Version 0.5.0 of clubSandwich introduced a new syntax
for Wald_test(), a function for conducting tests of
multiple-constraint hypotheses. In previous versions, this function was
poorly documented and, consequently, probably little used. This vignette
will demonstrate the new syntax.
For purposes of illustration, I will use the STAR data
(available in the AER package), which is drawn from a
randomized trial evaluating the effects of elementary school class size
on student achievement. The data consist of individual-level measures
for students in each of several dozen schools. For purposes of
illustration, I will look at effects on math performance in first grade.
Treatment conditions (the variable called stark) were
assigned at the classroom level, and consisted of either a) a
regular-size class, b) a small-size class, or c) a regular-size class
but with the addition of a teacher’s aide. In all of what follows, I
will cluster standard errors by school in order to allow for
generalization to a super-population of schools.
library(clubSandwich)
data(STAR, package = "AER")
# clean up a few variables
levels(STAR$stark)[3] <- "aide"
levels(STAR$schoolk)[1] <- "urban"
STAR <- subset(STAR,
!is.na(schoolidk),
select = c(schoolidk, schoolk, stark, gender, ethnicity, math1, lunchk))
head(STAR)## schoolidk schoolk stark gender ethnicity math1 lunchk
## 1137 63 rural small female cauc 538 non-free
## 1143 20 suburban small female afam 592 non-free
## 1183 19 urban aide male afam NA free
## 1277 69 rural regular male cauc 584 non-free
## 1292 79 rural small male cauc 545 free
## 1308 5 rural regular male cauc 553 freeThe Wald_test() function can be used to conduct
hypothesis tests that involve multiple constraints on the regression
coefficients. Consider a linear model for an outcome \(Y_{ij}\) regressed on a \(1 \times p\) row vector of predictors \(\mathbf{x}_{ij}\) (which might include a
constant intercept term): \[
Y_{ij} = \mathbf{x}_{ij} \boldsymbol\beta + \epsilon_{ij}
\] The regression coefficient vector is \(\boldsymbol\beta\). In quite general terms,
a set of constraints on the regression coefficient vector can be
expressed in terms of a \(q \times p\)
matrix \(\mathbf{C}\), where each row
of \(\mathbf{C}\) corresponds to one
constraint. A joint null hypothesis is then \(H_0: \mathbf{C} \boldsymbol\beta =
\mathbf{0}\), where \(\mathbf{0}\) is a \(q \times 1\) vector of zeros.1
Wald-type test are based on the test statistic \[
Q = \left(\mathbf{C}\boldsymbol{\hat\beta}\right)' \left(\mathbf{C}
\mathbf{V}^{CR} \mathbf{C}'\right)^{-1}
\left(\mathbf{C}\boldsymbol{\hat\beta}\right),
\] where \(\boldsymbol{\hat\beta}\) is the estimated
regression coefficient vector and \(\mathbf{V}^{CR}\) is a cluster-robust
variance matrix. If the number of clusters is sufficiently large, then
the distribution of \(Q\) under the
null hypothesis is approximately \(\chi^2(q)\). Tipton
& Pustejovsky (2015) investigated a wide range of other
approximations to the null distribution of \(Q\), many of which are included as options
in Wald_test(). Based on a large simulation, they (…er…we…)
recommended a method called the “approximate Hotelling’s \(T^2\)-Z” test, or “AHZ.” This test
approximates the distribution of \(Q /
q\) by a \(T^2\) distribution,
which is a multiple of an \(F\)
distribution, with numerator degrees of freedom \(q\) and denominator degrees of freedom
based on a generalization of the Satterthwaite approximation.
The Wald_test() function has three main arguments:
args(Wald_test)## function (obj, constraints, vcov, test = "HTZ", tidy = FALSE,
## ...)
## NULLobj argument is used to specify the estimated
regression model on which to perform the test,constraints argument is a \(\mathbf{C}\) matrix expressing the set of
constraints to test, andvcov argument is a cluster-robust variance matrix,
which is used to construct the test statistic. (Alternately,
vcov can be the type of cluster-robust variance matrix to
construct, in which case it will be computed internally.)By default, Wald_test() will use the HTZ small-sample
approximation. Other options are available (via the test
argument) but not recommended for routine use. The optional
tidy argument will be demonstrated below.
