vcovCL() models from feols() (from package fixest) are now also recognized
as linear regression models (in addition to models from lm()).
FIXME: Check df correction and inform Laurent.In meatPL() the case of cross-section data (i.e., all elements of order.by
being equal) is handled consistently even if aggregate = TRUE (reported by
Christof Schoetz).
Fix bread() method for survreg() objects in case the latter already used a
"robust" sandwich variance. In that case $naive.var rather than $var has
to be used for the bread (reported by Daniel Klinenberg).
Improve warnings in vcovHC() for hat values numerically equal to 1. (Suggested by
Sanford Weisberg and John Fox.)
Added jackknife estimator in all vcovBS() methods (suggested by Joe Ritter).
This is of particular practical interest in linear regression models where the
(clustered) jackknife and the (clustered) HC3 (or CV3, without cluster adjustment)
estimator coincide. In nonlinear models (including non-Gaussian GLMs) the jackknife and
the HC3 estimator do not coincide but the jackknife might still be a useful
alternative when the HC3 cannot be computed.
Added a new convenience interface vcovJK() for the jackknife covariance whose
default method simply calls vcovBS(..., type = "jackknife") (also suggested by
Joe Ritter, for more details see the previous item).
Added fractional-random-weight bootstrap, also known as Bayesian bootstrap, in all
vcovBS() methods (suggested by Noah Greifer and Grant McDermott). This is an
alternative to the classical xy bootstrap which has the computational advantage
that all observations are always part of the bootstrap samples with positive
weights drawn from a Dirichlet distribution. As weights can become close to zero
but no observations are excluded completely, this can stabilize the computation of
models that are not well-defined on all subsets.
Support weights, offsets, and different fitting methods in lm and glm objects
in the respective vcovBS() methods (reported by Noah Greifer).
More verbose error messages in bwAndrews() and bwNeweyWest() when bandwidth
cannot be computed, e.g., due to singular regressor variables (suggested by
Andrei V. Kostyrka).
Fix bread() method for coxph() objects in case the latter already used a
"robust" sandwich variance. In that case $naive.var rather than $var has
to be used for the bread (reported by Alec Todd).
Fix plm::plm(..., index = ...) calls which incorrectly used indexes = ...
(as in plm.data(), reported by Kevin Tappe).
Added new argument aggregate = TRUE to meatPL() which is thus inherited by
vcovPL(). By default, this still yields the Driscoll & Kraay (1998) covariance
matrix. When setting aggregate = FALSE the cross-sectional and cross-serial
correlation is set to zero, yielding the "pure" panel Newey-West covariance
matrix.
Bug fix in vcovCL(..., type = "HC2") for glm objects or lm objects
with weights. The code had erroneously assumed that the hat matrices were
all symmetric (as in the lm case without weights). This is corrected now.
(Detected and reported by Bixi Zhang.)
Issue a warning in vcovHC() for HC2/HC3/HC4/HC4m/HC5 if any of the hat values
are numerically equal to 1. This leads to numerically unstable covariances,
in the most extreme case NaN because the associated residuals are equal to
0 and divided by 0. (Suggested by Ding Peng and John Fox.)
Speed improvement in vcovBS.lm(): For "xy" bootstrap, .lm.fit() rather than
lm.fit() is used which is somewhat more efficient in some situations (suggested
by Grant McDermott). For "residual" and wild bootstrap, the bootstrap by default
still samples coefficients via QR decomposition in each iteration (qrjoint = FALSE)
but may alternatively sample the dependent variable and then apply the QR
decomposition jointly only once (qrjoint = TRUE). If the sample size (and the
number of coefficients) is large, then qrjoint = TRUE may be significantly faster
while requiring much more memory (proposed by Alexander Fischer).
Enable passing score matrix (as computed by estfun()) directly to
bwAndrews() and bwNeweyWest(). If this is used, the score matrix should
either have a column (Intercept) or the weights argument should be set
appropriately to identify the column pertaining to the intercept (if any).
