The function calculates best linear unbiased predictions (BLUPs) of the study-specific true outcomes by combining the fitted values based on the fixed effects and the estimated contributions of the random effects for objects of class "rma.uni". Corresponding standard errors and prediction interval bounds are also provided.

blup(x, ...)

# S3 method for rma.uni
blup(x, level, digits, transf, targs, ...)

## Arguments

x an object of class "rma.uni". numerical value between 0 and 100 specifying the prediction interval level (if unspecified, the default is to take the value from the object). 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). optional argument specifying the name of a function that should be used to transform the predicted values and interval bounds (e.g., transf=exp; see also transf). If unspecified, no transformation is used. optional arguments needed by the function specified under transf. other arguments.

## Value

An object of class "list.rma". The object is a list containing the following components:

pred

predicted values.

se

corresponding standard errors.

pi.lb

lower bound of the prediction intervals.

pi.ub

upper bound of the prediction intervals.

...

The "list.rma" object is formatted and printed with print.list.rma.

## Note

For best linear unbiased predictions of only the random effects, see ranef.

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

For conditional residuals (the deviations of the observed outcomes from the BLUPs), see rstandard.rma.uni with type="conditional".

Fixed-effects models (with or without moderators) do not contain random study effects. The BLUPs for these models will therefore be equal to the usual fitted values, that is, those obtained with fitted.rma and predict.rma.

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 value of $$\tau^2$$.

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.

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/.

rma.uni, predict.rma, fitted.rma, ranef.rma.uni

## Examples

### 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 true risk ratios for each study
blup(res, transf=exp)#>
#>      pred  pi.lb  pi.ub
#> 1  0.4492 0.2012 1.0032
#> 2  0.2860 0.1431 0.5716
#> 3  0.3727 0.1590 0.8740
#> 4  0.2471 0.1887 0.3236
#> 5  0.7502 0.4958 1.1352
#> 6  0.4563 0.3883 0.5362
#> 7  0.2882 0.1400 0.5936
#> 8  1.0029 0.8871 1.1338
#> 9  0.6024 0.3911 0.9279
#> 10 0.2873 0.1775 0.4651
#> 11 0.7021 0.5665 0.8702
#> 12 0.7522 0.3064 1.8469
#> 13 0.8635 0.5359 1.3915
#>
### illustrate shrinkage of BLUPs towards the (estimated) population average
res <- rma(yi, vi, data=dat)
blups <- blup(res)$pred plot(NA, NA, xlim=c(.8,2.4), ylim=c(-2,0.5), pch=19, xaxt="n", bty="n", xlab="", ylab="Log Risk Ratio")segments(rep(1,13), dat$yi, rep(2,13), blups, col="darkgray")points(rep(1,13), dat$yi, pch=19)points(rep(2,13), blups, pch=19)axis(side=1, at=c(1,2), labels=c("Observed\nValues", "BLUPs"), lwd=0)segments(.7, res$beta, 2.15, res$beta, lty="dotted")text(2.3, res$beta, expression(hat(mu)==-0.71), cex=1) 