the steps
argument in the various profile()
functions can now also be a numeric vector to specify for which parameter values the likelihood should be evaluated
a few minor fixes to the dynamic theming of plots based on the foreground and background colors of the plotting device
slightly improved flexibility for setting package options
new measures added to escalc()
: "SMN"
for the single-group standardized mean / single-group standardized mean difference, "SMCRP"
for the standardized mean change using raw score standardization with pooled standard deviations, and "SMCRPH"
for the standardized mean change using raw score standardization with pooled standard deviations and heteroscedastic population variances at the two measurement occasions
calculation of the sampling variances for measures "SMDH"
, "SMD1H"
, and "SMCRH"
was slightly adjusted for consistency
in plot.gosh.rma()
, can also set het="tau"
(to plot the square root of tau^2 as the measure of heterogeneity)
in the various forest()
functions, argument ylim
can now only be a single value to specify the lower bound (while the upper bound is still set automatically)
in forest()
and regplot()
, observation limits set via olim
are now properly applied to all elements
various internal improvements to selmodel()
selmodel()
no longer stops with an error when one or more intervals defined by the steps
argument do not contain any observed p-values (instead a warning is issued and model fitting proceeds, but may fail)
added decreasing
argument to selmodel()
for enforcing that the delta estimates must be a monotonically decreasing function of the p-values in the step function model
added the undocumented argument pval
to selmodel()
for passing p-values directly to the function (doing this is highly experimental)
some internal refactoring of the code
improved the documentation a bit
added getmfopt()
and setmfopt()
functions for getting and setting package options and made some of the options more flexible
removed argument weighted
from fsn()
(whether weighted or unweighted averages are used in Orwin’s method is now simply determined by whether sampling variances are specified or not); added type="General"
to fsn()
as a generalization of the Orwin and Rosenberg methods (that allows for a fail-safe N calculation based on a random-effects model); can now pass an rma
object to the fsn()
function
further improved the theming of all plots based on the foreground and background colors; within RStudio, plot colors can also be automatically chosen based on the theme (with setmfopt(theme="auto")
)
added additional/optional argument tabfig
to the various forest()
functions, for easily setting the annosym
argument to an appropriate vector for exactly aligning numbers (when using a matching font)
added (for now undocumented) vccon
argument to rma.mv()
for setting equality constraints on variance/correlation components
replace
argument in conv.2x2()
, conv.delta()
, conv.fivenum()
, and conv.wald()
can now also be a logical
added summary.matreg()
and print.summary.matreg()
methods for including additional statistics in the output (R^2 and the omnibus test) and added coef.matreg()
and vcov.matreg()
extractor functions
formatting functions fmtp()
, fmtx()
, and fmtt()
gain a quote
argument, which is set to FALSE
by default
for measures "PCOR"
, "ZPCOR"
, "SPCOR"
, and "ZSPCOR"
, argument mi
in escalc()
now refers to the total number of predictors in the regression models (i.e., also counting the focal predictor of interest)
added measures "R2"
and "ZR2"
to escalc()
addpoly.default()
and addpoly.rma.predict()
gain a constarea
argument (for the option to draw the polygons with a constant area)
plot.rma.uni.selmodel()
gains a shade
argument (for shading the confidence interval region)
plot.permutest.rma.uni()
gains a legend
argument
vcalc()
gains a sparse
argument
aggregate.escalc
gains var.names
argument
made the legend
argument more flexible in funnel()
made the append
argument more flexible in to.long()
added a few more transformation functions
small bug fixes
added automated visual comparison tests of plots
improved the documentation a bit
improved the various plotting functions so they respect par("fg")
; as a result, one can now create plots with a dark background and light plotting colors
also allow two or three values for xlab
in the various forest()
functions (for adding labels at the ends of the x-axis limits)
better default choices for xlim
