funnel.default(), funnel.rma(), and
regplot.rma() gain slab argument
vif() was completely refactored and gains
reestimate, sim, and parallel
arguments; added as.data.frame.vif.rma() and
plot.vif.rma() methods
plot.permutest.rma.uni() function sets the y-axis
limits automatically and in a smarter way when also drawing the
reference/null distribution and the density estimate
added possibility to specify a list for btt in
anova.rma(); added print.list.anova.rma() to
print the resulting object
added as.data.frame.anova.rma() and
as.data.frame.list.anova.rma() methods
documented the possibility to use an identity link (with
link="identity") in rma.uni() when fitting
location-scale models (although this will often lead to estimation
problems); added solnp() as an additional optimizer for
this case
optimizers nloptr and constrOptim.nl
(the latter from the alabama package) are now available in
rma.uni() for location-scale models when using an identity
link
added measure SMD1H to
escalc()
for measure="SMD", escalc() now also
allows the user to specify d-values and t-test statistics via arguments
di and ti, respectively
aggregate.escalc() gains addk
argument
added (experimental!) support for measures "RR",
"RD", "PLN", and "PR" to
rma.glmm() (but using these measures will often lead to
estimation problems)
replmiss() gains data argument
cumul() functions also store data, so that arguments
ilab, col, pch, and
psize in the forest.cumul.rma() function can
look for variables therein
fixed issue with rendering Rmarkdown documents with
metafor output due to the use of a zero-width
space
added misc-models, misc-recs, and
misc-options help pages
added as.data.frame.confint.rma() and
as.data.frame.list.confint.rma methods
permutest() can now also do permutation tests for
location-scale models; it also always returns the permutation
distributions; hence, argument retpermdist was
removed
added plot.permutest.rma.uni() function to plot the
permutation distributions
simplified regtest(), ranktest(), and
tes() to single functions instead of using generics and
methods; this way, a data argument could be added
added vcalc() and blsplit()
functions
robust() gains clubSandwich argument;
if set to TRUE, the methods from the
clubSandwich package
(https://cran.r-project.org/package=clubSandwich) are used to obtain the
cluster-robust results; anova.rma() and
predict.rma() updated to work appropriately in this
case
results from robust() are no longer printed with
print.robust.rma() but with the print methods
print.rma.uni() and print.rma.mv()
anova.rma() now gives a warning when running LRTs
not based on ML/REML estimation and gains rhs argument; it
also now has a refit argument (to refit REML fits with ML
in case the fixed effects of the models differ)
setting dfs="contain" in rma.mv()
automatically sets test="t" for convenience
elements of rho and phi in
rma.mv() are now based on the lower triangular part of the
respective correlation matrix (instead of the upper triangular part) for
consistency with other functions; note that this is in principle a
backwards incompatible change, although this should only be a concern in
very special circumstances
rma.mv() gains cvvc argument (for
calculating the var-cov matrix of the variance/correlation/covariance
components)
added measure "MPORM" to escalc() for
computing marginal log odds ratios based on marginal 2x2 tables directly
(which requires specification of the correlation coefficients in the
paired tables for the calculation of the sampling variances via the
ri argument)
added measure "REH" to escalc() for
computing the (log transformed) relative excess heterozygosity (to
assess deviations from the Hardy-Weinberg equilibrium)
aggregate.escalc() gains checkpd
argument and struct="CS+CAR"
rma.glmm() now has entire array of optimizers
available for model="CM.EL" and measure="OR";
switched the default from optim() with method
BFGS to nlminb() for consistency with
rma.mv(), rma.uni(), and
selmodel.rma.uni()
rma.glmm() gains coding and
cor arguments and hence more flexibility how the group
variable should be coded in the random effects structure and whether the
random study effects should be allowed to be correlated with the random
group effects
rma.uni() now also provides R^2 for fixed-effects
models
matreg() can now also analyze a covariance matrix
with a corresponding V matrix; can also specify variable
names (instead of indices) for arguments x and
y
renamed argument nearPD to nearpd in
matreg() (but nearPD continues to
work)
plot.profile.rma() gains refline
argument
added addpoly.rma.predict() method
addpoly.default() and addpoly.rma()
gain lty and annosym arguments; if
unspecified, arguments annotate, digits,
width, transf, atransf,
targs, efac, fonts,
cex, and annosym are now automatically set
equal to the same values that were used when creating the forest
plot
documented textpos and rowadj arguments
for the various forest functions and moved the
top and annosym arguments to ‘additional
arguments’
fixed that level argument in
addpoly.rma() did not affect the CI width
points.regplot() function now also redraws the
labels (if there were any to begin with)
added lbfgsb3c, subplex, and
BBoptim as possible optimizer in rma.mv(),
rma.glmm(), rma.uni(), and
selmodel.rma.uni()
the object returned by model fitting functions now includes the
data frame specified via the data argument; various method
functions now automatically look for specified variables within this
data frame first
datasets moved to the metadat package
(https://cran.r-project.org/package=metadat)
improved the documentation a bit
the metafor package now makes use of the
mathjaxr package to nicely render equations shown in the
HTML help pages
rma() can now also fit location-scale
models
added selmodel() for fitting a wide variety of
selection models (and added the corresponding
plot.rma.uni.selmodel() function for drawing the estimated
selection function)
rma.mv() gains dfs argument and now
provides an often better way for calculating the (denominator) degrees
of freedom for approximate t- and F-tests when
dfs="contain"
added tes() function for the test of excess
significance
added regplot() function for drawing scatter plots /
bubble plots based on meta-regression models
added rcalc() for calculating the
variance-covariance matrix of correlation coefficients and
matreg() for fitting regression models based on
correlation/covariance matrices
added convenience functions dfround() and
vec2mat()
added aggregate.escalc() function to aggregate
multiple effect sizes or outcomes within studies/clusters
regtest() now shows the ‘limit estimate’ of the
(average) true effect when using sei, vi,
ninv, or sqrtninv as predictors (and the model
does not contain any other moderators)
vif() gains btt argument and can now
also compute generalized variance inflation factors; a proper
print.vif.rma() function was also added
anova.rma() argument L renamed to
X (the former still works, but is no longer
documented)
argument order in cumul() should now
just be a variable, not the order of the variable, to be used for
ordering the studies and must be of the same length as the original
dataset that was used in the model fitting
similarly, vector arguments in various plotting functions such as
forest.rma() must now be of the same length as the original
dataset that was used in the model fitting (any subsetting and removal
of NAs is automatically applied)
the various leave1out() and cumul()
functions now provide I^2 and H^2 also for
fixed-effects models; accordingly, plot.cumul.rma() now
also works with such models
fixed level not getting passed down to the various
cumul() functions
plot.cumul.rma() argument addgrid
renamed to grid (the former still works, but is no longer
documented)
forest.default(), forest.rma(), and
labbe() gain plim argument and now provide
more flexibility in terms of the scaling of the points
forest.rma() gains colout argument (to
adjust the color of the observed effect sizes or outcomes)
in the various forest() functions, the right header
is now suppressed when annotate=FALSE and
header=TRUE
funnel.default() and funnel.rma() gain
label and offset arguments
funnel.default() and funnel.rma() gain
lty argument; the reference line is now drawn by default as
a dotted line (like the line for the pseudo confidence region)
the forest and funnel arguments of
reporter.rma.uni() can now also be logicals to suppress the
drawing of these plots
added weighted argument to fsn() (for
Orwin’s method)
added some more transformation functions
bldiag() now properly handles ?x0 or 0x?
