`ranef.Rd`

The function calculates best linear unbiased predictions (BLUPs) of the random effects for objects of class `"rma.uni"`

and `"rma.mv"`

. Corresponding standard errors and prediction interval bounds are also provided.

# S3 method for rma.uni ranef(object, level, digits, transf, targs, ...) # S3 method for rma.mv ranef(object, level, digits, transf, targs, verbose=FALSE, ...)

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

level | numerical value between 0 and 100 specifying the prediction interval level (if unspecified, the default is to take the value from the object). |

digits | integer specifying the number of decimal places to which the printed results should be rounded (if unspecified, the default is to take the value from the object). |

transf | optional argument specifying the name of a function that should be used to transform the predicted values and interval bounds (e.g., |

targs | optional arguments needed by the function specified under |

verbose | logical indicating whether output should be generated on the progress of the computations (the default is |

... | other arguments. |

For objects of class `"rma.uni"`

, an object of class `"list.rma"`

. The object is a list containing the following components:

predicted values.

corresponding standard errors.

lower bound of the prediction intervals.

upper bound of the prediction intervals.

some additional elements/values.

For best linear unbiased predictions that combine the fitted values based on the fixed effects and the estimated contributions of the random effects, see `blup`

.

For predicted/fitted values that are based only on the fixed effects of the model, see `fitted.rma`

and `predict.rma`

.

Fixed-effects models (with or without moderators) do not contain random study effects. The BLUPs for these models will therefore be 0.

When using the `transf`

argument, the transformation is applied to the predicted values and the corresponding interval bounds. The standard errors are then set equal to `NA`

and are omitted from the printed output.

The normal distribution is used to calculate the prediction intervals. When the model was fitted with the Knapp and Hartung (2003) method (i.e., `test="knha"`

in the `rma.uni`

function), then the t-distribution with \(k-p\) degrees of freedom is used.

To be precise, it should be noted that the function actually calculates empirical BLUPs (eBLUPs), since the predicted values are a function of the estimated variance component(s).

Kackar, R. N., & Harville, D. A. (1981). Unbiasedness of two-stage estimation and prediction procedures for mixed linear models. Communications in Statistics, Theory and Methods, **10**, 1249--1261.

Raudenbush, S. W., & Bryk, A. S. (1985). Empirical Bayes meta-analysis. *Journal of Educational Statistics*, **10**, 75--98.

Robinson, G. K. (1991). That BLUP is a good thing: The estimation of random effects. *Statistical Science*, **6**, 15--32.

Searle, S. R., Casella, G., & McCulloch, C. E. (1992). *Variance components*. Hoboken, NJ: Wiley.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. *Journal of Statistical Software*, **36**(3), 1--48. http://www.jstatsoft.org/v36/i03/.

### calculate log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg) ### meta-analysis of the log risk ratios using a random-effects model res <- rma(yi, vi, data=dat) ### BLUPs of the random effects ranef(res)#> #> pred se pi.lb pi.ub #> 1 -0.0857 0.4092 -0.8877 0.7163 #> 2 -0.5372 0.3638 -1.2501 0.1758 #> 3 -0.2724 0.4296 -1.1144 0.5696 #> 4 -0.6834 0.2176 -1.1099 -0.2568 #> 5 0.4272 0.2606 -0.0835 0.9378 #> 6 -0.0700 0.1942 -0.4506 0.3105 #> 7 -0.5294 0.3759 -1.2662 0.2073 #> 8 0.7174 0.1882 0.3485 1.0863 #> 9 0.2077 0.2665 -0.3146 0.7300 #> 10 -0.5326 0.2837 -1.0886 0.0234 #> 11 0.3609 0.2046 -0.0401 0.7618 #> 12 0.4298 0.4491 -0.4504 1.3100 #> 13 0.5678 0.2821 0.0149 1.1207 #>