## File: constraint_matrices.Rd

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r-cran-clubsandwich 0.5.3-1
 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/Wald_test.R \name{constraint_matrices} \alias{constraint_matrices} \alias{constrain_zero} \alias{constrain_equal} \alias{constrain_pairwise} \title{Create constraint matrices} \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{ \item{constraints}{Set of constraints to test. Can be logical (using \code{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.} \item{coefs}{Vector of coefficient estimates, used to determine the column dimension of the constraint matrix. Can be omitted if the function is called inside \code{Wald_test()}.} \item{reg_ex}{Logical indicating whether \code{constraints} should be interpreted as a regular expression. Defaults to \code{FALSE}.} \item{with_zero}{Logical indicating whether coefficients should also be compared to zero. Defaults to \code{FALSE}.} } \value{ A matrix or list of matrices encoding the specified set of constraints. } \description{ Helper functions to create common types of constraint matrices, for use with \code{\link{Wald_test}} to conduct Wald-type tests of linear contrasts from a fitted regression model. } \details{ Constraints can be specified as character vectors, regular expressions (with \code{reg_ex = TRUE}), integer vectors, or logical vectors. \code{constrain_zero()} Creates a matrix that constrains a specified set of coefficients to all be equal to zero. \code{constrain_equal()} Creates a matrix that constrains a specified set of coefficients to all be equal. \code{constrain_pairwise()} Creates a list of constraint matrices consisting of all pairwise comparisons between a specified set of coefficients. If \code{with_zero = TRUE}, then the list will also include a set of constraint matrices comparing each coefficient to zero. } \examples{ 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) } \seealso{ \code{\link{Wald_test}} }