matrices
p-values are still given to 2 digits even when
digits=1
summary.escalc() also provides the p-values (of the
Wald-type tests); but when using the transf argument, the
sampling variances, standard errors, test statistics, and p-values are
no longer shown
rma.uni() no longer constrains a fixed tau^2 value
to 0 when k=1
slight speedup in functions that repeatedly fit
rma.uni() models by skipping the computation of the pseudo
R^2 statistic
started using the pbapply package for showing
progress bars, also when using parallel processing
to avoid potential confusion, all references to ‘credibility
intervals’ have been removed from the documentation; these intervals are
now exclusively referred to as ‘prediction intervals’; in the output,
the bounds are therefore indicated now as pi.lb and
pi.ub (instead of cr.lb and
cr.ub); the corresponding argument names were changed in
addpoly.default(); argument addcred was
changed to addpred in addpoly.rma() and
forest.rma(); however, code using the old arguments names
should continue to work
one can now use weights(..., type="rowsum") for
intercept-only rma.mv models (to obtain ‘row-sum
weights’)
simulate.rma() gains olim argument;
renamed the clim argument in summary.escalc()
and the various forest() functions to olim for
consistency (the old clim argument should continue to
work)
show nicer network graphs for dat.hasselblad1998 and
dat.senn2013 in the help files
added 24 datasets (dat.anand1999,
dat.assink2016, dat.baskerville2012,
dat.bornmann2007, dat.cannon2006,
dat.cohen1981, dat.craft2003,
dat.crede2010, dat.dagostino1998,
dat.damico2009, dat.dorn2007,
dat.hahn2001, dat.kalaian1996,
dat.kearon1998, dat.knapp2017,
dat.landenberger2005, dat.lau1992,
dat.lim2014, dat.lopez2019,
dat.maire2019,, dat.moura2021
dat.obrien2003, dat.vanhowe1999,
dat.viechtbauer2021)
the package now runs a version check on startup in interactive
sessions; setting the environment variable
METAFOR_VERSION_CHECK to FALSE disables
this
refactored various functions (for cleaner/simpler code)
improved the documentation a bit
version jump to 2.4-0 for CRAN release (from now on, even minor numbers for CRAN releases, odd numbers for development versions)
the various forest() functions gain
header argument
escalc() gains include
argument
setting verbose=3 in model fitting functions sets
options(warn=1)
forest.rma() and forest.default() now
throw informative errors when misusing order and
subset arguments
fixed failing tests due to the
stringsAsFactors=FALSE change in the upcoming version of
R
print.infl.rma.uni() gains infonly
argument, to only show the influential studies
removed MASS from Suggests (no longer
needed)
argument btt can now also take a string to grep
for
added optimParallel as possible optimizer in
rma.mv()
added (for now undocumented) option to fit models in
rma.glmm() via the GLMMadaptive package
(instead of lme4); to try this, use:
control=list(package="GLMMadaptive")
started to use numbering scheme for devel version (the number after the dash indicates the devel version)
added contrmat() function (for creating a matrix
that indicates which groups have been compared against each other in
each row of a dataset)
added to.wide() function (for restructuring long
format datasets into the wide format needed for contrast-based
analyses)
I^2 and H^2 are also shown in output
for fixed-effects models
argument grid in baujat() can now also
be a color name
added (for now undocumented) time argument to more
functions that are computationally expensive
added (for now undocumented) textpos argument to the
various forest functions
added a new dataset (dat.graves2010)
added more tests
added formula() method for objects of class
rma
llplot() now also allows for
measure="GEN"; also, the documentation and y-axis label
have been corrected to indicate that the function plots likelihoods (not
log likelihoods)
confint.rma.mv() now returns an object of class
list.confint.rma when obtaining CIs for all variance and
correlation components of the model; added corresponding
print.list.confint.rma() function
moved tol argument in permutest() to
control and renamed to comptol
added PMM and GENQM estimators in
rma.uni()
added vif() function to get variance inflation
factors
added .glmulti object for making the interaction
with glmulti easier
added reporter() and reporter.rma.uni()
for dynamically generating analysis reports for objects of class
rma.uni
output is now styled/colored when crayon package is
loaded (this only works on a ‘proper’ terminal with color support; also
works in RStudio)
overhauled plot.gosh.rma(); when out is
specified, it now shows two distributions, one for the values when the
outlier is included and one for the values when for outlier is excluded;
dropped the hcol argument and added border
argument
refactored influence.rma.uni() to be more consistent
internally with other functions; print.infl.rma.uni() and
plot.infl.rma.uni() adjusted accordingly; functions
cooks.distance.rma.uni(), dfbetas.rma.uni(),
and rstudent.rma.uni() now call
influence.rma.uni() for the computations
rstudent.rma.uni() now computes the SE of the
deleted residuals in such a way that it will yield identical results to
a mean shift outlier model even when that model is fitted with
test="knha"
rstandard.rma.uni() gains type
argument, and can now also compute conditional residuals (it still
computes marginal residuals by default)
cooks.distance.rma.mv() gains cluster
argument, so that the Cook’s distances can be computed for groups of
estimates
cooks.distance.rma.mv() gains parallel,
ncpus, and cl arguments and can now make use
of parallel processing
cooks.distance.rma.mv() should be faster by using
the estimates from the full model as starting values when fitting the
models with the ith study/cluster deleted from the dataset
cooks.distance.rma.mv() gains
reestimate argument; when set to FALSE,
variance/correlation components are not reestimated
rstandard.rma.mv() gains cluster
argument for computing cluster-level multivariate standardized
residuals
added rstudent.rma.mv() and
dfbetas.rma.mv()
smarter matching of elements in newmods (when using
a named vector) in predict() that also works for models
with interactions (thanks to Nicole Erler for pointing out the
problem)
rma.uni() and rma.mv() no longer issue
(obvious) warnings when user constrains vi or
V to 0 (i.e., vi=0 or V=0,
respectively)
rma.mv() does more intelligent filtering based on
NAs in V matrix
rma.mv() now ensures strict symmetry of any (var-cov
or correlation) matrices specified via the R
argument
fixed rma.mv() so checks on R argument
run as intended; also fixed an issue when multiple formulas with slashes
are specified via random (thanks to Andrew Loignon for
pointing out the problem)
suppressed showing calls on some warnings/errors in
rma.mv()
rma.mv() now allows for a continuous-time
autoregressive random effects structure (struct="CAR") and
various spatial correlation structures (struct="SPEXP",
"SPGAU", "SPLIN", "SPRAT", and
"SPSPH")
rma.mv() now allows for struct="GEN"
which models correlated random effects for any number of predictors,
including continuous ones (i.e., this allows for ‘random
slopes’)
in the various forest() functions, when
options(na.action="na.pass") or
options(na.action="na.exclude") and an annotation contains
NA, this is now shown as a blank (instead of
NA [NA, NA])
the various forest() and addpoly()
functions gain a fonts argument
the various forest() functions gain a
top argument
the various forest() functions now show correct
point sizes when the weights of the studies are exactly the
same
forest.cumul.rma() gains a col
argument
funnel.default() and funnel.rma() can
now take vectors as input for the col and bg
arguments (and also for pch); both functions also gain a
legend argument
addpoly() functions can now also show prediction
interval bounds
removed ‘formula interface’ from escalc(); until
this actually adds some kind of extra functionality, this just makes
escalc() more confusing to use
escalc() can now compute the coefficient of
variation ratio and the variability ratio for pre-post or matched
designs ("CVRC", "VRC")
escalc() does a bit more housekeeping
added (currently undocumented) arguments onlyo1,
addyi, and addvi to escalc() that
allow for more flexibility when computing certain bias corrections and
when computing sampling variances for measures that make use of the
add and to arguments
escalc() now sets add=0 for measures
where the use of such a bias correction makes little sense; this applies
to the following measures: "AS", "PHI",
"RTET", "IRSD", "PAS",
"PFT", "IRS", and "IRFT"; one can
still force the use of the bias correction by explicitly setting the
add argument to some non-zero value
added clim argument to
summary.escalc()
added ilim argument to
trimfill()
labbe() gains lty argument
labbe() now (invisibly) returns a data frame with
the coordinates of the points that were drawn (which may be useful for