The vignettes have been tweaked so that they still "run" without technical errors when suggested packages (listed in the VignetteDepends) are not available. This is achieved by defining replacement functions that do not fail but lead to partially non-sensical output. A warning is added in the vignettes if any of the replacements is used.
Extended the "Getting started" page with information on how to use sandwich in combination with the modelsummary package (Arel-Bundock) based on broom infrastructure (Robinson, Hayes, Couch). (Based on ideas from Grant McDermott.) https://sandwich.R-Forge.R-project.org/articles/sandwich.html
Catch NA observations in cluster and/or order.by indexes for vcovCL(),
vcovBS(), vcovPL(), and vcovPC(). Such missing observations cannot be
handled in the covariance extractor functions but need to be addressed prior
to fitting the model object, either by omitting these observations or by
imputing the missing values. (Raised by Alexander Fischer on StackOverflow
https://stackoverflow.com/questions/64849935/clustered-standard-errors-and-missing-values.)
In vcovHC() if there are estfun() rows that are all zero and type = "const",
then the working residuals for lm and glm objects are obtained via
residuals() rather than estfun(). (Prompted by an issue raised by
Alex Torgovitsky.)
Release of version 3.0-0 accompanying the publication of the paper
"Various Versatile Variances: An Object-Oriented Implementation of
Clustered Covariances in R." together with Susanne Koell and Nathaniel Graham
in the Journal of Statistcal Software at https://doi.org/10.18637/jss.v095.i01.
The paper is also provided as a vignette in the package as
vignette("sandwich-CL", package = "sandwich").
Improved or clarified notation in Equations 6, 9, 21, and 22 (based on feedback from Bettina Gruen).
The documentation of the HC1 bias correction for clustered covariances
in vignette("sandwich-CL", package = "sandwich") has been corrected (Equation 15).
While both the code in vcovCL() and the corresponding documentation ?vcovCL
always correctly used (n-1)/(n-k), the vignette had incorrectly stated it as
n/(n-k). (Reported by Yves Croissant.)
The package is also accompanied by a pkgdown website on R-Forge now:
https://sandwich.R-Forge.R-project.org/
This essentially uses the previous content of the package (documentation,
vignettes, NEWS) and just formatting was enhanced. But a few new features
were also added:
pkgdown page (but not shipped in the
package) providing an introduction to the package and listing all
variance-covariance functions provided with links to further details.pkgdown page (but also not shipped
in the package) linking the Sweave-based PDF vignettes so that they are
easily accessible online.README with very brief overview for the pkgdown title page.All kernel weights functions in kweights() are made symmetric around zero now
(suggested by Christoph Hanck). The quadratic spectral kernal is approximated
by exp(-c * x^2) rather than 1 for very small x.
In case the Formula namespace is loaded, warnings are suppressed now for
processing formula specifications like cluster = ~ id in expand.model.frame().
Otherwise warnings may occur with the | separator in multi-part formulas with
factors. (Reported by David Hugh-Jones.)
The bread() method for mlm objects has been improved to also handle
weighted mlm objects. (Suggested by James Pustejovsky.)
In various vcov*() functions assuring that the variance-covariance matrix is
positive-definite (via fix = TRUE) erroneously dropped the dimnames. Now these
are properly preserved. (Reported by Joe Ritter.)
Added suppressWarnings(RNGversion("3.5.0")) in those places where set.seed()
was used to assure exactly reproducible results from R 3.6.0 onwards.
Enhanced vignette("sandwich-CL", package = "sandwich") by better describing
the background of clustered covariances and being more precise in the mathematical
notation. Documentation for the new features (see below, e.g., the formula cluster
specification and the vcovBS() methods) has been added.
In vcovCL(), vcovPL(), vcovPC(), and vcovBS(), the cluster argument (and
potentially also order.by) can be specified by a formula - provided that
expand.model.frame(x, cluster) works for the model object x.
