real-data example on raw perspective scores

[ml_measurement_error_public.git] / simulations / robustness_check_notes.md

diff --git a/simulations/robustness_check_notes.md b/simulations/robustness_check_notes.md
index 1c786e994f47dd36f596942d1e2d1f5e2e8157f1..ac7e88fac4ab937b5e274de557606eb702702310 100644 (file)
--- a/simulations/robustness_check_notes.md
+++ b/simulations/robustness_check_notes.md
@@ -2,19 +2,18 @@
 
 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.
 
 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` file.
-
+The stats are in the list named `robustness_1` in the `.RDS` 

 # robustness\_1\_dv.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 `outcome_formula`. In this robustness check, we do not.
+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
 
 
 # robustness_2.RDS
 

-This is just example 1 with varying levels of classifier accuracy. 
+This is just example 1 with varying levels of classifier accuracy indicated by the `prediction_accuracy` variable.. 

 
 # robustness_2_dv.RDS
 
 
 # robustness_2_dv.RDS
 

-Example 3 with varying levels of classifier accuracy
+Example 3 with varying levels of classifier accuracy indicated by the `prediction_accuracy` variable.

 
 # robustness_3.RDS
 
 
 # robustness_3.RDS