Running examples in ‘MAc-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: MAc-package
> ### Title: Meta-Analysis with Correlations
> ### Aliases: MAc-package MAc
> ### Keywords: package
> 
> ### ** Examples
>  
> # Examples for each broad area:
> 
> id<-c(1:20)
> n<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)
> r<-c(.68,.56,.23,.64,.49,-.04,.49,.33,.58,.18,-.11,.27,.26,.40,.49,
+ .51,.40,.34,.42,.16)
> mod1<-c(1,2,3,4,1,2,8,7,5,3,9,7,5,4,3,2,3,5,7,1)
> dat<-data.frame(id,n,r,mod1)
> dat$var.r <- var_r(dat$r, dat$n) # MAc function to derive variance
> dat$z <- r_to_z(dat$r)  # MAc function to convert to Fisher's z (z')
> dat$var.z <- var_z(dat$n)  # MAc function for variance of z'
> dat$mods2 <- factor(rep(1:2, 10))
> dat
   id   n     r mod1      var.r           z       var.z mods2
1   1  10  0.68    1 0.03211264  0.82911404 0.142857143     1
2   2  20  0.56    2 0.02479710  0.63283319 0.058823529     2
3   3  13  0.23    3 0.07474987  0.23418947 0.100000000     1
4   4  22  0.64    4 0.01659867  0.75817374 0.052631579     2
5   5  28  0.49    1 0.02138696  0.53606034 0.040000000     1
6   6  12 -0.04    2 0.09061841 -0.04002135 0.111111111     2
7   7  12  0.49    8 0.05249527  0.53606034 0.111111111     1
8   8  36  0.33    7 0.02268741  0.34282825 0.030303030     2
9   9  19  0.58    5 0.02446472  0.66246271 0.062500000     1
10 10  12  0.18    3 0.08511361  0.18198269 0.111111111     2
11 11  36 -0.11    9 0.02788418 -0.11044692 0.030303030     1
12 12  75  0.27    7 0.01161506  0.27686382 0.013888889     2
13 13  33  0.26    5 0.02716780  0.26610841 0.033333333     1
14 14 121  0.40    4 0.00588000  0.42364893 0.008474576     2
15 15  37  0.49    3 0.01604022  0.53606034 0.029411765     1
16 16  14  0.51    2 0.04211169  0.56272977 0.090909091     2
17 17  40  0.40    3 0.01809231  0.42364893 0.027027027     1
18 18  16  0.34    5 0.05214422  0.35409253 0.076923077     2
19 19  14  0.42    7 0.05217823  0.44769202 0.090909091     1
20 20  20  0.16    1 0.04997133  0.16138670 0.058823529     2
> 
> # Note: for all the examples in this manual, we have made up data and manually 
> # created variables and datasets. If conducting your own meta-analysis, 
> # a more convenient way for using the functions is to import your data from
> # a .csv file (with relevant variables in the dataset). One way to do this:
> # dat <- read.csv(file.choose(), header = TRUE)
> #
> # Then, you can run the functions with this dataset and you do not need to 
> # manually create your dataset, as we have done above.
>   
>   
> 
> # 1. Computations to Calculate Correlations:
>  
> # For example, suppose the primary study reported a t-test value for differences 
> # between 2 groups and the total sample size. Then, running:
> 
> r_from_t (1.74, 30)  
             r      var_r
[1,] 0.3123742 0.02808159
> 
> # reported t-value (1.74) and sample size (30) will output the 
> # correlation desired for use in the meta-analysis.
> 
> # 2. Within-Study Aggregation of Effect Sizes: 
> 
> agg(id = id, r = r, n = n, data=dat) 
   id     r   n
1   1  0.68  10
2   2  0.56  20
3   3  0.23  13
4   4  0.64  22
5   5  0.49  28
6   6 -0.04  12
7   7  0.49  12
8   8  0.33  36
9   9  0.58  19
10 10  0.18  12
11 11 -0.11  36
12 12  0.27  75
13 13  0.26  33
14 14  0.40 121
15 15  0.49  37
16 16  0.51  14
17 17  0.40  40
18 18  0.34  16
19 19  0.42  14
20 20  0.16  20
> 
> # where data = data.frame with columns for id, r (correlation coefficient),
> # and n (sample size) with multiple rows within each study (multiple
> # correlations reported for each study). Outputs an aggregated data.frame 
> # with 1 effect size per study. 
> 
> # 3. Fixed and Random Effects Omnibus Analysis
> 
> omni(es = z, var = var.z, data = dat, type="weighted", method = "random", ztor = TRUE)

 Note: 
       If using r and the variance of r as input, be sure to 
       leave the default ztor = FALSE, otherwise the output will be
       inaccurate. If using z' (Fisher's z) and the variance of z'
       as the input, changing ztor = TRUE will convert z' back to r 
       (for interpretive purposes) after all analyses have been 
       conducted.  
 ---- 

 Model Results:  
 
   k estimate    var     se   ci.l   ci.u z p
1 20   0.3634 0.0022 0.0474 0.2802 0.4413 1 0

 Heterogeneity:  
 
        Q df.Q     Qp      I2
1 20.9744   19 0.3382 9.4136%
> 
> # where data = data.frame with columns for id, es (r or z')
> # , var (variance of r or z'), n (sample size). ztor = if using z', should
> # it be converted back to r? see omni documentation for more details.
>  
> # 4. Moderator Analyses:
> 
> # Random effects
> mareg(z~ mod1 + mods2, var = var.z, method = "REML", ztor = TRUE, data = dat) 
Error: package or namespace load failed for ‘Matrix’ in dyn.load(file, DLLpath = DLLpath, ...):
 unable to load shared object '/home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/lattice/libs/lattice.so':
  /home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/lattice/libs/lattice.so: failed to map segment from shared object
Error in rma(yi = yi, vi = vi, method = method, ...) : 
  could not find function "rma"
Calls: mareg -> mareg.default
Execution halted