manual labeling of points in the plot)
added a print method for profile.rma
objects
profile.rma.mv() now check whether any of the
profiled log-likelihood values is larger than the log-likelihood of the
fitted model (using numerical tolerance given by lltol) and
issues a warning if so
profile.rma.uni(), profile.rma.mv(),
and plot.profile.rma() gain cline argument;
plot.profile.rma() gains xlim,
ylab, and main arguments
fixed an issue with robust.rma.mv() when the model
was fitted with sparse=TRUE (thanks to Roger Martineau for
noting the problem)
various method functions (fitted(),
resid(), predict(), etc.) behave in a more
consistent manner when model omitted studies with missings
predict.rma() gains vcov argument; when
set to TRUE, the variance-covariance matrix of the
predicted values is also returned
vcov.rma() can now also return the
variance-covariance matrix of the fitted values
(type="fitted") and the residuals
(type="resid")
added $<- and as.matrix() methods
for list.rma objects
fixed error in simulate.rma() that would generate
too many samples for rma.mv models
added undocumented argument time to all model
fitting functions; if set to TRUE, the model fitting time
is printed
added more tests (also for parallel operations); also, all tests updated to use proper tolerances instead of rounding
reorganized the documentation a bit
added simulate() method for rma
objects; added MASS to Suggests (since
simulating for rma.mv objects requires
mvrnorm() from MASS)
cooks.distance.rma.mv() now works properly even when
there are missing values in the data
residuals() gains type argument and can
compute Pearson residuals
the newmods argument in predict() can
now be a named vector or a matrix/data frame with column names that get
properly matched up with the variables in the model
added ranef.rma.mv() for extracting the BLUPs of the
random effects for rma.mv models
all functions that repeatedly refit models now have the option to show a progress bar
added ranktest.default(), so user can now pass the
outcomes and corresponding sampling variances directly to the
function
added regtest.default(), so user can now pass the
outcomes and corresponding sampling variances directly to the
function
funnel.default() gains subset
argument
funnel.default() and funnel.rma() gain
col and bg arguments
plot.profile.rma() gains ylab
argument
more consistent handling of robust.rma
objects
added a print method for rma.gosh objects
the (log) relative risk is now called the (log) risk ratio in all help files, plots, code, and comments
escalc() can now compute outcome measures based on
paired binary data ("MPRR", "MPOR",
"MPRD", "MPORC", and
"MPPETO")
escalc() can now compute (semi-)partial correlation
coefficients ("PCOR", "ZPCOR",
"SPCOR")
escalc() can now compute measures of variability for
single groups ("CVLN", "SDLN") and for the
difference in variability between two groups ("CVR",
"VR"); also the log transformed mean ("MNLN")
has been added for consistency
escalc() can now compute the sampling variance for
measure="PHI" for studies using stratified sampling
(vtpye="ST")
the [ method for escalc objects now
properly handles the ni and slab attributes
and does a better job of cleaning out superfluous variable name
information
added rbind() method for escalc
objects
added as.data.frame() method for
list.rma objects
added a new dataset (dat.pagliaro1992) for another
illustration of a network meta-analysis
added a new dataset (dat.laopaiboon2015) on the
effectiveness of azithromycin for treating lower respiratory tract
infections
rma.uni() and rma.mv() now check if the
ratio of the largest to smallest sampling variance is very large;
results may not be stable then (and very large ratios typically indicate
wrongly coded data)
model fitting functions now check if extra/superfluous arguments
are specified via ... and issues are warning if so
instead of defining own generic ranef(), import
ranef() from nlme
improved output formatting
added more tests (but disabled a few tests on CRAN to avoid some
issues when R is compiled with
--disable-long-double)
some general code cleanup
renamed diagram_metafor.pdf vignette to just
diagram.pdf
minor updates in the documentation
started to use git as version control system, GitHub to host the repository (https://github.com/wviechtb/metafor) for the development version of the package, Travis CI as continuous integration service (https://travis-ci.org/wviechtb/metafor), and Codecov for automated code coverage reporting (https://app.codecov.io/gh/wviechtb/metafor)
argument knha in rma.uni() and argument
tdist in rma.glmm() and rma.mv()
are now superseded by argument test in all three functions;
for backwards compatibility, the knha and
tdist arguments still work, but are no longer
documented
rma(yi, vi, weights=1, test="knha") now yields the
same results as rma(yi, vi, weighted=FALSE, test="knha")
(but use of the Knapp and Hartung method in the context of an unweighted
analysis remains an experimental feature)
one can now pass an escalc object directly to
rma.uni(), which then tries to automatically determine the
yi and vi variables in the data frame (thanks
to Christian Roever for the suggestion)
escalc() can now also be used to convert a regular
data frame to an escalc object
for measure="UCOR", the exact bias-correction is now
used (instead of the approximation); when vtype="UB", the
exact equation is now used to compute the unbiased estimate of the
variance of the bias-corrected correlation coefficient; hence
gsl is now a suggested package (needed to compute the
hypergeometric function) and is loaded when required
cooks.distance() now also works with
rma.mv objects; and since model fitting can take some time,
an option to show a progress bar has been added
fixed an issue with robust.rma.mv() throwing errors
when the model was fitted with sparse=TRUE
fixed an error with robust.rma.mv() when the model
was fitted with user-defined weights (or a user-defined weight
matrix)
added ranef() for extracting the BLUPs of the random
effects (only for rma.uni objects at the moment)
reverted back to the pre-1.1-0 way of computing p-values for
individual coefficients in permutest.rma.uni(), that is,
the p-value is computed with
mean(abs(z_perm) >= abs(z_obs) - tol) (where
tol is a numerical tolerance)
permutest.rma.uni() gains permci
argument, which can be used to obtain permutation-based CIs of the model
coefficients (note that this is computationally very demanding and may
take a long time to complete)
rma.glmm() continues to work even when the saturated
model cannot be fitted (although the tests for heterogeneity are not
available then)
rma.glmm() now allows control over the arguments
used for method.args (via
control=list(hessianCtrl=list(...))) passed to
hessian() (from the numDeriv package) when
using model="CM.EL" and measure="OR"
in rma.glmm(), default method.args
value for r passed to hessian() has been
increased to 16 (while this slows things down a bit, this appears to
improve the accuracy of the numerical approximation to the Hessian,
especially when tau^2 is close to 0)
the various forest() and addpoly()
functions now have a new argument called width, which
provides manual control over the width of the annotation columns; this
is useful when creating complex forest plots with a monospaced font and
we want to ensure that all annotations are properly lined up at the
decimal point
the annotations created by the various forest() and
addpoly() functions are now a bit more compact by
default
more flexible efac argument in the various
forest() functions
trailing zeros in the axis labels are now dropped in forest and
funnel plots by default; but trailing zeros can be retained by
specifying a numeric (and not an integer) value for the
digits argument
added funnel.default(), which directly takes as
input a vector with the observed effect sizes or outcomes and the
corresponding sampling variances, standard errors, and/or sample
sizes
added plot.profile.rma(), a plot method for objects
returned by the profile.rma.uni() and
profile.rma.mv() functions
simplified baujat.rma.uni(),
baujat.rma.mh(), and baujat.rma.peto() to
baujat.rma(), which now handles objects of class
rma.uni, rma.mh, and
rma.peto
baujat.rma() gains argument symbol for
more control over the plotting symbol
labbe() gains a grid argument
more logical placement of labels in
qqnorm.rma.uni(), qqnorm.rma.mh(), and
qqnorm.rma.peto() functions (and more control
thereof)
qqnorm.rma.uni() gains lty
argument
added gosh.rma() and plot.gosh.rma()
for creating GOSH (i.e., graphical display of study heterogeneity) plots
based on Olkin et al. (2012)
in the (rare) case where all observed outcomes are exactly equal
to each other, test="knha" (i.e., knha=TRUE)
in rma() now leads to more appropriate results
updated datasets so those containing precomputed effect size
estimates or observed outcomes are already declared to be
escalc objects
added new datasets (dat.egger2001 and
