Tidy diagnosis
Usage
# S3 method for 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 std.error
#> 1 design ATE adjusted Y Z mean_estimand 0.10000000 0.00000000
#> 2 design ATE adjusted Y Z mean_estimate 0.08784819 0.01965011
#> 3 design ATE adjusted Y Z bias -0.01215181 0.01965011
#> 4 design ATE adjusted Y Z sd_estimate 0.19344932 0.01023594
#> 5 design ATE adjusted Y Z rmse 0.19286285 0.01028401
#> 6 design ATE adjusted Y Z power 0.04000000 0.01715379
#> 7 design ATE adjusted Y Z coverage 0.98000000 0.01462114
#> 8 design ATE unadjusted Y Z mean_estimand 0.10000000 0.00000000
#> 9 design ATE unadjusted Y Z mean_estimate 0.11383758 0.02750359
#> 10 design ATE unadjusted Y Z bias 0.01383758 0.02750359
#> 11 design ATE unadjusted Y Z sd_estimate 0.26628644 0.01822501
#> 12 design ATE unadjusted Y Z rmse 0.26531276 0.01783680
#> 13 design ATE unadjusted Y Z power 0.05000000 0.01845989
#> 14 design ATE unadjusted Y Z coverage 0.97000000 0.01520068
#> conf.low conf.high
#> 1 0.10000000 0.10000000
#> 2 0.05727377 0.12455532
#> 3 -0.04272623 0.02455532
#> 4 0.17300388 0.21154875
#> 5 0.17296243 0.21213197
#> 6 0.00475000 0.07000000
#> 7 0.94475000 1.00000000
#> 8 0.10000000 0.10000000
#> 9 0.06096946 0.16917737
#> 10 -0.03903054 0.06917737
#> 11 0.23108192 0.29944747
#> 12 0.22999362 0.29959583
#> 13 0.02000000 0.08525000
#> 14 0.94000000 1.00000000