Returning to the STAR data, let’s suppose we want to examine
differences in math performance across class sizes. This can be done
with a simple linear regression model, while clustering the standard
errors by schoolidk. The estimating equation is \[
\left(\text{Math}\right)_{ij} = \beta_0 + \beta_1
\left(\text{small}\right)_{ij} + \beta_2 \left(\text{aide}\right)_{ij} +
e_{ij},
\] which can be estimated in R as follows:
lm_trt <- lm(math1 ~ stark, data = STAR)
V_trt <- vcovCR(lm_trt, cluster = STAR$schoolidk, type = "CR2")
coef_test(lm_trt, vcov = V_trt)## Coef. Estimate SE t-stat d.f. (Satt) p-val (Satt) Sig.
## (Intercept) 531.727 2.78 191.506 59.9 <0.001 ***
## starksmall 9.469 2.30 4.114 65.6 <0.001 ***
## starkaide -0.483 1.86 -0.259 65.6 0.796In this estimating equation, the coefficients \(\beta_1\) and \(\beta_2\) represent treatment effects, or
differences in average math scores relative to the reference level of
stark, which in this case is a regular-size class. The
t-statistics and p-values reported by coef_test are
separate tests of the null hypotheses that each of these coefficients
are equal to zero, meaning that there is no difference between the
specified treatment condition and the reference level. We might want to
instead test the joint null hypothesis that there are no
differences among any of the conditions. This null can be
expressed by a set of multiple constraints on the parameters: \(\beta_1 = 0\) and \(\beta_2 = 0\).
To test the null hypothesis that \(\beta_1 = \beta_2 = 0\) based on the treatment effects model specification, we can use:
C_trt <- matrix(c(0,0,1,0,0,1), 2, 3)
C_trt## [,1] [,2] [,3]
## [1,] 0 1 0
## [2,] 0 0 1Wald_test(lm_trt, constraints = C_trt, vcov = V_trt)## test Fstat df_num df_denom p_val sig
## HTZ 10.2 2 65.3 <0.001 ***The result includes details about the form of test
computed, the \(F\)-statistic, the
numerator and denominator degrees of freedom used to compute the
reference distribution, and the \(p\)-value corresponding to the specified
null hypothesis. In this example, \(p =
0.000141\), so we can rule out the null hypothesis that there are
no differences in math performance across conditions.
The representation of null hypotheses as arbitrary constraint
matrices is useful for developing theory about how to test such
hypotheses, but it is not all that helpful for actually running
tests—constructing constraint matrices “by hand” is just too cumbersome
of an exercise. Moreover, \(\mathbf{C}\) matrices typically follow one
of a small number of patterns. Two common use cases are a) constraining
a set of \(q > 1\) parameters to all
be equal to zero and b) constraining a set of \(q + 1\) parameters to be equal to a common
value. The clubSandwich package now includes a set of
helper functions to create constraint matrices for these common use
cases.
constrain_zero()To constrain a set of \(q\)
regression coefficients to all be equal to zero, the simplest form of
the \(\mathbf{C}\) matrix would consist
of a set of \(q\) rows, where a single
entry in each row would be equal to 1 and the remaining entries would
all be zero. For the lm_trt model, the C matrix would look
like this: \[
\mathbf{C} = \left[\begin{array}{ccc} 0 & 1 & 0 \\ 0 & 0
& 1 \end{array} \right],
\] so that \[
\mathbf{C}\boldsymbol\beta = \left[\begin{array}{ccc} 0 & 1 & 0
\\ 0 & 0 & 1 \end{array} \right] \left[\begin{array}{c} \beta_0
\\ \beta_1 \\ \beta_2 \end{array} \right] = \left[\begin{array}{c}
\beta_1 \\ \beta_2 \end{array} \right],
\] which is set equal to \(\left[\begin{array}{c} 0 \\ 0 \end{array}
\right]\).
