`weights.rma.Rd`

The function computes the weights given to the observed effects or outcomes during the model fitting for objects of class `"rma.uni"`

, `"rma.mh"`

, `"rma.peto"`

, and `"rma.mv"`

.

# S3 method for rma.uni weights(object, type="diagonal", ...) # S3 method for rma.mh weights(object, type="diagonal", ...) # S3 method for rma.peto weights(object, type="diagonal", ...) # S3 method for rma.glmm weights(object, ...) # S3 method for rma.mv weights(object, type="diagonal", ...)

object | an object of class |
---|---|

type | character string to specify whether to return only the diagonal of the weight matrix ( |

... | other arguments. |

Either a vector with the diagonal elements of the weight matrix or the entire weight matrix. When only the diagonal elements are returned, they are given in % (and they add up to 100%).

When the entire weight matrix is requested, this is always a diagonal matrix for objects of class `"rma.uni"`

, `"rma.mh"`

, `"rma.peto"`

.

For `"rma.mv"`

, the structure of the weight matrix depends on the model fitted (i.e., the random effects included and the variance-covariance matrix of the sampling errors) but is often more complex and not just diagonal.

For `"rma.mv"`

intercept-only models, one can also take the sum over the rows in the weight matrix, which are actually the weights assigned to the observed effects / outcomes when estimating the model intercept. These weights can be obtained with `type="rowsum"`

(as with `type="diagonal"`

, they are also given in %).

Wolfgang Viechtbauer wvb@metafor-project.org http://www.metafor-project.org

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. *Journal of Statistical Software*, **36**(3), 1--48. https://doi.org/10.18637/jss.v036.i03

### meta-analysis of the log risk ratios using a mixed-effects model ### with two moderators (absolute latitude and publication year) res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, mods = ~ ablat + year, data=dat.bcg) weights(res)#> 1 2 3 4 5 6 7 8 #> 3.366420 4.810621 2.791974 11.231203 9.068114 12.481731 4.400814 12.801967 #> 9 10 11 12 13 #> 8.784824 7.991920 11.923841 2.283581 8.062988