View source: R/Wald_test_wildmeta.R
Wald_test_cwb | R Documentation |
Calculate p-values for single coefficient and multiple contrast hypothesis tests using cluster wild bootstrapping.
Wald_test_cwb(
full_model,
constraints,
R,
cluster = NULL,
auxiliary_dist = "Rademacher",
adjust = "CR0",
type = "CR0",
test = "Naive-F",
seed = NULL,
future_args = NULL
)
full_model |
Model fit using |
constraints |
A q X p constraint matrix be tested. Alternately, a
function to create such a matrix, specified using
|
R |
Number of bootstrap replications. |
cluster |
Vector of identifiers indicating which observations
belong to the same cluster. If |
auxiliary_dist |
Character string indicating the auxiliary distribution to be used for cluster wild bootstrapping, with available options: "Rademacher", "Mammen", "Webb six", "uniform", "standard normal". The default is set to "Rademacher." We recommend the Rademacher distribution for models that have at least 10 clusters. For models with less than 10 clusters, we recommend the use of "Webb six" distribution. |
adjust |
Character string specifying which small-sample adjustment
should be used to multiply the residuals by. The available options are
|
type |
Character string specifying which small-sample adjustment is used
to calculate the Wald test statistic. The available options are
|
test |
Character string specifying which (if any) small-sample
adjustment is used in calculating the test statistic. Default is
|
seed |
Optional seed value to ensure reproducibility. |
future_args |
Optional list of additional arguments passed to the
|
A data.frame
containing the name of the test, the adjustment
used for the bootstrap process, the type of variance-covariance matrix
used, the type of test statistic, the number of bootstrap replicates, and
the bootstrapped p-value.
library(clubSandwich)
library(robumeta)
model <- robu(d ~ 0 + study_type + hrs + test,
studynum = study,
var.eff.size = V,
small = FALSE,
data = SATcoaching)
C_mat <- constrain_equal(1:3, coefs = coef(model))
Wald_test_cwb(full_model = model,
constraints = C_mat,
R = 12)
# Equivalent, using constrain_equal()
Wald_test_cwb(full_model = model,
constraints = constrain_equal(1:3),
R = 12)
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