dat.li2007) on the effectiveness of intravenous magnesium
in acute myocardial infarction
methods package is now under Depends
(in addition to Matrix), so that
rma.mv(..., sparse=TRUE) always works, even under
Rscript
some general code cleanup
added more tests (and used a more consistent naming scheme for tests)
due to more stringent package testing, it is increasingly difficult to ensure that the package passes all checks on older versions of R; from now on, the package will therefore require, and be checked under, only the current (and the development) version of R
added graphics, grDevices, and
methods to Imports (due to recent change in
how CRAN checks packages)
the struct argument for rma.mv() now
also allows for "ID" and "DIAG", which are
identical to the "CS" and "HCS" structures,
but with the correlation parameter fixed to 0
added robust() for (cluster) robust tests and
confidence intervals for rma.uni and rma.mv
models (this uses a robust sandwich-type estimator of the
variance-covariance matrix of the fixed effects along the lines of the
Eicker-Huber-White method)
confint() now works for models fitted with the
rma.mv() function; for variance and correlation parameters,
the function provides profile likelihood confidence intervals; the
output generated by the confint() function has been
adjusted in general to make the formatting more consistent across the
different model types
for objects of class rma.mv, profile()
now provides profile plots for all (non-fixed) variance and correlation
components of the model when no component is specified by the user (via
the sigma2, tau2, rho,
gamma2, or phi arguments)
for measure="MD" and measure="ROM", one
can now choose between vtype="LS" (the default) and
vtype="HO"; the former computes the sampling variances
without assuming homoscedasticity, while the latter assumes
homoscedasticity
multiple model objects can now be passed to the
fitstats(), AIC(), and BIC()
functions
check for duplicates in the slab argument is now
done after any subsetting is done (as suggested by Michael
Dewey)
rma.glmm() now again works when using
add=0, in which case some of the observed outcomes (e.g.,
log odds or log odds ratios) may be NA
when using rma.glmm() with
model="CM.EL", the saturated model (used to compute the
Wald-type and likelihood ratio tests for the presence of (residual)
heterogeneity) often fails to converge; the function now continues to
run (instead of stopping with an error) and simply omits the test
results from the output
when using rma.glmm() with
model="CM.EL" and inversion of the Hessian fails via the
Choleski factorization, the function now makes another attempt via the
QR decomposition (even when this works, a warning is issued)
for rma.glmm(), BIC and AICc values were switched
around; corrected
more use of suppressWarnings() is made when
functions repeatedly need to fit the same model, such as
cumul(), influence(), and
profile(); that way, one does not get inundated with the
same warning(s)
some (overdue) updates to the documentation
default optimizer for rma.mv() changed to
nlminb() (instead of optim() with
"Nelder-Mead"); extensive testing indicated that
nlminb() (and also optim() with
"BFGS") is typically quicker and more robust; note that
this is in principle a non-backwards compatible change, but really a
necessary one; and you can always revert to the old behavior with
control=list(optimizer="optim", optmethod="Nelder-Mead")
all tests have been updated in accordance with the recommended
syntax of the testthat package; for example,
expect_equivalent(x,y) is used instead of
test_that(x, is_equivalent_to(y))
changed a few is_identical_to() comparisons to
expect_equivalent() ones (that failed on Sparc
Solaris)
funnel() now works again for rma.glmm
objects (note to self: quit breaking things that work!)
rma.glmm() will now only issue a warning (and not an
error) when the Hessian for the saturated model cannot be inverted
(which is needed to compute the Wald-type test for heterogeneity, so the
test statistic is then simply set to NA)
rma.mv() now allows for two terms of the form
~ inner | outer; the variance components corresponding to
such a structure are called gamma2 and correlations are
called phi; other functions that work with objects of class
rma.mv have been updated accordingly
rma.mv() now provides (even) more optimizer choices:
nlm() from the stats package,
hjk() and nmk() from the dfoptim
package, and ucminf() from the ucminf package;
choose the desired optimizer via the control argument (e.g.,
control=list(optimizer="nlm"))
profile.rma.uni() and profile.rma.mv()
now can do parallel processing (which is especially relevant for
rma.mv objects, where profiling is crucial and model
fitting can be slow)
the various confint() functions now have a
transf argument (to apply some kind of transformation to
the model coefficients and confidence interval bounds); coefficients and
bounds for objects of class rma.mh and
rma.peto are no longer automatically transformed
the various forest() functions no longer enforce
that the actual x-axis limits (alim) encompass the observed
outcomes to be plotted; also, outcomes below or above the actual x-axis
limits are no longer shown
the various forest() functions now provide control
over the horizontal lines (at the top/bottom) that are automatically
added to the plot via the lty argument (this also allows
for removing them); also, the vertical reference line is now placed
behind the points/CIs
forest.default() now has argument col
which can be used to specify the color(s) to be used for drawing the
study labels, points, CIs, and annotations
the efac argument for forest.rma() now
also allows two values, the first for the arrows and CI limits, the
second for summary estimates
corrected some axis labels in various plots when
measure="PLO"
axes in labbe() plots now have
"(Group 1)" and "(Group 2)" added by
default
anova.rma() gains argument L for
specifying linear combinations of the coefficients in the model that
should be tested to be zero
in case removal of a row of data would lead to one or more
inestimable model coefficients, baujat(),
cooks.distance(), dfbetas(),
influence(), and rstudent() could fail for
rma.uni objects; such cases are now handled
properly
for models with moderators, the predict() function
now shows the study labels when they have been specified by the user
(and newmods is not used)
if there is only one fixed effect (model coefficient) in the
model, the print.infl.rma.uni() function now shows the
DFBETAS values with the other case diagnostics in a single table (for
easier inspection); if there is more than one fixed effect, a separate
table is still used for the DFBETAS values (with one column for each
coefficient)
added measure="SMCRH" to the escalc()
function for the standardized mean change using raw score
standardization with heteroscedastic population variances at the two
measurement occasions
added measure="ROMC" to the escalc()
function for the (log transformed) ratio of means (response ratio) when
the means reflect two measurement occasions (e.g., for a single group of
people) and hence are correlated
added own function for computing/estimating the tetrachoric
correlation coefficient (for measure="RTET"); package
therefore no longer suggests polycor but now suggest
mvtnorm (which is loaded as needed)
element fill returned by
trimfill.rma.uni() is now a logical vector (instead of a
0/1 dummy variable)
print.list.rma() now also returns the printed
results invisibly as a data frame
added a new dataset (dat.senn2013) as another
illustration of a network meta-analysis
metafor now depends on at least version 3.1.0 of
R
moved the stats and Matrix packages
from Depends to Imports; as a result, had to
add utils to Imports; moved the
Formula package from Depends to
Suggests
added update.rma() function (for updating/refitting
a model); model objects also now store and keep the call
the vcov() function now also extracts the marginal
variance-covariance matrix of the observed effect sizes or outcomes from
a fitted model (of class rma.uni or
rma.mv)
rma.mv() now makes use of the Cholesky decomposition
when there is a random = ~ inner | outer formula and
struct="UN"; this is numerically more stable than the old
approach that avoided non-positive definite solutions by forcing the
log-likelihood to be -Inf in those cases; the old behavior can be
restored with control = list(cholesky=FALSE)
rma.mv() now requires the inner
variable in an ~ inner | outer formula to be a factor or
character variable (except when struct is "AR"
or "HAR"); use ~ factor(inner) | outer in case
it isn’t
anova.rma.uni() function changed to
anova.rma() that works now for both rma.uni
and rma.mv objects
the profile.rma.mv() function now omits the number
of the variance or correlation component from the plot title and x-axis
label when the model only includes one of the respective
parameters
profile() functions now pass on the ...