in the various forest()
functions; also, argument ilab.xpos
is now optional when using the ilab
argument
added shade
and colshade
arguments to the various forest()
functions
the various forest()
functions no longer enforce that xlim
must be at least as wide as alim
added link
argument to rma.glmm()
rma.glmm()
with measure="OR", model="CM.EL", method="ML"
now treats tau^2 values below 1e-04 effectively as zero before computing the standard errors of the fixed effects; this helps to avoid numerical problems in approximating the Hessian; similarly, selmodel()
now treats tau^2 values below 1e-04 or min(vi/10) effectively as zero before computing the standard errors
for measure SMCC
, can now specify d-values, t-test statistics, and p-values via arguments di
, ti
, and pi
functions that issue a warning when omitting studies due to NAs now indicate how many were omitted
properly documented the level
argument
added a few more transformation functions
small bug fixes
improved the documentation a bit
added conv.2x2()
function for reconstructing the cell frequencies in 2x2 tables based on other summary statistics
added conv.wald()
function for converting Wald-type confidence intervals and test statistics to sampling variances
added conv.fivenum()
function for estimating means and standard deviations from five-number summary values
added conv.delta()
function for transforming observed effect sizes or outcomes and their sampling variances using the delta method
added emmprep()
function to create a reference grid for use with the emmeans()
function from the package of the same name
exposed formatter functions fmtp()
, fmtx()
, and fmtt()
package numDeriv
moved from Suggests
to Depends
model.matrix.rma()
gains asdf
argument
corrected bug in vcalc()
(values for obs
and type
were taken directly as indices instead of using them as identifiers)
improved efficiency of vif()
when sim=TRUE
by reshuffling only the data needed in the model matrix; due to some edge cases, the simulation approach cannot be used when some redundant predictors were dropped from the original model; and when redundancies occur after reshuffling the data, the simulated (G)VIF value(s) are now set to Inf
instead of NA
selmodel()
gains type='trunc'
and type='truncest'
models (the latter should be considered experimental)
added exact="i"
option in permutest()
(to just return the number of iterations required for an exact permutation test)
escalc()
now provides more informative error messages when not specifying all required arguments to compute a particular measure
added measures "ZPHI"
, "ZTET"
, "ZPB"
, "ZBIS"
, and "ZSPCOR"
to escalc()
(but note that Fisher’s r-to-z transformation is not a variance-stabilizing transformation for these measures)
the variance of measure ZPCOR
is now calculated with 1/(ni-mi-3)
(instead of 1/(ni-mi-1)
), which provides a better approximation in small samples (and analogous to how the variance of ZCOR
is calculated with 1/(ni-3)
)
as with measure="SMD"
, one can now also use arguments di
and ti
to specify d-values and t-test statistics for measures RPB
, RBIS
, D2ORN
, and D2ORL
in escalc()
for measures COR
, UCOR
, and ZCOR
, can now use argument ti
to specify t-test statistics in escalc()
can also specify (two-sided) p-values (of the respective t-tests) for these measures (and for measures PCOR
, ZPCOR
, SPCOR
, and ZSPCOR
) via argument pi
(the sign of the p-value is taken to be the sign of the measure)
can also specify (semi-)partial correlations directly via argument ri
for measures PCOR
, ZPCOR
, SPCOR
, and ZSPCOR
when passing a correlation marix to rcalc()
, it now orders the elements (columnwise) based on the lower triangular part of the matrix, not the upper one (which is more consistent with what matreg()
expects as input when using the V
argument)
optimizers Rcgmin
and Rvmmin
are now available in rma.uni()
, rma.mv()
, rma.glmm()
, and selmodel()
improved the documentation a bit
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 NA
s 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 NA
s 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)
<dat.authoryear>
; therefore, the datasets are now called (old name -> new name
):
dat.bcg -> dat.colditz1994
dat.warfarin -> dat.hart1999
dat.los -> dat.normand1999
dat.co2 -> dat.curtis1998
dat.empint -> dat.mcdaniel1994
but 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 NA
s 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