X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/fa05dbab6bd2c5db6ed4eccf38cff03bb4fd6683..d9d3e47a44ddead1cdf7a649bc0e9849c2219498:/simulations/robustness_check_notes.md?ds=inline

diff --git a/simulations/robustness_check_notes.md b/simulations/robustness_check_notes.md
index 64a472d..ac7e88f 100644
--- a/simulations/robustness_check_notes.md
+++ b/simulations/robustness_check_notes.md
@@ -2,11 +2,10 @@
 
 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
 
-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