Nathan TeBlunthuis
b8d2048cc5
Also fix a possible bug in the MI logic and simplify the error correction formula in example 2.
1.9 KiB
robustness_1.RDS
Tests how robust the MLE method for independent variables with differential error is when the model for X is less precise. In the main paper, we include Z on the right-hand-side of the truth_formula.
In this robustness check, the truth_formula is an intercept-only model.
The stats are in the list named robustness_1 in the .RDS
robustness_1_dv.RDS
Like robustness\_1.RDS but with a less precise model for w_pred. In the main paper, we included Z in the proxy_formula. In this robustness check, we do not.
robustness_2.RDS
This is just example 1 with varying levels of classifier accuracy indicated by the prediction_accuracy variable..
robustness_2_dv.RDS
Example 3 with varying levels of classifier accuracy indicated by the prediction_accuracy variable.
robustness_3.RDS
Example 1 with varying levels of skewness in the classified variable. The variable Px is the baserate of X and controls the skewness of X.
It probably makes more sense to report the mean of X instead of Px in the supplement.
robustness_3_dv.RDS
Example 3 with varying levels of skewness in the classified variable. The variable B0 is the intercept of the main model and controls the skewness of Y.
It probably makes more sense to report the mean of Y instead of B0 in the supplement.
robustness_4.RDS
Example 2 with varying amounts of differential error. The variable y_bias controls the amount of differential error.
It probably makes more sense to report the corrleation between Y and X-~, or the difference in accuracy from when when Y=1 to Y=0 in the supplement instead of y_bias.
robustness_4_dv.RDS
Example 4 with varying amounts of bias. The variable z_bias controls the amount of differential error.
It probably makes more sense to report the corrleation between Z and Y-W, or the difference in accuracy from when when Z=1 to Z=0 in the supplement instead of z_bias.