The constrain_zero() function will create matrices like
this automatically. The function takes two main arguments:
args(constrain_zero)## function (constraints, coefs, reg_ex = FALSE)
## NULLconstraints argument is used to specify
which coefficients in a regression model to set equal to
zero.coefs argument is the set of estimated regression
coefficients, for which to calculate the constraints.Constraints can be specified by position index, by name, or via a regular expression. To test the joint null hypothesis that average math performance is equal across the three treatment conditions, we need to constrain the second and third coefficients to zero:
constrain_zero(2:3, coefs = coef(lm_trt))## [,1] [,2] [,3]
## [1,] 0 1 0
## [2,] 0 0 1Or equivalently:
constrain_zero(c("starksmall","starkaide"), coefs = coef(lm_trt))## [,1] [,2] [,3]
## [1,] 0 1 0
## [2,] 0 0 1or
constrain_zero("^stark", coefs = coef(lm_trt), reg_ex = TRUE)## [,1] [,2] [,3]
## [1,] 0 1 0
## [2,] 0 0 1Note that if constraints is a regular expression, then
the reg_ex argument needs to be set to
TRUE.
The result of constrain_zero() can then be fed into the
Wald_test() function:
C_trt <- constrain_zero(2:3, coefs = coef(lm_trt))
Wald_test(lm_trt, constraints = C_trt, vcov = V_trt)## test Fstat df_num df_denom p_val sig
## HTZ 10.2 2 65.3 <0.001 ***To reduce redundancy in the syntax, we can also omit the
coefs argument to constrain_zero, so long as
we call it inside of Wald_test2:
Wald_test(lm_trt, constraints = constrain_zero(2:3), vcov = V_trt)## test Fstat df_num df_denom p_val sig
## HTZ 10.2 2 65.3 <0.001 ***constrain_equal()Another common type of constraints involve setting a set of \(q + 1\) regression coefficients to be all equal to a common (but unknown) value (\(q + 1\) because it takes \(q\) constraints to do this). There are many equivalent ways to express such a set of constraints in terms of a \(\mathbf{C}\) matrix. One fairly simple form consists of a set of \(q\) rows, where the entry corresponding to one of the coefficients of interest is equal to -1 and the entry corresponding to another coefficient of interest is equal to 1.
To see how this works, let’s look at a different way of parameterizing our simple model for the STAR data, by using separate intercepts for each treatment condition. The estimating equation would then be \[ \left(\text{Math}\right)_{ij} = \beta_0 \left(\text{regular}\right)_{ij} + \beta_1 \left(\text{small}\right)_{ij} + \beta_2 \left(\text{aide}\right)_{ij} + e_{ij}. \] This model can be estimated in R by dropping the intercept term:
lm_sep <- lm(math1 ~ 0 + stark, data = STAR)
V_sep <- vcovCR(lm_sep, cluster = STAR$schoolidk, type = "CR2")
coef_test(lm_sep, vcov = V_sep)## Coef. Estimate SE t-stat d.f. (Satt) p-val (Satt) Sig.
## starkregular 532 2.78 192 59.9 <0.001 ***
## starksmall 541 2.89 187 65.0 <0.001 ***
## starkaide 531 2.72 195 64.3 <0.001 ***In this parameterization, the coefficients \(\beta_0\), \(\beta_1\), and \(\beta_2\) represent the average math performance levels of students in each of the treatment conditions. The t-tests and p-values now have a very different interpretation because they pertain to the null hypothesis that the average performance level for a given condition is equal to zero. With this separate-intercepts model, the joint null hypothesis that performance levels are equal across conditions amounts to constraining the intercepts to be equal to each other: \(\beta_0 = \beta_1\) and \(\beta_0 = \beta_2\) (note that we don’t need the constraint \(\beta_1 = \beta_2\) because it is implied by the first two).