argument also to the title() function used to create the
figure titles (esp. relevant when using the cex.main
argument)
the drop00 argument of the rma.mh() and
rma.peto() functions now also accepts a vector with two
logicals, the first applies when calculating the observed outcomes, the
second when applying the Mantel-Haenszel or Peto’s method
weights.rma.uni() now shows the correct weights when
weighted=FALSE
argument showweight renamed to
showweights in the forest.default() and
forest.rma() functions (more consistent with the naming of
the various weights() functions)
added model.matrix.rma() function (to extract the
model matrix from objects of class rma)
funnel() and radial() now (invisibly)
return data frames with the coordinates of the points that were drawn
(may be useful for manual labeling of points in the plots)
permutest.rma.uni() function now uses a numerical
tolerance when making comparisons (>= or <=) between an observed
test statistic and the test statistic under the permuted data; when
using random permutations, the function now ensures that the very first
permutation correspond to the original data
corrected some missing/redundant row/column labels in some output
most require() calls replaced with
requireNamespace() to avoid altering the search path
(hopefully this won’t break stuff …)
some non-visible changes including more use of some (non-exported) helper functions for common tasks
dataset dat.collins91985a updated (including all
reported outcomes and some more information about the various
trials)
oh, and guess what? I updated the documentation …
added method="GENQ" to rma.uni() for
the generalized Q-statistic estimator of tau^2, which allows for
used-defined weights (note: the DL and HE estimators are just special
cases of this method)
when the model was fitted with method="GENQ", then
confint() will now use the generalized Q-statistic method
to construct the corresponding confidence interval for tau^2 (thanks to
Dan Jackson for the code); the iterative method used to obtain the CI
makes use of Farebrother’s algorithm as implemented in the
CompQuadForm package
slight improvements in how the rma.uni() function
handles non-positive sampling variances
rma.uni(), rma.mv(), and
rma.glmm() now try to detect and remove any redundant
predictors before the model fitting; therefore, if there are exact
linear relationships among the predictor variables (i.e., perfect
multicollinearity), terms are removed to obtain a set of predictors that
is no longer perfectly multicollinear (a warning is issued when this
happens); note that the order of how the variables are specified in the
model formula can influence which terms are removed
the last update introduced an error in how hat values were
computed when the model was fitted with the rma() function
using the Knapp & Hartung method (i.e., when
knha=TRUE); this has been fixed
regtest() no longer works (for now) with
rma.mv objects (it wasn’t meant to in the first place); if
you want to run something along the same lines, just consider adding
some measure of the precision of the observed outcomes (e.g., their
standard errors) as a predictor to the model
added "sqrtni" and "sqrtninv" as
possible options for the predictor argument of
regtest()
more optimizers are now available for the rma.mv()
function via the nloptr package by setting
control = list(optimizer="nloptr"); when using this
optimizer, the default is to use the BOBYQA implementation from that
package with a relative convergence criterion of 1e-8 on the function
value (see documentation on how to change these defaults)
predict.rma() function now works for
rma.mv objects with multiple tau^2 values even if the user
specifies the newmods argument but not the
tau2.levels argument (but a warning is issued and the
prediction intervals are not computed)
argument var.names now works properly in
escalc() when the user has not made use of the
data argument (thanks to Jarrett Byrnes for bringing this
to my attention)
added plot() function for cumulative random-effects
models results as obtained with the cumul.rma.uni()
function; the plot shows the model estimate on the x-axis and the
corresponding tau^2 estimate on the y-axis in the cumulative order of
the results
fixed the omitted offset term in the underlying model fitted by
the rma.glmm() function when method="ML",
measure="IRR", and model="UM.FS", that is,
when fitting a mixed-effects Poisson regression model with fixed study
effects to two-group event count data (thanks to Peter Konings for
pointing out this error)
added two new datasets (dat.bourassa1996,
dat.riley2003)
added function replmiss() (just a useful helper
function)
package now uses LazyData: TRUE
some improvements to the documentation (do I still need to mention this every time?)
some minor tweaks to rma.uni() that should be user
transparent
rma.uni() now has a weights argument,
allowing the user to specify arbitrary user-defined weights; all
functions affected by this have been updated accordingly
better handling of mismatched length of yi and
ni vectors in rma.uni() and
rma.mv() functions
subsetting is now handled as early as possible within functions with subsetting capabilities; this avoids some (rare) cases where studies ultimately excluded by the subsetting could still affect the results
some general tweaks to rma.mv() that should make it
a bit faster
argument V of rma.mv() now also accepts
a list of var-cov matrices for the observed effects or outcomes; from
the list elements, the full (block diagonal) var-cov matrix
V is then automatically constructed
rma.mv() now has a new argument W
allowing the user to specify arbitrary user-defined weights or an
arbitrary weight matrix
rma.mv() now has a new argument sparse;
by setting this to TRUE, the function uses sparse matrix
objects to the extent possible; this can speed up model fitting
substantially for certain models (hence, the metafor
package now depends on the Matrix package)
rma.mv() now allows for struct="AR" and
struct="HAR", to fit models with (heteroscedastic)
autoregressive (AR1) structures among the true effects (useful for
meta-analyses of studies reporting outcomes at multiple time
points)
rma.mv() now has a new argument Rscale
which can be used to control how matrices specified via the
R argument are scaled (see docs for more details)
rma.mv() now only checks for missing values in the
rows of the lower triangular part of the V matrix
(including the diagonal); this way, if
Vi = matrix(c(.5,NA,NA,NA), nrow=2, ncol=2) is the var-cov
matrix of the sampling errors for a particular study with two outcomes,
then only the second row/column needs to be removed before the model
fitting (and not the entire study)
added five new datasets (dat.begg1989,
dat.ishak2007, dat.fine1993,
dat.konstantopoulos2011, and
dat.hasselblad1998) to provide further illustrations of the
use of the rma.mv() function (for meta-analyses combining
controlled and uncontrolled studies, for meta-analyses of longitudinal
studies, for multilevel meta-analyses, and for network meta-analyses /
mixed treatment comparison meta-analyses)
added rstandard.rma.mv() function to compute
standardized residuals for models fitted with the rma.mv()
function (rstudent.rma.mv() to be added at a later point);
also added hatvalues.rma.mv() for computing the hat values
and weights.rma.uni() for computing the weights (i.e., the
diagonal elements of the weight matrix)
the various weights() functions now have a new
argument type to indicate whether only the diagonal
elements of the weight matrix (default) or the entire weight matrix
should be returned
the various hatvalues() functions now have a new
argument type to indicate whether only the diagonal
elements of the hat matrix (default) or the entire hat matrix should be
returned
predict.rma() function now works properly for
rma.mv objects (also has a new argument
tau2.levels to specify, where applicable, the levels of the
inner factor when computing prediction intervals)
forest.rma() function now provides a bit more
control over the color of the summary polygon and is now compatible with
rma.mv objects; also, has a new argument lty,
which provides more control over the line type for the individual CIs
and the prediction interval
addpoly.default() and addpoly.rma() now
have a border argument (for consistency with the
forest.rma() function); addpoly.rma() now
yields the correct CI bounds when the model was fitted with
knha=TRUE
forest.cumul.rma() now provides the correct CI
bounds when the models were fitted with the Knapp & Hartung method
(i.e., when knha=TRUE in the original rma()
function call)
the various forest() functions now return
information about the chosen values for arguments xlim,
alim, at, ylim,
rows, cex, cex.lab, and
cex.axis invisibly (useful for tweaking the default
values); thanks to Michael Dewey for the suggestion
the various forest() functions now have a new
argument, clim, to set limits for the confidence/prediction
interval bounds
cumul.mh() and cumul.peto() now get the
order of the studies right when there are missing values in the
data
the transf argument of
leave1out.rma.mh(), leave1out.rma.peto(),
cumul.rma.mh(), and cumul.rma.peto() should
now be used to specify the actual function for the transformation (the
former behavior of setting this argument to TRUE to
exponentiate log RRs, log ORs, or log IRRs still works for
back-compatibility); this is more consistent with how the
cumul.rma.uni() and leave1out.rma.uni()
functions work and is also more flexible
added bldiag() function to construct a block
diagonal matrix from (a list of) matrices (may be needed to construct
the V matrix when using the rma.mv()
function); bdiag() function from the Matrix
package does the same thing, but creates sparse matrix objects
profile.rma.mv() now has a startmethod
argument; by setting this to "prev", successive model fits
