constraint_matrices {clubSandwich}

Create constraint matrices

Description

Helper functions to create common types of constraint matrices, for use with Wald_test to conduct Wald-type tests of linear contrasts from a fitted regression model.

Usage

constrain_zero(constraints, coefs, reg_ex = FALSE)

constrain_equal(constraints, coefs, reg_ex = FALSE)

constrain_pairwise(constraints, coefs, reg_ex = FALSE, with_zero = FALSE)

Arguments

constraints

Set of constraints to test. Can be logical (using TRUE to specify which coefficients to constrain), integer (specify the index of coefficients to constrain), character (specify the names of the coefficients to constrain), or a regular expression.

coefs

Vector of coefficient estimates, used to determine the column dimension of the constraint matrix. Can be omitted if the function is called inside Wald_test().

reg_ex

Logical indicating whether constraints should be interpreted as a regular expression. Defaults to FALSE.

with_zero

Logical indicating whether coefficients should also be compared to zero. Defaults to FALSE.

Details

Constraints can be specified as character vectors, regular expressions (with reg_ex = TRUE), integer vectors, or logical vectors.

constrain_zero() Creates a matrix that constrains a specified set of coefficients to all be equal to zero.

constrain_equal() Creates a matrix that constrains a specified set of coefficients to all be equal.

constrain_pairwise() Creates a list of constraint matrices consisting of all pairwise comparisons between a specified set of coefficients. If with_zero = TRUE, then the list will also include a set of constraint matrices comparing each coefficient to zero.

Value

A matrix or list of matrices encoding the specified set of constraints.

See Also

Wald_test

Examples

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

Duncan_coefs <- coef(Duncan_fit)

# The following are all equivalent
constrain_zero(constraints = c("typeprof:income","typewc:income"), 
               coefs = Duncan_coefs)
constrain_zero(constraints = ":income", coefs = Duncan_coefs, 
               reg_ex = TRUE)
constrain_zero(constraints = 5:6, coefs = Duncan_coefs)
constrain_zero(constraints = c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE), 
               coefs = Duncan_coefs)

# The following are all equivalent
constrain_equal(c("typebc:education","typeprof:education","typewc:education"), 
                Duncan_coefs)
constrain_equal(":education", Duncan_coefs, reg_ex = TRUE)
constrain_equal(7:9, Duncan_coefs)
constrain_equal(c(FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,TRUE), 
                Duncan_coefs)

# Test pairwise equality of the education slopes
constrain_pairwise(":education", Duncan_coefs,
                   reg_ex = TRUE)

# Test pairwise equality of the income slopes, plus compare against zero
constrain_pairwise(":income", Duncan_coefs, 
                   reg_ex = TRUE, with_zero = TRUE)
                   
})