-validation groups (typically 10). This should be randomly assigned, and is usually created by ensembleImpute. |

`fits` |
The fitted values from the models. |

`method` |
Must be either "inverse" or "stacking". If "inverse", the final ensemble is a weighted average of all the individual models, where the weight of each model is proportional to 1/error from that model. If "stacking", then the weight is assigned via a linear regression (where the independent variable in the regression is the variable being imputed, and each individual model is a dependent variable). The linear regression is restricted, however: no weights may be negative and the weights must sum to one. |

`imputationParameters` |
A list of the parameters for the imputation algorithms. See defaultImputationParameters() for a starting point. |

A list of two objects. The first is a matrix of weights that can be multiplied by the fitted models to give the imputed values. Rows corresponding to non-missing values in data have values of NA.

The second object is a matrix of errors for each model and each byKey. These error values are used for creating an estimate for the variability of each imputed value.

SWS-Methodology/faoswsImputation documentation built on April 7, 2018, 10:12 p.m.

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