Returns the standard set of diagnosands. A diagnosand is a summary of a design's behaviour across simulations, so each one is computed over the whole set of draws rather than within any single draw.
Value
A diagnosands design_step, as declare_diagnosands() returns,
holding the seven diagnosands listed under Details.
Details
mean_estimandAverage of the estimand across draws. The estimand varies from draw to draw whenever the model is stochastic.
mean_estimateAverage of the estimator's estimate across draws.
biasAverage of estimate minus estimand. Zero means the estimator lands on the estimand on average, not that it is close on any one draw.
sd_estimateStandard deviation of the estimates, which is the true sampling variability of the design.
rmseRoot mean squared error, the average distance between estimate and estimand. It counts bias and variability together, so it is the one to read when an estimator trades one against the other.
powerShare of draws in which the p-value is at or below 0.05. Read it as power only when the effect is real; under a true null the same number is the false positive rate.
coverageShare of draws in which the confidence interval contains the estimand. A 95 percent interval should give 0.95. Materially lower means the intervals are too narrow and the design overstates its own precision.
See also
Other diagnosands:
declare_diagnosands(),
format.diagnosis(),
select_diagnosands(),
set_diagnosands()