The metafor Package

A Meta-Analysis Package for R

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updates [2022/08/27 16:13] – Wolfgang Viechtbauerupdates [2023/05/09 06:25] – Wolfgang Viechtbauer
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 On this page, you can find a description of the (recent) updates to the metafor package. Older updates are archived [[updates_old|here]]. On this page, you can find a description of the (recent) updates to the metafor package. Older updates are archived [[updates_old|here]].
 +
 +==== Version 4.2-0 (2023-05-08) ====
 +
 +  * 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
 +
 +==== Version 4.0-0 (2023-03-19) ====
 +
 +  * 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
  
 ==== Version 3.8-1 (2022-08-26) ==== ==== Version 3.8-1 (2022-08-26) ====
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   * added more tests (also for parallel operations); also, all tests updated to use proper tolerances instead of rounding   * added more tests (also for parallel operations); also, all tests updated to use proper tolerances instead of rounding
   * reorganized the documentation a bit   * reorganized the documentation a bit
- 
-==== Version 2.0-0 (2017-06-22) ==== 
- 
-  * 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 
- 
-==== Version 1.9-9 (2016-09-25) ==== 
- 
-  * 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 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) 
- 
-==== Version 1.9-8 (2015-05-28) ==== 
- 
-  * 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 Versions ==== ==== Older Versions ====
updates.txt · Last modified: 2024/03/29 09:58 by Wolfgang Viechtbauer