Combining normalized bias and variance over a range of values for omitted R-squared to produce normalized MSE.
mlr.combine.bias.variance(tr, bvmat, orsq.min = 0.001, orsq.max = 1, n.orsq = 100)Binary treatment indicator vector (1=treatment, 0=control), whose coefficient in the linear regression model is TE.
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.
Minimum omitted R-squared used for combining bias and variance.
Maximum omitted R-squared.
Number of values for omitted R-squared generated in the vector.
A list with the following elements:
Vector of omitted R-squared values used for combining bias and variance.
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 bvmat.
Matrix of squared biases, with a structure similar to errmat.
Value of calibration index (row number for errmat) with minimum MSE.
Link to a draft paper, documenting the supporting mathematical framework, will be provided in the next release.