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.
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.
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.
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)
#> Loading required package: survival
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))
)
}