plm objects estimated by
random effects, which occurred when a user-specified clustering variable
was at a higher level than the random effects.plm objects with nested random
effects (effects = "nested").plm objects. See ?plm.Wald_test() labeled results when
test = "Naive-Fp".New function linear_contrast() calculates robust
confidence intervals and p-values for linear contrasts of regression
coefficients from a fitted model. Works with
constrain_pairwise() and other constrain_*()
helper functions.
Corrected precision of unit test leading to error on M1mac.
Wald_test() gains an option for test = “Naive-Fp”,
which uses denominator degrees of freedom equal to the number of
clusters minus the number of coefficients in the fitted model.coef_test() and conf_int() gain an option
for test = “naive-tp”, which uses denominator degrees of freedom equal
to the number of clusters minus the number of coefficients in the fitted
model.Corrected a bug in the Satterthwaite degrees of freedom calculations for models that include only an intercept.
Output of coef_test() and conf_int()
now include a variable containing the coefficient names, so that the
results are “tidy.”
conf_int() now includes an option to report a
p-value for each coefficient.
coef_test() now reports degrees of freedom for
test = 'z' and test = 'naive-t'.
vcovCR() now provides a more informative error
message when the clustering variable is a constant.
vcovCR() now handles models estimated using analytic
weights, where some weights are equal to zero. Results are consistent
with omitting observations with weights of zero.
Added more informative error messages for
Wald_test() and conf_int(), triggered if the
test argument does not match any of the available tests.
Corrected a bug in findCluster.rma.mv(), which threw
an error if a random effects factor in the rma.mv model had unobserved
levels.
Corrected a bug in Wald_test(), which threw an error
for tests of intercept-only models.
Fixed a minor bug in print method for Wald_test()
results, which threw an error when the p-value was
NA.
impute_covariance_matrix():
pattern_covariance_matrix(), which creates
a covariance matrix based on a specified pattern of correlations between
different categories of effects.rma.mv objects, which
previously led to incorrect identification of clustering variables in
models with multiple levels of random effects, where at least one set of
random effects has inner | outer structure.Wald_test()Wald_test() now uses new helper functions
constrain_zero(), constrain_equal(), and
constrain_pairwise() to specify constraint
matrices.
Wald_test() gains an argument tidy.
When TRUE, results for a list of tests will be tidied into
a single data.frame.
Output of Wald_test() now includes both numerator
and denominator degrees of freedom.
Corrected bug in methods for plm objects, which
occurred when “within” models included cluster-level interactions.
Previously main effects of cluster-level variables were not getting
dropped from model_matrix.plm().
Corrected bugs in methods for robu objects
constrain_equal() and
constrain_zero() when called on robu objects.Added methods for lmerMod objects fitted by
lme4::lmer().
Updated internals to use inherits() instead of
checking class() directly.
Added t statistics to output of
coef_test().
Fixed a bug in get_index_order(), an internal
function used with plm objects. Previously, the function assumed that
both individual and time indices were specified in the plm
call. The new function works even when zero or one indices are
specified.
impute_covariance_matrix() now drops unobserved
factor levels.
updated method for handling residuals from rma.uni
and rma.mv objects, for consistency with metafor
2.1-0.
Added conf_int() to provide easy cluster-robust
confidence intervals.
Added examples to documentation for conf_int() and
coef_test().
Added coefs option to coef_test() to
allow testing of subsets of coefficients.
Updated tests to use carData instead of car
package.
Added methods for ivreg objects.
Added methods for mlm objects.
Updated residuals_CS.plm to account for changes in
plm 1.6-6.
Added methods for glm objects.
Provide facility to cluster at higher level than highest random
effects for lme and gls objects.
Added impute_covariance_matrix() utility function
for multivariate meta-analysis.
Updated methods for plm objects to account for changes in plm 1.6-6.
Added documentation of type options in
vcovCR().
Added examples for all vcovCR() methods.
Added bread() methods for all supported model
classes.
vcovCR() is now calculated using
bread(), and carries attributes for bread,
est_mat, and adjustment matrices.
vcovCR() gains a form argument to
obtain just the meat of the sandwich, or to use a user-specified bread
matrix.
Refactored internal functions for degrees of freedom calculation to improve speed and memory usage.
Bug fixes:
nobs.plm() method to handle first-differenced
models