are started at the parameter estimates from the previous model fit; this
may speed things up a bit; also, the method for automatically choosing
the xlim values has been changed
slight improvement to profile.rma.mv() function,
which would throw an error if the last model fit did not
converge
added a new dataset (dat.linde2005) for replication
of the analyses in Viechtbauer (2007)
added a new dataset (dat.molloy2014) for
illustrating the meta-analysis of (r-to-z transformed) correlation
coefficients
added a new dataset (dat.gibson2002) to illustrate
the combined analysis of standardized mean differences and probit
transformed risk differences
computations in weights.mh() slightly changed to
prevent integer overflows for large counts
unnecessary warnings in transf.ipft.hm() are now
suppressed (cases that raised those warnings were already handled
correctly)
in predict(), blup(),
cumul(), and leave1out(), when using the
transf argument, the standard errors (which are
NA) are no longer shown in the output
argument slab in various functions will now also
accept non-unique study labels; make.unique() is used as
needed to make them unique
vignettes("metafor") and
vignettes("metafor_diagram") work again (yes, I know they
are not true vignettes in the strict sense, but I think they should show
up on the CRAN website for the package and using a minimal valid Sweave
document that is recognized by the R build system makes that
happen)
escalc() and its summary() method now
keep better track when the data frame contains multiple columns with
outcome or effect size values (and corresponding sampling variances) for
print formatting; also simplified the class structure a bit (and hence,
print.summary.escalc() removed)
summary.escalc() has a new argument H0
to specify the value of the outcome under the null hypothesis for
computing the test statistics
added measures "OR2DN" and "D2ORN" to
escalc() for transforming log odds ratios to standardized
mean differences and vice-versa, based on the method of Cox & Snell
(1989), which assumes normally distributed response variables within the
two groups before the dichotomization
permutest.rma.uni() function now catches an error
when the number of permutations requested is too large (for R to even
create the objects to store the results in) and produces a proper error
message
funnel.rma() function now allows the
yaxis argument to be set to "wi" so that the
actual weights (in %) are placed on the y-axis (useful when arbitrary
user-defined have been specified)
for rma.glmm(), the control argument
optCtrl is now used for passing control arguments to all of
the optimizers (hence, control arguments nlminbCtrl and
minqaCtrl are now defunct)
rma.glmm() should not throw an error anymore when
including only a single moderator/predictor in the model
predict.rma() now returns an object of class
list.rma (therefore, function
print.predict.rma() has been removed)
for rma.list objects, added [,
head(), and tail() methods
automated testing using the testthat package (still
many more tests to add, but finally made a start on this)
encoding changed to UTF-8 (to use ‘foreign characters’ in the docs and to make the HTML help files look a bit nicer)
guess what? some improvements to the documentation! (also combined some of the help files to reduce the size of the manual a bit; and yes, it’s still way too big)
added function rma.mv() to fit
multivariate/multilevel meta-analytic models via appropriate linear
(mixed-effects) models; this function allows for modeling of
non-independent sampling errors and/or true effects and can be used for
network meta-analyses, meta-analyses accounting for phylogenetic
relatedness, and other complicated meta-analytic data
structures
added the AICc to the information criteria computed by the various model fitting functions
if the value of tau^2 is fixed by the user via the corresponding
argument in rma.uni(), then tau^2 is no longer counted as
an additional parameter for the computation of the information criteria
(i.e., AIC, BIC, and AICc)
rma.uni(), rma.glmm(), and
rma.mv() now use a more stringent check whether the model
matrix is of full rank
added profile() method functions for objects of
class rma.uni and rma.mv (can be used to
obtain a plot of the profiled log-likelihood as a function of a specific
variance component or correlation parameter of the model)
predict.rma() function now has an
intercept argument that allows the user to decide whether
the intercept term should be included when calculating the predicted
values (rare that this should be changed from the default)
for rma.uni(), rma.glmm(), and
rma.mv(), the control argument can now also
accept an integer value; values > 1 generate more verbose output
about the progress inside of the function
rma.glmm() has been updated to work with
lme4 1.0.x for fitting various models; as a result,
model="UM.RS" can only use nAGQ=1 at the
moment (hopefully this will change in the future)
the control argument of rma.glmm() can
now be used to pass all desired control arguments to the various
functions and optimizers used for the model fitting (admittedly the use
of lists within this argument is a bit unwieldy, but much more
flexible)
rma.mh() and rma.peto() also now have a
verbose argument (not really needed, but added for sake of
consistency across functions)
fixed (silly) error that would prevent rma.glmm()
from running for measures "IRR", "PLO", and
"IRLN" when there are missing values in the data (lesson:
add some missing values to datasets for the unit tests!)
a bit of code reorganization (should be user transparent)
vignettes ("metafor" and
"metafor_diagram") are now just ‘other files’ in the doc
directory (as these were not true vignettes to begin with)
some improvements to the documentation (as always)
rma.mh() now also implements the Mantel-Haenszel
method for incidence rate differences
(measure="IRD")
when analyzing incidence rate ratios (measure="IRR")
with the rma.mh() function, the Mantel-Haenszel test for
person-time data is now also provided
rma.mh() has a new argument correct
(default is TRUE) to indicate whether the continuity
correction should be applied when computing the
(Cochran-)Mantel-Haenszel test statistic
renamed elements CMH and CMHp (for the
Cochran-Mantel-Haenszel test statistic and corresponding p-value) to
MH and MHp
added function baujat() to create Baujat
plots
added a new dataset (dat.pignon2000) to illustrate
the use of the baujat() function
added function to.table() to convert data from
vector format into the corresponding table format
added function to.long() to convert data from vector
format into the corresponding long format
rma.glmm() now even runs when k=1 (yielding trivial
results)
for models with an intercept and moderators,
rma.glmm() now internally rescales (non-dummy) variables to
z-scores during the model fitting (this improves the stability of the
model fitting, especially when model="CM.EL"); results are
given after back-scaling, so this should be transparent to the
user
in rma.glmm(), default number of quadrature points
(nAGQ) is now 7 (setting this to 100 was a bit
overkill)
a few more error checks here and there for misspecified arguments
some improvements to the documentation
vignette renamed to metafor so
vignette("metafor") works now
added a diagram to the documentation, showing the various
functions in the metafor package (and how they relate to
each other); can be loaded with
vignette("metafor_diagram")
anova.rma.uni() function can now also be used to
test (sub)sets of model coefficients with a Wald-type test when a single
model is passed to the function
the pseudo R^2 statistic is now automatically calculated by the
rma.uni() function and supplied in the output (only for
mixed-effects models and when the model includes an intercept, so that
the random- effects model is clearly nested within the mixed-effects
model)
component VAF is now called R2 in
anova.rma.uni() function
added function hc() that carries out a
random-effects model analysis using the method by Henmi and Copas
(2010); thanks to Michael Dewey for the suggestion and providing the
code
added new dataset (dat.lee2004), which was used in
the article by Henmi and Copas (2010) to illustrate their
method
fixed missing x-axis labels in the forest()
functions
rma.glmm() now computes Hessian matrices via the
numDeriv package when model="CM.EL" and
measure="OR" (i.e., for the conditional logistic model with
exact likelihood); so numDeriv is now a suggested package
and is loaded within rma.glmm() when required
trimfill.rma.uni() now also implements the
"Q0" estimator (although the "L0" and
"R0" estimators are generally to be preferred)
trimfill.rma.uni() now also calculates the SE of the
estimated number of missing studies and, for estimator
"R0", provides a formal test of the null hypothesis that
the number of missing studies on a given side is zero
added new dataset (dat.bangertdrowns2004)
the level argument in various functions now either
accepts a value representing a percentage or a proportion (values
greater than 1 are assumed to be a percentage)
summary.escalc() now computes confidence intervals
correctly when using the transf argument
computation of Cochran-Mantel-Haenszel statistic in
rma.mh() changed slightly to avoid integer overflow with
very big counts
some internal improvements with respect to object attributes that were getting discarded when subsetting
some general code cleanup
some improvements to the documentation
added additional clarifications about the change score outcome
measures ("MC", "SMCC", and
"SMCR") to the help file for the escalc()
function and changed the code so that "SMCR" no longer
expects argument sd2i to be specified (which is not needed
anyways) (thanks to Markus Kösters for bringing this to my
attention)
sampling variance for the biserial correlation coefficient
("RBIS") is now calculated in a slightly more accurate