The cluster and/or order.by are processed accordingly if observations
were dropped in the NA processing of the model object x (provided x$na.action
is available).
New dedicated vcovBS() method for lm objects that (a) provides many more
bootstrapping techniques applicable to linear models (e.g., residual-based
or wild bootstrap), (b) computes the bootstrap coefficients more efficiently
with lm.fit() or qr.coef() rather than update().
New dedicated vcovBS() method for glm objects that uses "xy" bootstrap
like the default method but uses glm.fit() instead of update() and hence
is slightly faster.
All vcovBS() methods (default, glm, and lm) facilitate parallel bootstrapping
by changing the applyfun from the default lapply(). By setting cores either
parallel::parLapply() (on Windows) or parallel::mclapply() (otherwise)
are used.
Default handling of missing parameter estimates in vcovBS() changed
from "everything" to "pairwise.complete.obs" and allow modification
of cov(..., use = ...). This is relevant if not all parameters can be
re-estimated on the bootstrap samples, e.g., for dummy variables of
relatively rare events.
Fix of a bug in vcovHC.mlm() (reported by James Pustejovsky). The off-diagonal
values of the vcovHC() were computed without preserving the sign of the
underlying residuals. This issue did not affect the diagonal because
the underlying cross product amounts to squaring all values - but it does
matter for the off-diagonal. Also, type = "const" was disabled in this
scenario and vcov(...) is simply used instead of vcovHC(..., type = "const").
Bug fix in vcovCL()/meatCL() for multi-way clustering (reported by Brian Tsay).
If patterns of levels in one clustering variable also occured in another
clustering variable, their interactions were sometimes not computed
correctly.
In vcovCL() for multi-way clustering without cluster adjustment, all
cluster adjustment factors are omitted entirely. In previous versions they
were scaled with (Gmin - 1)/Gmin, where Gmin is the minimal number of
clusters across clustering dimensions.
meatHC() and meatHAC() now pass their ... argument to estfun(), just
as meatCL(), meatPL(), and meatPC() do as well.
Various flavors of clustered sandwich estimators in vcovCL(), panel
sandwich estimators in vcovPL(), and panel-corrected estimators a la
Beck & Katz in vcovPC(). The new vignette("sandwich-CL", package = "sandwich")
introduces all functions and illustrates their use and properties.
The new function vcovBS() provides a basic (clustered) bootstrap
covariance matrix estimate, using case-based resampling.
In meatHAC(), bwAndrews(), and bwNeweyWest() it is now assured that
the estfun() is transformed to a plain matrix. Otherwise for time series
regression with irregular zoo data, the bandwidth estimation might
have failed.
In meatHC() it is now assured that the residuals are zero in observations
where all regressors and all estimating functions are zero.
Now the default methods of vcovHC() and vcovHAC() are also correctly
registered as S3 methods in the NAMESPACE.
Corrected errors in Equation 3 of vignette("sandwich", package = "sandwich").
The equation incorrectly listed the error terms "u" instead of the
observations "y" on the right-hand side (pointed out by Karl-Kuno Kunze).
sandwich(), vcovHC(), and vcovHAC() did not work when models were
fitted with na.action = na.exclude because the estfun() then (correctly)
preserved the NAs. This is now avoided and all functions handle the
na.exclude case like the na.omit case. (Thanks to John Fox for spotting
the problem and suggesting the solution.)estfun() methods for survreg and coxph objects incorrectly
returned the unweighted estimating functions in case the objects
were fitted with weights. Now the weights are properly extracted
and used.Depends/Imports: Package zoo is only in Imports now.Added estfun() and bread() methods for ordered response models
from MASS::polr() and ordinal::clm().
Added output of examples and vignettes as .Rout.save for R CMD check.
