# Lasso Averaging Estimation

### Description

Lasso (least absolute shrinkage and selection operator) estimation is performed and evaluated for different tuning parameter choices. To address tuning parameter selection uncertainty a weighted average of these estimators is calculated. The weight vector is chosen such that a k-fold cross validation criterion is minimized.

### Usage

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### Arguments

`X` |
A dataframe or matrix containing the data to be analyzed. |

`ycol` |
An integer specifying the column of the outcome variable. |

`B.var` |
An integer specifying the number of bootstrap replications to be used to estimate the standard error of the Lasso estimator. |

`nolambda` |
An integer specifying the number of candidate complexity parameters to consider. |

`kfold` |
An integer specifying the |

`my.formula` |
A formula specifying the full model. |

`standardize` |
A logical value speciying whether the covariate data should be standardized. |

`calc.variance` |
A logical value specifying whether the standard error of the estimates should be estimated at all (by means of bootstrapping). |

### Value

Returns an object of `class`

‘lae’:

`coefficients` |
A matrix of coefficients and standard errors for Lasso averaging, Lasso selection, and OLS estimation. |

`variable.importance` |
A matrix containing the relative importance of each variable based on model averaging weights. |

`sae.weights` |
A vector containing the weights used for Lasso averaging. |

`sel.weights` |
A vector indicating the complexity parameter that was chosen for Lasso estimation based on k-fold cross validation. |

`complexity.parameter` |
A vector of the actual complexity parameter values used as candidate values for Lasso Averaging Estimation. |

### Author(s)

Michael Schomaker

### References

Schomaker, M. (2012) *Shrinkage Averaging Estimation*, Statistical Papers, 53:1015-1034

### See Also

`plot.lae`

for visualizing the estimation results.

### Examples

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