way
llplot() now properly scales the
log-likelihoods
argument which in the
plot.infl.rma.uni() function has been replaced with
argument plotinf which can now also be set to
FALSE to suppress plotting of the various case diagnostics
altogether
labeling of the axes in labbe() plots is now correct
for odds ratios (and transformations thereof)
added two new datasets (dat.nielweise2007 and
dat.nielweise2008) to illustrate some methods/models from
the rma.glmm() function
added a new dataset (dat.yusuf1985) to illustrate
the use of rma.peto()
test for heterogeneity is now conducted by the
rma.peto() function exactly as described by Yusuf et
al. (1985)
in rma.glmm(), default number of quadrature points
(nAGQ) is now 100 (which is quite a bit slower, but should
provide more than sufficient accuracy in most cases)
the standard errors of the HS and DL estimators of tau^2 are now
correctly computed when tau^2 is prespecified by the user in the
rma() function; in addition, the standard error of the SJ
estimator is also now provided when tau^2 is prespecified
rma.uni() and rma.glmm() now use a
better method to check whether the model matrix is of full rank
I^2 and H^2 statistics are now also calculated for mixed-effects
models by the rma.uni() and rma.glmm()
function; confint.rma.uni() provides the corresponding
confidence intervals for rma.uni models
various print() methods now have a new argument
called signif.stars, which defaults to
getOption("show.signif.stars") (which by default is
TRUE) to determine whether the infamous ‘significance
stars’ should be printed
slight changes in wording in the output produced by the
print.rma.uni() and print.rma.glmm()
functions
some improvements to the documentation
added rma.glmm() function for fitting of appropriate
generalized linear (mixed-effects) models when analyzing odds ratios,
incidence rate ratios, proportions, or rates; the function makes use of
the lme4 and BiasedUrn packages; these are now
suggested packages and loaded within rma.glmm() only when
required (this makes for faster loading of the metafor
package)
added several method functions for objects of class
rma.glmm (not all methods yet implemented; to be completed
in the future)
rma.uni() now allows the user to specify a formula
for the yi argument, so instead of rma(yi, vi,
mods=~mod1+mod2), one can specify the same model with rma(yi~mod1+mod2,
vi)
rma.uni() now has a weights argument to
specify the inverse of the sampling variances (instead of using the
vi or sei arguments); for now, this is all
this argument should be used for (in the future, this argument may
potentially be used to allow the user to define alternative
weights)
rma.uni() now checks whether the model matrix is not
of full rank and issues an error accordingly (instead of the rather
cryptic error that was issued before)
rma.uni() now has a verbose
argument
coef.rma() now returns only the model coefficients
(this change was necessary to make the package compatible with the
multcomp package; see help(rma) for an
example); use coef(summary()) to obtain the full table of
results
the escalc() function now does some more extensive
error checking for misspecified data and some unusual cases
append argument is now TRUE by default
in the escalc() function
objects generated by the escalc() function now have
their own class
added print() and summary() methods for
objects of class escalc
added [ and cbind() methods for objects
of class escalc
added a few additional arguments to the escalc()
function (i.e., slab, subset,
var.names, replace,
digits)
added drop00 argument to the escalc(),
rma.uni(), rma.mh(), and
rma.peto() functions
added "MN", "MC", "SMCC",
and "SMCR" measures to the escalc() and
rma.uni() functions for the raw mean, the raw mean change,
and the standardized mean change (with change score or raw score
standardization) as possible outcome measures
the "IRFT" measure in the escalc() and
rma.uni() functions is now computed with
1/2*(sqrt(xi/ti) + sqrt(xi/ti+1/ti)) which is more
consistent with the definition of the Freeman-Tukey transformation for
proportions
added "RTET" measure to the escalc()
and rma.uni() functions to compute the tetrachoric
correlation coefficient based on 2x2 table data (the
polycor package is therefore now a suggested package, which
is loaded within escalc() only when required)
added "RPB" and "RBIS" measures to the
escalc() and rma.uni() functions to compute
the point-biserial and biserial correlation coefficient based on means
and standard deviations
added "PBIT" and "OR2D" measures to the
escalc() and rma.uni() functions to compute
the standardized mean difference based on 2x2 table data
added the "D2OR" measure to the
escalc() and rma.uni() functions to compute
the log odds ratio based on the standardized mean difference
added "SMDH" measure to the escalc()
and rma.uni() functions to compute the standardized mean
difference without assuming equal population variances
added "ARAW", "AHW", and
"ABT" measures to the escalc() and
rma.uni() functions for the raw value of Cronbach’s alpha,
the transformation suggested by Hakstian & Whalen (1976), and the
transformation suggested by Bonett (2002) for the meta-analysis of
reliability coefficients (see help(escalc) for
details)
corrected a small mistake in the equation used to compute the
sampling variance of the phi coefficient (measure="PHI") in
the escalc() function
the permutest.rma.uni() function now uses an
algorithm to find only the unique permutations of the model matrix
(which may be much smaller than the total number of permutations),
making the exact permutation test feasible in a larger set of
circumstances (thanks to John Hodgson for making me aware of this issue
and to Hans-Jörg Viechtbauer for coming up with a recursive algorithm
for finding the unique permutations)
prediction interval in forest.rma() is now indicated
with a dotted (instead of a dashed) line; ends of the interval are now
marked with vertical bars
completely rewrote the funnel.rma() function which
now supports many more options for the values to put on the y-axis;
trimfill.rma.uni() function was adapted
accordingly
removed the ni argument from the
regtest.rma() function; instead, sample sizes can now be
explicitly specified via the ni argument when using the
rma.uni() function (i.e., when measure="GEN");
the escalc() function also now adds information on the
ni values to the resulting data frame (as an attribute of
the yi variable), so, if possible, this information is
passed on to regtest.rma()
added switch so that regtest() can also provide the
full results from the fitted model (thanks to Michael Dewey for the
suggestion)
weights.rma.mh() now shows the weights in % as
intended (thanks to Gavin Stewart for pointing out this error)
more flexible handling of the digits argument in the
various forest functions
forest functions now use pretty() by default to set
the x-axis tick locations (alim and at
arguments can still be used for complete control)
studies that are considered to be ‘influential’ are now marked
with an asterisk when printing the results returned by the
influence.rma.uni() function (see the documentation of this
function for details on how such studies are identified)
added additional extractor functions for some of the influence
measures (i.e., cooks.distance(), dfbetas());
unfortunately, the covratio() and dffits()
functions in the stats package are not generic; so, to
avoid masking, there are currently no extractor functions for these
measures
better handling of missing values in some unusual situations
corrected small bug in fsn() that would not allow
the user to specify the standard errors instead of the sampling
variances (thanks to Bernd Weiss for pointing this out)
plot.infl.rma.uni() function now allows the user to
specify which plots to draw (and the layout) and adds the option to show
study labels on the x-axis
added proper print() method for objects generated by
the confint.rma.uni(), confint.rma.mh(), and
confint.rma.peto() functions
when transf or atransf argument was a
monotonically decreasing function, then confidence and
prediction interval bounds were in reversed order; various functions now
check for this and order the bounds correctly
trimfill.rma.uni() now only prints information about
the number of imputed studies when actually printing the model
object
qqnorm.rma.uni(), qqnorm.rma.mh(), and
qqnorm.rma.peto() functions now have a new argument called
label, which allows for labeling of points; the functions
also now return (invisibly) the x and y coordinates of the points
drawn
rma.mh() with measure="RD" now computes
the standard error of the estimated risk difference based on Sato,
Greenland, & Robins (1989), which provides a consistent estimate
under both large-stratum and sparse-data limiting models
the restricted maximum likelihood (REML) is now calculated using the full likelihood equation (without leaving out additive constants)
the model deviance is now calculated as -2 times the difference between the model log-likelihood and the log-likelihood under the saturated model (this is a more appropriate definition of the deviance than just taking -2 times the model log-likelihood)
naming scheme of illustrative datasets bundled with the package
has been changed; now datasets are called
<dat.authoryear>; therefore, the datasets are now
called (old name -> new name):
dat.bcg -> dat.colditz1994dat.warfarin -> dat.hart1999dat.los -> dat.normand1999dat.co2 -> dat.curtis1998dat.empint -> dat.mcdaniel1994but dat.bcg has been kept as an alias for
dat.colditz1994, as it has been referenced under that name
in some publications
added new dataset (dat.pritz1997) to illustrate the
meta-analysis of proportions (raw values and transformations
thereof)
added new dataset (dat.bonett2010) to illustrate the
meta-analysis of Cronbach’s alpha values (raw values and transformations
