Tidy diagnosis
Usage
# S3 method for class 'diagnosis'
tidy(x, conf.int = TRUE, conf.level = 0.95, ...)Arguments
- x
A diagnosis object generated by
diagnose_design.- conf.int
Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to ‘TRUE’.
- conf.level
The confidence level to use for the confidence interval if ‘conf.int = TRUE’. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
- ...
extra arguments (not used)
Value
A data.frame with columns for diagnosand names, estimated diagnosand values, bootstrapped standard errors and confidence intervals
Examples
effect_size <- 0.1
design <-
declare_model(
N = 100,
U = rnorm(N),
X = rnorm(N),
potential_outcomes(Y ~ effect_size * Z + X + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_assignment(Z = complete_ra(N)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE", label = "unadjusted") +
declare_estimator(Y ~ Z + X, inquiry = "ATE", label = "adjusted")
diagnosis <- diagnose_design(design, sims = 100)
tidy(diagnosis)
#> design inquiry estimator outcome term diagnosand estimate
#> 1 design ATE adjusted Y Z mean_estimand 0.100000000
#> 2 design ATE adjusted Y Z mean_estimate 0.101733047
#> 3 design ATE adjusted Y Z bias 0.001733047
#> 4 design ATE adjusted Y Z sd_estimate 0.186361104
#> 5 design ATE adjusted Y Z rmse 0.185435055
#> 6 design ATE adjusted Y Z power 0.050000000
#> 7 design ATE adjusted Y Z coverage 1.000000000
#> 8 design ATE unadjusted Y Z mean_estimand 0.100000000
#> 9 design ATE unadjusted Y Z mean_estimate 0.090097200
#> 10 design ATE unadjusted Y Z bias -0.009902800
#> 11 design ATE unadjusted Y Z sd_estimate 0.263237190
#> 12 design ATE unadjusted Y Z rmse 0.262104837
#> 13 design ATE unadjusted Y Z power 0.050000000
#> 14 design ATE unadjusted Y Z coverage 0.980000000
#> std.error conf.low conf.high
#> 1 0.000000000 0.10000000 0.10000000
#> 2 0.018086283 0.07023091 0.13998336
#> 3 0.018086283 -0.02976909 0.03998336
#> 4 0.010255730 0.16576485 0.20527442
#> 5 0.009964692 0.16717757 0.20447557
#> 6 0.021508749 0.01475000 0.09000000
#> 7 0.000000000 1.00000000 1.00000000
#> 8 0.000000000 0.10000000 0.10000000
#> 9 0.025323990 0.03600417 0.13608792
#> 10 0.025323990 -0.06399583 0.03608792
#> 11 0.013593547 0.24039327 0.29207338
#> 12 0.013468443 0.24001494 0.29073641
#> 13 0.019928154 0.02000000 0.09525000
#> 14 0.012872333 0.95000000 1.00000000