Performs model averaging on a set of nested candidate models with the weights vector chosen such that a specific Mallow's criterion is minimized.
1 |
X |
A dataframe or matrix of data. |
formula |
Formula of the full model. |
ycol |
Either a character vector or integer specifying the column with the outcome variable. |
variance |
A character vector specifying whether the variance is estimated due to the formula of Burnham
and Anderson ( |
bsa |
A positive integer specifying the number of bootstrap samples used if |
Mallow's Model Averaging (MMA) considers all nested submodels of the full model as candidate models, i.e if there are 7 variables there are 7 candidate models. The weight vector used to combine the respective estimates is chosen such that a certain Mallow's type criterion is minimized. The final weighted estimate targets to minimize the mean squared prediciton error and is optimal in some sense, see Theorem 1 and Lemma 3 in Hansen, B. (2007, Least Squares Model Averaging, Econometrica, 75:1175-1189).
Note however that the results of MMA depend on the ordering of the regresssors.
Returns an object of class
‘mma’:
coefficients |
A matrix of estimated coefficients and standard errors (and bootstrap standard errors if |
averaging.weights |
A matrix containing the weights for each models used in the model averaging procedure. |
Michael Schomaker
Hansen, B. (2007), Least Squares Model Averaging, Econometrica, 75:1175-1189
1 2 |
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.