mlr.combine.bias.variance {MatchLinReg} | R Documentation |
Combining bias and variance to produce total MSE for treatment effect
Description
Combining normalized bias and variance over a range of values for omitted R-squared to produce normalized MSE.
Usage
mlr.combine.bias.variance(tr, bvmat, orsq.min = 0.001, orsq.max = 1, n.orsq = 100)
Arguments
tr |
Binary treatment indicator vector (1=treatment, 0=control), whose coefficient in the linear regression model is TE. |
bvmat |
Matrix of bias and variances. First column must be bias, and second column must be variance. Each row corresponds to a different ‘calibration index’ or scenario, which we want to compare and find the best among them. |
orsq.min |
Minimum omitted R-squared used for combining bias and variance. |
orsq.max |
Maximum omitted R-squared. |
n.orsq |
Number of values for omitted R-squared generated in the vector. |
Value
A list with the following elements:
orsq.vec |
Vector of omitted R-squared values used for combining bias and variance. |
errmat |
Matrix of MSE, with each row corresponding to an omitted R-squared value, and each column for a value of calibration index, i.e. one row if |
biassq.mat |
Matrix of squared biases, with a structure similar to |
which.min.vec |
Value of calibration index (row number for |
Author(s)
Alireza S. Mahani, Mansour T.A. Sharabiani
References
Link to a draft paper, documenting the supporting mathematical framework, will be provided in the next release.