DeclareDesign (version 1.0.8)

declare_test: Declare test

Description

Declares an test which generates a test statistic and associated inferential statistics.

Use of declare_test is identical to use of declare_estimator. Use declare_test for hypothesis testing with no specific inquiry in mind; use declare_estimator for hypothesis testing when you can link each estimate to an inquiry. For example, declare_test could be used for a K-S test of distributional equality and declare_estimator for a difference-in-means estimate of an average treatment effect.

See declare_estimator help for an explanation of how to use method_handler, which is used identically in both declare_estimator and declare_test. The main difference between declare_estimator and declare_test is that declare_test does not link with an explicit inquiry.

Usage

declare_test(..., handler = label_test(method_handler), label = "test")
label_test(fn)

Value

A function that accepts a data.frame as an argument and returns a data.frame containing the value of the test statistic and other inferential statistics.

Arguments

...

arguments to be captured, and later passed to the handler

handler

a tidy-in, tidy-out function

label

a string describing the step

fn

A function that takes a data.frame as an argument and returns a data.frame with test statistics as columns.

Details

label_test takes a data-in-data out function to fn, and returns a data-in-data-out function that first runs the provided test function fn and then appends a label for the test.

See Also

See declare_estimator for documentation of the method_handler function.

Examples

# Balance test F test

balance_test_design <-
  declare_model(
    N = 100, 
    cov1 = rnorm(N), 
    cov2 = rnorm(N), 
    cov3 = rnorm(N)
  ) +
  declare_assignment(Z = complete_ra(N, prob = 0.2)) +
  declare_test(Z ~ cov1 + cov2 + cov3, .method = lm_robust, .summary = glance)
  
if (FALSE) {
diagnosis <- diagnose_design(
  design = balance_test_design,
  diagnosands = declare_diagnosands(
  false_positive_rate = mean(p.value <= 0.05))
)
}

# K-S test of distributional equality

ks_test <- function(data) {
  test <- with(data, ks.test(x = Y[Z == 1], y = Y[Z == 0]))
  data.frame(statistic = test$statistic, p.value = test$p.value)
}

distributional_equality_design <-
  declare_model(
    N = 100, 
    Y_Z_1 = rnorm(N), 
    Y_Z_0 = rnorm(N, sd = 1.5)
  ) + 
  declare_assignment(Z = complete_ra(N, prob = 0.5)) + 
  declare_measurement(Y = reveal_outcomes(Y ~ Z)) + 
  declare_test(handler = label_test(ks_test), label = "ks-test")
  
if (FALSE) {
diagnosis <- diagnose_design(
  design = distributional_equality_design,
  diagnosands = declare_diagnosands(power = mean(p.value <= 0.05))
) 
}

# Thanks to Jake Bowers for this example

library(coin) 

our_ttest <- function(data) {
  res <- coin::oneway_test(
    outcome ~ factor(Xclus),
    data = data,
    distribution = "asymptotic"
  )
  data.frame(p.value = pvalue(res)[[1]])
}

ttest_design <- 
  declare_model(
    N = 100, 
    Xclus = rbinom(n = N, size = 1, prob = 0.2), 
    outcome = 3 + rnorm(N)) +
  declare_test(handler = label_test(our_ttest), label = "t-test")
  
if (FALSE) {
diagnosis <- diagnose_design(
  design = ttest_design,
  diagnosands = declare_diagnosands(
    false_positive_rate = mean(p.value <= 0.05))
)
}