thereof)
added new datasets (dat.hackshaw1998,
dat.raudenbush1985)
(approximate) standard error of the tau^2 estimate is now computed and shown for most of the (residual) heterogeneity estimators
added nobs() and df.residual() methods
for objects of class rma
metafor.news() is now simply a wrapper for
news(package="metafor")
the package code is now byte-compiled, which yields some modest increases in execution speed
some general code cleanup
the metafor package no longer depends on the
nlme package
some improvements to the documentation
trimfill.rma.uni() now returns a proper object even
when the number of missing studies is estimated to be zero
added the (log transformed) ratio of means as a possible outcome
measure to the escalc() and rma.uni()
functions (measure="ROM")
added new dataset (dat.co2) to illustrate the use of
the ratio of means outcome measure
some additional error checking in the various forest functions
(especially when using the ilab argument)
in labbe.rma(), the solid and dashed lines are now
drawn behind (and not on top of) the points
slight change to transf.ipft.hm() so that missing
values in targs$ni are ignored
some improvements to the documentation
the metafor package now has its own project website
at: https://www.metafor-project.org/
added labbe() function to create L’Abbe
plots
the forest.default() and
addpoly.default() functions now allow the user to directly
specify the lower and upper confidence interval bounds (this can be
useful when the CI bounds have been calculated with other
methods/functions)
added the incidence rate for a single group and for two groups
(and transformations thereof) as possible outcome measures to the
escalc() and rma.uni() functions
(measure="IRR", "IRD", "IRSD",
"IR", "IRLN", "IRS", and
"IRFT")
added the incidence rate ratio as a possible outcome measure to
the rma.mh() function
added transformation functions related to incidence rates
added the Freeman-Tukey double arcsine transformation and its inverse to the transformation functions
added some additional error checking for out-of-range p-values in
the permutest.rma.uni() function
added some additional checking for out-of-range values in several transformation functions
added confint() methods for rma.mh and
rma.peto objects (only for completeness sake; print already
provides CIs)
added new datasets (dat.warfarin,
dat.los, dat.empint)
some improvements to the documentation
a paper about the package has now been published in the Journal of Statistical Software (https://www.jstatsoft.org/v36/i03/)
added citation info; see:
citation("metafor")
the metafor package now depends on the
nlme package
added extractor functions for the AIC, BIC, and deviance
some updates to the documentation
the metafor package now depends on the
Formula package
made escalc() generic and implemented a default and
a formula interface
added the (inverse) arcsine transformation to the set of transformation functions
cases where k is very small (e.g., k equal to 1 or 2) are now handled more gracefully
added sanity check for cases where all observed outcomes are equal to each other (this led to division by zero when using the Knapp & Hartung method)
the “smarter way to set the number of iterations for permutation tests” (see notes for previous version below) now actually works like it is supposed to
the permutest.rma.uni() function now provides more
sensible results when k is very small; the documentation for the
function has also been updated with some notes about the use of
permutation tests under those circumstances
made some general improvements to the various forest plot
functions making them more flexible in particular when creating more
complex displays; most importantly, added a rows argument
and removed the addrows argument
some additional examples have been added to the help files for the forest and addpoly functions to demonstrate how to create more complex displays with these functions
added showweight argument to the
forest.default() and forest.rma()
functions
cumul() functions not showing all of the output
columns when using fixed-effects models has been corrected
weights.rma.uni() function now handles
NAs appropriately
weights.rma.mh() and weights.rma.peto()
functions added
logLik.rma() function now behaves more like other
logLik() functions (such as logLik.lm() and
logLik.lme())
cint() generic removed and replaced with
confint() method for objects of class
rma.uni
slightly improved the code to set the x-axis title in the
forest() and funnel() functions
added coef() method for
permutest.rma.uni objects
added append argument to escalc()
function
implemented a smarter way to set the number of iterations for
permutation tests (i.e., the permutest.rma.uni() function
will now switch to an exact test if the number of iterations required
for an exact test is actually smaller than the requested number of
iterations for an approximate test)
changed the way how p-values for individual coefficients are
calculated in permutest.rma.uni() to ‘two times the
one-tailed area under the permutation distribution’ (more consistent
with the way we typically define two-tailed p-values)
added retpermdist argument to
permutest.rma.uni() to return the permutation distributions
of the test statistics
slight improvements to the various transformation functions to cope better with some extreme cases
p-values are now calculated in such a way that very small p-values stored in fitted model objects are no longer truncated to 0 (the printed results are still truncated depending on the number of digits specified)
changed the default number of iterations for the ML, REML, and EB estimators from 50 to 100
version jump in conjunction with the upcoming publication of a
paper in the Journal of Statistical Software describing the
metafor package
instead of specifying a model matrix, the user can now specify a
model formula for the mods argument in the
rma() function (e.g., like in the lm()
function)
permutest() function now allows exact permutation
tests (but this is only feasible when k is not too large)
forest() function now uses the level
argument properly to adjust the CI level of the summary estimate for
models without moderators (i.e., for fixed- and random-effets
models)
forest() function can now also show the prediction
interval as a dashed line for a random-effects model
information about the measure used is now passed on to the
forest() and funnel() functions, which try to
set an appropriate x-axis title accordingly
funnel() function now has more arguments (e.g.,
atransf, at) providing more control over the
display of the x-axis
predict() function now has its own
print() method and has a new argument called
addx, which adds the values of the moderator variables to
the returned object (when addx=TRUE)
functions now properly handle the na.action
"na.pass" (treated essentially like
"na.exclude")
added method for weights() to extract the weights
used when fitting models with rma.uni()
some small improvements to the documentation
added permutest() function for permutation
tests
added metafor.news() function to display the
NEWS file of the metafor package within R
(based on same idea in the animate package by Yihui
Xie)
added some checks for values below machine precision
a bit of code reorganization (nothing that affects how the functions work)
small changes to the computation of the DFFITS and DFBETAS values
in the influence() function, so that these statistics are
more in line with their definitions in regular linear regression
models
added option to the plot function for objects returned by
influence() to allow plotting the covariance ratios on a
log scale (now the default)
slight adjustments to various print() functions (to
catch some errors when certain values were NA)
added a control option to rma() to adjust the step
length of the Fisher scoring algorithm by a constant factor (this may be
useful when the algorithm does not converge)
added the phi coefficient (measure="PHI"), Yule’s Q
("YUQ"), and Yule’s Y ("YUY") as additional
measures to the escalc() function for 2x2 table
data
forest plots now order the studies so that the first study is at
the top of the plot and the last study at the bottom (the order can
still be set with the order or subset
argument)
added cumul() function for cumulative meta-analyses
(with a corresponding forest() method to plot the
cumulative results)
added leave1out() function for leave-one-out
diagnostics
added option to qqnorm.rma.uni() so that the user
can choose whether to apply the Bonferroni correction to the bounds of
the pseudo confidence envelope
some internal changes to the class and methods names
some small corrections to the documentation
corrected the trimfill() function
improvements to various print functions
added a regtest() function for various regression
tests of funnel plot asymmetry (e.g., Egger’s regression test)
made ranktest() generic and added a method for
objects of class rma so that the test can be carried out
after fitting
added anova() function for full vs reduced model
comparisons via fit statistics and likelihood ratio tests
added the Orwin and Rosenberg approaches to
fsn()
added H^2 measure to the output for random-effects models
in escalc(), measure="COR" is now used
for the (usual) raw correlation coefficient and
measure="UCOR" for the bias corrected correlation
coefficients
some small corrections to the documentation
small changes to some of the examples
added the log transformed proportion (measure="PLN")
as another measure to the escalc() function; changed
"PL" to "PLO" for the logit (i.e., log odds)
transformation for proportions
added an option in plot.infl.rma.uni() to open a new
device for plotting the DFBETAS values
thanks to Jim Lemon, added a much better method for adjusting the
size of the labels, annotations, and symbols in the
forest() function when the number of studies is
large