For the lm_sep model, which has separate intercepts
\(\beta_0\), \(\beta_1\), and \(\beta_2\), the C matrix would look like
this: \[
\mathbf{C} = \left[\begin{array}{ccc} -1 & 1 & 0 \\ -1 & 0
& 1 \end{array} \right],
\] so that \[
\mathbf{C}\boldsymbol\beta = \left[\begin{array}{ccc} -1 & 1 & 0
\\ -1 & 0 & 1 \end{array} \right] \left[\begin{array}{c} \beta_0
\\ \beta_1 \\ \beta_2 \end{array} \right] = \left[\begin{array}{c}
\beta_1 - \beta_0 \\ \beta_2 - \beta_0 \end{array} \right],
\] which is set equal to \(\left[\begin{array}{c} 0 \\ 0 \end{array}
\right]\).
The constrain_equal() function will create matrices like
this automatically, given a set of coefficients to constrain. The syntax
is identical to constrain_zero():
args(constrain_equal)## function (constraints, coefs, reg_ex = FALSE)
## NULLTo test the joint null hypothesis that average math performance is
equal across the three treatment conditions, we can constrain all three
coefficients of lm_sep to be equal:
constrain_equal(1:3, coefs = coef(lm_sep))## [,1] [,2] [,3]
## [1,] -1 1 0
## [2,] -1 0 1Or equivalently:
constrain_equal(c("starkregular","starksmall","starkaide"), coefs = coef(lm_sep))## [,1] [,2] [,3]
## [1,] -1 1 0
## [2,] -1 0 1or
constrain_equal("^stark", coefs = coef(lm_sep), reg_ex = TRUE)## [,1] [,2] [,3]
## [1,] -1 1 0
## [2,] -1 0 1Just as with constrain_zero, if constraints
is a regular expression, then the reg_ex argument needs to
be set to TRUE.
This constraint matrix can then be fed into
Wald_test():
C_sep <- constrain_equal("^stark", coefs = coef(lm_sep), reg_ex = TRUE)
Wald_test(lm_sep, constraints = C_sep, vcov = V_sep)## test Fstat df_num df_denom p_val sig
## HTZ 10.2 2 65.3 <0.001 ***or equivalently:
Wald_test(lm_sep, constraints = constrain_equal(1:3), vcov = V_sep)## test Fstat df_num df_denom p_val sig
## HTZ 10.2 2 65.3 <0.001 ***Note that these test results are exactly equal to the tests based on
lm_trt with constrain_zero(). They’re
algebraically equivalent—just different ways of parameterizing the same
model and constraints.
Let’s now consider how these functions can be applied in a more complex model. Suppose that we are interested in understanding whether the effect of being in a small class is consistent across schools in different areas, where areas are categorized as urban, suburban, or rural. To answer this question, we need to test for an interaction between urbanicity and treatment condition. One estimating equation that would let us examine this question is: \[ \begin{aligned} \left(\text{Math}\right)_{ij} &= \beta_0 + \beta_1 \left(\text{suburban}\right)_{ij} + \beta_2 \left(\text{rural}\right)_{ij} \\ & \quad + \beta_3 \left(\text{small}\right)_{ij} + \beta_4 \left(\text{aide}\right)_{ij} \\ & \quad\quad + \beta_5 \left(\text{small}\right)(\text{suburban})_{ij} + \beta_6 \left(\text{aide}\right)(\text{suburban})_{ij} \\ & \quad\quad\quad + \beta_{7} \left(\text{small}\right)(\text{rural})_{ij} + \beta_{8} \left(\text{aide}\right)(\text{rural})_{ij} \\ & \quad\quad\quad\quad + \mathbf{x}_{ij} \boldsymbol\gamma + e_{ij}, \end{aligned} \] where \(\mathbf{x}_{ij}\) is a row vector of student characteristics, included just to make the regression look fancier. In this specification, \(\beta_3\) and \(\beta_4\) represent the effects of being in a small class or aide class, compared to being in a regular class, but only for the reference level of urbanicity—in this case, urban schools. The coefficients \(\beta_5, \beta_6, \beta_7, \beta_8\) all represent interactions between treatment condition and urbanicity. Here’s the model, estimated in R:
lm_urbanicity <- lm(math1 ~ schoolk * stark + gender + ethnicity + lunchk, data = STAR)
V_urbanicity <- vcovCR(lm_urbanicity, cluster = STAR$schoolidk, type = "CR2")
coef_test(lm_urbanicity, vcov = V_urbanicity)## Coef. Estimate SE t-stat d.f. (Satt) p-val (Satt)
## (Intercept) 542.62 5.91 91.8599 21.70 <0.001
## schoolksuburban 2.77 6.76 0.4100 28.35 0.6849
## schoolkrural 1.03 6.38 0.1616 30.74 0.8727
## starksmall 9.42 4.56 2.0649 17.10 0.0544
## starkaide -4.27 2.17 -1.9631 16.73 0.0665
## genderfemale 2.14 1.20 1.7773 67.14 0.0800
## ethnicityafam -16.79 4.19 -4.0026 34.94 <0.001
## ethnicityasian 13.19 11.02 1.1963 6.23 0.2751
## ethnicityhispanic 39.23 20.62 1.9028 1.01 0.3067
## ethnicityother 8.86 18.78 0.4720 3.02 0.6690
## lunchkfree -19.37 2.04 -9.4848 57.38 <0.001
## schoolksuburban:starksmall 3.03 6.39 0.4746 28.94 0.6386
## schoolkrural:starksmall -0.31 5.58 -0.0555 34.04 0.9560
## schoolksuburban:starkaide 5.10 3.72 1.3711 28.64 0.1810
## schoolkrural:starkaide 8.16 3.16 2.5857 34.30 0.0141
## Sig.
## ***
##
##
## .
## .
## .
## ***
##
##
##
## ***
##
##
##
## *With this specification, there are several different null hypotheses that we might want to test. For one, perhaps we want to see if there is any variation in treatment effects across different levels of urbanicity. This can be expressed in the null hypothesis that all four interaction terms are zero, or \(H_{0A}: \beta_5 = \beta_6 = \beta_7 = \beta_8 = 0\). With Wald test:
Wald_test(lm_urbanicity,
constraints = constrain_zero("schoolk.+:stark", reg_ex = TRUE),
vcov = V_urbanicity)## test Fstat df_num df_denom p_val sig
## HTZ 1.96 4 37.5 0.121Another possibility is that we might want to focus on variation in the effect of being in a small class or regular class, while ignoring whatever is going on in the aide class condition. Here, the null hypothesis would be simply \(H_{0B}: \beta_5 = \beta_6 = 0\), tested as:
Wald_test(lm_urbanicity,
constraints = constrain_zero("schoolk.+:starksmall", reg_ex = TRUE),
vcov = V_urbanicity)## test Fstat df_num df_denom p_val sig
## HTZ 0.189 2 34.5 0.828In models like the urbanicity-by-treatment interaction specification,
we may need to run multiple tests on the same estimating equation. This
can be accomplished with Wald_test by providing a
list of constraints to the constraints argument.
