Running examples in 'metafor-Ex.R' failed
The error most likely occurred in:

> ### Name: dat.pritz1997
> ### Title: Studies on the Effectiveness of Hyperdynamic Therapy for
> ###   Treating Cerebral Vasospasm
> ### Aliases: dat.pritz1997
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ### copy data into 'dat' and examine data
> dat <- dat.pritz1997
> dat
   study              authors xi ni
1      1     Giannotta et al. 16 17
2      2  Haraguchi and Ebina 10 12
3      3    Swift and Solomon  4  8
4      4       Kassell et al. 43 58
5      5        Tanabe et al. 10 10
6      6          Awad et al. 25 42
7      7          Finn et al. 13 14
8      8      Hadeishi et al. 12 12
9      9        Otsubo et al. 22 41
10    10 Muizelaar and Becker  4  5
11    11    Rosenstein et al.  5  6
12    12          Levy et al. 18 23
13    13       Shimoda et al. 58 68
14    14       Solomon et al.  6 10
> 
> ### computation of "weighted average" in Zhou et al. (1999), Table IV
> dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat, add=0)
> theta.hat    <- sum(dat$ni * dat$yi) / sum(dat$ni)
> se.theta.hat <- sqrt(sum(dat$ni^2 * dat$vi) / sum(dat$ni)^2)
> ci.lb        <- theta.hat - 1.96 * se.theta.hat
> ci.ub        <- theta.hat + 1.96 * se.theta.hat
> round(c(estimate = theta.hat, se = se.theta.hat, ci.lb = ci.lb, ci.ub = ci.ub), 4)
estimate       se    ci.lb    ci.ub 
  0.7546   0.0224   0.7106   0.7986 
> 
> ### this is identical to a FE model with sample size weights
> rma(yi, vi, weights=ni, method="FE", data=dat)
Warning: There are outcomes with non-positive sampling variances.
Warning: Cannot compute Q-test, I^2, or H^2 when there are non-positive sampling variances in the data.

Fixed-Effects Model (k = 14)

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub 
  0.7546  0.0224  33.6491  <.0001  0.7106  0.7986  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> 
> ### random-effects model with raw proportions
> dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat)
> res <- rma(yi, vi, data=dat)
> predict(res)

   pred     se  ci.lb  ci.ub  pi.lb  pi.ub 
 0.7968 0.0423 0.7138 0.8797 0.5306 1.0629 

> 
> ### random-effects model with logit transformed proportions
> dat <- escalc(measure="PLO", xi=xi, ni=ni, data=dat)
> res <- rma(yi, vi, data=dat)
> predict(res, transf=transf.ilogit)

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.7575 0.6605 0.8337 0.4661 0.9179 

> 
> ### mixed-effects logistic regression model
> res <- rma.glmm(measure="PLO", xi=xi, ni=ni, data=dat)
Error in rma.glmm(measure = "PLO", xi = xi, ni = ni, data = dat) : 
  Please install the 'lme4' package to fit this model.
Execution halted