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.
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 now samples the dependent variable and applies the QR decomposition jointly only once (qrjoint = TRUE
) rather than sampling coefficients by applying the QR decomposition separately in each iteration (qrjoint = FALSE
). The joint QR needs somewhat more memory but is somewhat faster, especially if the number of coefficients is large (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).
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 NA
s. 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.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.bread()
method for lm
objects now calls summary.lm()
explicitely (rather than the generic) so that it also works with aov
objects.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
.
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.