diagnosand_handler {DeclareDesign}

R Documentation

Declare diagnosands

Description

Declare diagnosands

Usage

diagnosand_handler(data, ..., subset = NULL, alpha = 0.05, label)

declare_diagnosands(..., handler = diagnosand_handler, label = NULL)

Arguments

data	A data.frame.
...	A set of new diagnosands.
subset	A subset of the simulations data frame within which to calculate diagnosands e.g. subset = p.value < .05.
alpha	Alpha significance level. Defaults to .05.
label	Label for the set of diagnosands.
handler	a tidy-in, tidy-out function

Details

If term is TRUE, the names of ... will be returned in a term column, and inquiry will contain the step label. This can be used as an additional dimension for use in diagnosis.

Diagnosands summarize the simulations generated by diagnose_design or simulate_design. Typically, the columns of the resulting simulations data.frame include the following variables: estimate, std.error, p.value, conf.low, conf.high, and inquiry. Many diagnosands will be a function of these variables.

Value

a function that returns a data.frame

Examples

design <- 
  declare_model(
    N = 500, 
    U = rnorm(N),
    Y_Z_0 = U, 
    Y_Z_1 = U + rnorm(N, mean = 2, sd = 2)
  ) + 
  declare_assignment(Z = complete_ra(N)) + 
  declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) + 
  declare_estimator(Y ~ Z, inquiry = my_inquiry) + 
  declare_measurement(Y = reveal_outcomes(Y ~ Z))

## Not run: 
# using built-in defaults:
diagnosis <- diagnose_design(design)
diagnosis

## End(Not run)

# You can choose your own diagnosands instead of the defaults e.g.,

my_diagnosands <-
  declare_diagnosands(median_bias = median(estimate - inquiry))
## Not run: 
diagnosis <- diagnose_design(design, diagnosands = my_diagnosands)
diagnosis

## End(Not run)
## Not run: 
design <- set_diagnosands(design, diagnosands = my_diagnosands)
diagnosis <- diagnose_design(design)
diagnosis

## End(Not run)

# 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)
  )

# 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 = estimate > 0 & p.value <= alpha,
    mean_ci_length = mean(conf.high - conf.low)
  )

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