Lasso Averaging Estimation

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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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lae(X, ycol = 1, B.var = 50, nolambda = 100, kfold = 5, my.formula = NULL,
 standardize = TRUE, calc.variance = TRUE)

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 kfold cross validation criterion to (i) use for tuning parameter selection (ii) be minimized for Lasso averaging estimation.

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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library(lasso2)
data(Prostate)
lae(Prostate, ycol=9, kfold=10, my.formula=lpsa~.)