For example, we could test the hypotheses described above by creating a
list of several constraint matrices and then passing it to
Wald_test:
C_list <- list(
`Any interaction` = constrain_zero("schoolk.+:stark",
coef(lm_urbanicity), reg_ex = TRUE),
`Small vs regular` = constrain_zero("schoolk.+:starksmall",
coef(lm_urbanicity), reg_ex = TRUE)
)
Wald_test(lm_urbanicity,
constraints = C_list,
vcov = V_urbanicity)## $`Any interaction`
## test Fstat df_num df_denom p_val sig
## HTZ 1.96 4 37.5 0.121
##
## $`Small vs regular`
## test Fstat df_num df_denom p_val sig
## HTZ 0.189 2 34.5 0.828Setting the option tidy = TRUE will arrange the output
of all the tests into a single data frame:
Wald_test(lm_urbanicity,
constraints = C_list,
vcov = V_urbanicity,
tidy = TRUE)## hypothesis test Fstat df_num df_denom p_val sig
## Any interaction HTZ 1.960 4 37.5 0.121
## Small vs regular HTZ 0.189 2 34.5 0.828The list of constraints can also be created inside
Wald_test, so that the coefs argument can be
omitted from constrain_zero():
Wald_test(
lm_urbanicity,
constraints = list(
`Any interaction` = constrain_zero("schoolk.+:stark", reg_ex = TRUE),
`Small vs regular` = constrain_zero("schoolk.+:starksmall", reg_ex = TRUE)
),
vcov = V_urbanicity,
tidy = TRUE
)## hypothesis test Fstat df_num df_denom p_val sig
## Any interaction HTZ 1.960 4 37.5 0.121
## Small vs regular HTZ 0.189 2 34.5 0.828The clubSandwich package also provides a further
convenience function, constrain_pairwise() that can be used
in combination with Wald_test() to conduct pairwise
comparisons among a set of regression coefficients. This function
differs from the other two constrain_*() functions because
it returns a list of constraint matrices, each of which
corresponds to a single linear combination of covariates. Specifically,
the constrain_pairwise() function provides a list of
constraints that represent the differences between every possible pair
among a specified set of coefficients. The syntax is very similar to the
other constrain_*() functions.
To demonstrate, consider the separate-intercepts specification of the simpler regression model:
coef_test(lm_sep, vcov = V_sep)## Coef. Estimate SE t-stat d.f. (Satt) p-val (Satt) Sig.
## starkregular 532 2.78 192 59.9 <0.001 ***
## starksmall 541 2.89 187 65.0 <0.001 ***
## starkaide 531 2.72 195 64.3 <0.001 ***This specification is nice because it lets us simply read off the average outcomes for each group. However, we will naturally also want to know about whether there are differences between the groups, so we’ll want to compare the small-class condition to the regular-size class condition, the aide condition to the regular-size class condition, and the small-class condition to the aide condition. Thus, we’ll want comparisons among all three coefficients:
C_pairs <- constrain_pairwise(1:3, coefs = coef(lm_sep))
C_pairs## $`starksmall - starkregular`
## [,1] [,2] [,3]
## [1,] -1 1 0
##
## $`starkaide - starkregular`
## [,1] [,2] [,3]
## [1,] -1 0 1
##
## $`starkaide - starksmall`
## [,1] [,2] [,3]
## [1,] 0 -1 1Feeding these constraints into Wald_test() gives us
significance tests for each pair:
Wald_test(lm_sep, constraints = C_pairs, vcov = V_sep, tidy = TRUE)## hypothesis test Fstat df_num df_denom p_val sig
## starksmall - starkregular HTZ 16.9238 1 65.6 <0.001 ***
## starkaide - starkregular HTZ 0.0673 1 65.6 0.796
## starkaide - starksmall HTZ 17.8137 1 66.9 <0.001 ***The first two of these tests are equivalent to the tests of the
treatment effect coefficients in the other parameterization of the
model. Indeed, the denominator degrees of freedom are identical to the
results of coef_test(lm_trt, vcov = V_trt); the
Fstats here are equal to the squared t-statistics from the
first model:
t_stats <- coef_test(lm_trt, vcov = V_trt)$tstat[2:3]
F_stats <- Wald_test(lm_sep, constraints = C_pairs, vcov = V_sep, tidy = TRUE)$Fstat[1:2]
all.equal(t_stats^2, F_stats)## [1] TRUEIt is important to note that the p-values from the pairwise
comparisons are not corrected for multiplicity.3 For now, please
correct-your-own using p.adjust() or your preferred
method.
