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updates

On this page, you can find a description of the (recent) updates to the metafor package. Older updates are archived here.

- 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 the argument 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://codecov.io/github/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 Röver 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

Older updates are archived here.

updates.txt · Last modified: 2021/01/24 16:43 by Wolfgang Viechtbauer

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