Added convenience function lrvar() to compute the long-run
variance of the mean of a time series: a simple wrapper
for kernHAC() and NeweyWest() applied to lm(x ~ 1).
lm/mlm/glm models with aliased parameters were not handled
correctly (leading to errors in sandwich()/vcovHC() etc.), fixed
now.
An improved error message is issued if prewhitening in vcovHAC()
cannot work due to collinearity in the estimating functions.
bwNeweyWest() for mlm objects that only have
an intercept.vcovHC() and related
functions.estfun() method for survreg objects.estfun() methods for hurdle/zeroinfl objects can now
handle multiple offset vectors (if any).vcovHC() method for mlm objects. This simply
combines the "meat" for each individual regression and combines
the result.bread() method for mlm objects.estfun() methods for hurdle/zeroinfl objects.Added/enhanced bread() and estfun() methods for rlm
and mlogit objects (from MASS and mlogit, respectively).
Made vcovHC() and vcovHAC() generic with previous function
definitions as default methods.
Updated vignettes (in particular using the more convenient
tobit() interface from the AER package).
Added bread() and estfun() methods for hurdle/zeroinfl
objects as computed by hurdle()/zeroinfl() in package pscl.
Fixed bread() and estfun() methods for negative binomial
glm objects: Now dispersion = 1 is used.
bread() method for lm objects now calls summary.lm()
explicitely (rather than the generic) so that it also
works with aov objects.Added new vcovOPG() function for computing the outer
product of gradients estimator (works for maximum
likelihood estfun() methods only).
Scaled estfun() and bread() method for glm objects
by dispersion estimate. Hence, this corresponds to
maximum likelihood and not deviance methods.
bwAndrews() so that it can be easily used
in models for multivariate means.A paper based on the "sandwich-OOP" vignette was accepted
for publication in volume 16(9) of Journal of Statistical Software
at https://doi.org/10.18637/jss.v016.i09.
A NAMESPACE was added for the package.
The vignette "sandwich-OOP" has been revised, extended and
released as a technical report.
Several estfun() methods and some of the meat*() functions have
been enhanced and made more consistent.
Thanks to Henric Nilsson and Giovanni Millo for feedback and testing.
estfun() methods now use directly the model.matrix() method
instead of the terms() and model.frame() methods.sandwich is made object-oriented, so that various types
of sandwich estimators can be computed not only for lm
models, but also glm, survreg, etc.
To achieve object orientation various changes have
been made: a sandwich() function is provided which needs
a bread and a meat matrix. For the bread, a generic bread()
function is provided, for the meat, there are meat(),
meatHC() and meatHAC(). All rely on the existence of a
estfun() method.
vcovHC() and vcovHAC() have been restructured to use
sandwich() together with meatHC() and meatHAC(), respectively.
A new vignette("sandwich-OOP", package = "sandwich") has been
added, explaining the new object-orientation features.
Various methods to bread() and estfun() have been added,
particularly for survreg and coxph.
Added CITATION file, see citation("sandwich").
Small documentation improvements.
vignette("sandwich", package = "sandwich").Added bandwidth selection a la Newey & West (1994) in bwNeweyWest().
NeweyWest() is a new convenience function for vcovHAC() with
bwNeweyWest().
Added estfun() methods for rlm and coxph.
Argument omega can also be a function in vcovHC().
Added data sets from Greene (1993): Investment and PublicSchools.
Improvements in vcovHC() and vcovHAC(). Argument order.by can now
be a formula and ar.method can be modified (rather than being
hard-coded "ols" which is still the default).
Thanks to Hiroyuki Kawakatsu for feedback and testing.
vcovHC(): The new default is now HC3 and support was
added for HC4.First CRAN release of the sandwich package for robust covariance
matrix estimators. Provides heteroscedasticity-consistent (HC) and
hetereoscedasticity- and autocorrelation-consistent (HAC) covariance
matrix estimators. Based on prior work by Thomas Lumley in his
weave package.
Thanks to Christian Kleiber for support, feedback, and testing.