Pairwise comparisons might also be of use in the model with treatment-by-urbanicity interactions. Here’s the model results again:
coef_test(lm_urbanicity, vcov = V_urbanicity)## Coef. Estimate SE t-stat d.f. (Satt) p-val (Satt)
## (Intercept) 542.62 5.91 91.8599 21.70 <0.001
## schoolksuburban 2.77 6.76 0.4100 28.35 0.6849
## schoolkrural 1.03 6.38 0.1616 30.74 0.8727
## starksmall 9.42 4.56 2.0649 17.10 0.0544
## starkaide -4.27 2.17 -1.9631 16.73 0.0665
## genderfemale 2.14 1.20 1.7773 67.14 0.0800
## ethnicityafam -16.79 4.19 -4.0026 34.94 <0.001
## ethnicityasian 13.19 11.02 1.1963 6.23 0.2751
## ethnicityhispanic 39.23 20.62 1.9028 1.01 0.3067
## ethnicityother 8.86 18.78 0.4720 3.02 0.6690
## lunchkfree -19.37 2.04 -9.4848 57.38 <0.001
## schoolksuburban:starksmall 3.03 6.39 0.4746 28.94 0.6386
## schoolkrural:starksmall -0.31 5.58 -0.0555 34.04 0.9560
## schoolksuburban:starkaide 5.10 3.72 1.3711 28.64 0.1810
## schoolkrural:starkaide 8.16 3.16 2.5857 34.30 0.0141
## Sig.
## ***
##
##
## .
## .
## .
## ***
##
##
##
## ***
##
##
##
## *Suppose that we are interested in the effect of small versus regular
size classes, and in particular whether this effect varies across
schools in different areas. The coefficients on
schoolksuburban:starksmall and
schoolkrural:starksmall already give us the differences in
treatment effects between suburban schools versus urban schools and
between rural schools versus urban schools. The difference between these
coefficients gives us the difference in treatment effects between
suburban schools and rural schools. We can look at all three of these
contrasts using constrain_pairwise() by setting the option
with_zero = TRUE:
Wald_test(lm_urbanicity,
constraints = constrain_pairwise(":starksmall", reg_ex = TRUE, with_zero = TRUE),
vcov = V_urbanicity,
tidy = TRUE)## hypothesis test Fstat df_num
## schoolksuburban:starksmall HTZ 0.22526 1
## schoolkrural:starksmall HTZ 0.00308 1
## schoolkrural:starksmall - schoolksuburban:starksmall HTZ 0.36471 1
## df_denom p_val sig
## 28.9 0.639
## 34.0 0.956
## 24.4 0.551Again, the results of the first two tests are identical to the
t-tests reported in coef_test().
All of the preceding examples were based on ordinary linear
regression models with clustered standard errors. However,
Wald_test() and its helper functions all work identically
for all of the other models with supporting clubSandwich
methods, including nlme::lme(), nlme::gls(),
lme4::lmer(), rma.uni(),
rma.mv(), and robu(), among others.
In Pustejovsky & Tipton (2018) we used a more general formulation of multiple-constraint null hypotheses, expressed as \(H_0: \mathbf{C} \boldsymbol\beta = \mathbf{d}\) for some fixed \(q \times 1\) vector \(\mathbf{d}\). In practice, it’s often possible to modify the \(\mathbf{C}\) matrix so that \(\mathbf{d}\) can always be set to \(\mathbf{0}\).↩︎
How does this work? If we omit the coefs
argument, constrain_zero() acts as a functional, by
returning a function equivalent to
function(coefs) constrain_zero(constraints, coefs = coefs).
If this function is fed into the constraints argument of
Wald_test(), Wald_test() recognizes that it is
a function and evaluates the function with coef(obj). It’s
a kinda-sorta hacky substitute for lazy evaluation. If you have
suggestions for how to do this more elegantly, please send them my
way.↩︎
Options to include multiplicity corrections (Bonferroni, Holm, Benjamini-Hochberg, etc.) might be included in a future release. Reach out if this is of interest to you.↩︎