Wald_test | R Documentation |
Wald_test
reports Wald-type tests of linear contrasts from a fitted
linear regression model, using a sandwich estimator for the
variance-covariance matrix and a small sample correction for the p-value.
Several different small-sample corrections are available.
Wald_test(
obj,
constraints,
vcov,
null_constant = 0,
test = "HTZ",
tidy = FALSE,
adjustment_method = "none",
...
)
obj |
Fitted model for which to calculate Wald tests. |
constraints |
constraint or list of multiple constraints to test. See details and examples. |
vcov |
Variance covariance matrix estimated using |
null_constant |
vector of null values or list of such vectors for each
set of constraints to test. For a single |
test |
Character vector specifying which small-sample correction(s) to
calculate. The following corrections are available: |
tidy |
Logical value controlling whether to tidy the test results. If
|
adjustment_method |
A character string indicating a multiple comparisons
correction to apply to p-values in instances where multiple tests are run.
Possible options are from |
... |
Further arguments passed to |
Constraints can be specified directly as q X p matrices or
indirectly through constrain_equal
,
constrain_zero
, or constrain_pairwise
. By
default, each constraint will be tested against the null hypothesis that it
equal to a zero vector. Non-zero values for null-hypotheses can be
specified using the null_constant
argument.
A list of test results.
vcovCR
, constrain_equal
,
constrain_zero
, constrain_pairwise
if (requireNamespace("carData", quietly = TRUE)) withAutoprint({
data(Duncan, package = "carData")
Duncan$cluster <- sample(LETTERS[1:8], size = nrow(Duncan), replace = TRUE)
Duncan_fit <- lm(prestige ~ 0 + type + income + type:income + type:education, data=Duncan)
# Note that type:income terms are interactions because main effect of income is included
# but type:education terms are separate slopes for each unique level of type
# Test equality of intercepts
Wald_test(Duncan_fit,
constraints = constrain_equal(1:3),
vcov = "CR2", cluster = Duncan$cluster)
# Test equality of type-by-education slopes
Wald_test(Duncan_fit,
constraints = constrain_equal(":education", reg_ex = TRUE),
vcov = "CR2", cluster = Duncan$cluster)
# Pairwise comparisons of type-by-education slopes
Wald_test(Duncan_fit,
constraints = constrain_pairwise(":education", reg_ex = TRUE),
vcov = "CR2", cluster = Duncan$cluster)
# Test type-by-income interactions
Wald_test(Duncan_fit,
constraints = constrain_zero(":income", reg_ex = TRUE),
vcov = "CR2", cluster = Duncan$cluster)
# Pairwise comparisons of type-by-income interactions
Wald_test(Duncan_fit,
constraints = constrain_pairwise(":income", reg_ex = TRUE, with_zero = TRUE),
vcov = "CR2", cluster = Duncan$cluster)
# Pairwise comparisons of type-by-education slopes, with two tests and
# multiple comparisons p-value adjustment
Wald_test(Duncan_fit,
constraints = constrain_pairwise(":education", reg_ex = TRUE),
vcov = "CR2",
cluster = Duncan$cluster,
test = c("HTZ","chi-sq"),
adjustment_method = "holm")
})
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