A researcher often has a set of diagnosands in mind to appropriately assess the quality of a design. set_diagnosands sets the default diagnosands for a design, so that later readers can assess the design on the same terms as the original author. Readers can also use diagnose_design to diagnose the design using any other set of diagnosands.
Arguments
- x
A design typically created using the + operator, or a simulations data.frame created by
simulate_design.- diagnosands
A set of diagnosands created by
declare_diagnosands
Examples
# Two-arm randomized experiment
design <-
declare_model(
N = 500,
gender = rbinom(N, 1, 0.5),
X = rep(c(0, 1), each = N / 2),
U = rnorm(N, sd = 0.25),
potential_outcomes(Y ~ 0.2 * Z + X + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N = N, n = 200)) +
declare_assignment(Z = complete_ra(N = N, m = 100)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
# You can choose your own diagnosands instead of the defaults:
my_diagnosands <-
declare_diagnosands(median_bias = median(estimate - estimand))
if (FALSE) {
## You can set diagnosands with set_diagnosands
design <- set_diagnosands(design, diagnosands = my_diagnosands)
diagnosis <- diagnose_design(design)
diagnosis
## Using set_diagnosands to diagnose simulated data
simulations_df <- simulate_design(design)
simulations_df <- set_diagnosands(simulations_df, my_diagnosands)
diagnose_design(simulations_df)
# If you do not specify diagnosands in diagnose_design,
# the function default_diagnosands() is used,
# which is reproduced below.
alpha <- 0.05
default_diagnosands <-
declare_diagnosands(
mean_estimand = mean(estimand),
mean_estimate = mean(estimate),
bias = mean(estimate - estimand),
sd_estimate = sqrt(pop.var(estimate)),
rmse = sqrt(mean((estimate - estimand) ^ 2)),
power = mean(p.value <= alpha),
coverage = mean(estimand <= conf.high & estimand >= conf.low)
)
diagnose_design(
simulations_df,
diagnosands = default_diagnosands
)
# A longer list of potentially useful diagnosands might include:
extended_diagnosands <-
declare_diagnosands(
mean_estimand = mean(estimand),
mean_estimate = mean(estimate),
bias = mean(estimate - estimand),
sd_estimate = sd(estimate),
rmse = sqrt(mean((estimate - estimand) ^ 2)),
power = mean(p.value <= alpha),
coverage = mean(estimand <= conf.high & estimand >= conf.low),
mean_se = mean(std.error),
type_s_rate = mean((sign(estimate) != sign(estimand))[p.value <= alpha]),
exaggeration_ratio = mean((estimate/estimand)[p.value <= alpha]),
var_estimate = pop.var(estimate),
mean_var_hat = mean(std.error^2),
prop_pos_sig = mean(estimate > 0 & p.value <= alpha),
mean_ci_length = mean(conf.high - conf.low)
)
diagnose_design(
simulations_df,
diagnosands = extended_diagnosands
)
}