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

> ### Name: Berkey98
> ### Title: Five Published Trails from Berkey et al. (1998)
> ### Aliases: Berkey98
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(Berkey98)
> 
> #### ML estimation method
> ## Multivariate meta-analysis
> x <- meta(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL), data=Berkey98)
> x <- rerun(x)
Running Meta analysis with ML with 5 parameters

Beginning initial fit attempt
Running Meta analysis with ML with 5 parameters
 OpenMx status code 6 not in list of acceptable status codes, (0,1)
 Not all eigenvalues of the Hessian are positive: 9934988429.49698, 125322.754223701, 18951.5856106602, 1916.32350679377, -3794821642.05127

Beginning fit attempt 1 of at maximum 10 extra tries
Running Meta analysis with ML with 5 parameters

 Fit attempt generated errors

Beginning fit attempt 2 of at maximum 10 extra tries
Running Meta analysis with ML with 5 parameters

 Lowest minimum so far:  -11.6813137695501
 OpenMx status code 6 not in list of acceptable status codes, (0,1)
 Not all eigenvalues of the Hessian are positive: 12198571197.1692, 125322.759991431, 18951.5808351482, 1916.32252636003, -4659433322.95327

Beginning fit attempt 3 of at maximum 10 extra tries
Running Meta analysis with ML with 5 parameters

Solution found




 Solution found!  Final fit=-11.681314 (started at -11.681314)  (4 attempt(s): 3 valid, 1 errors)

> summary(x)

Call:
meta(y = cbind(PD, AL), v = cbind(var_PD, cov_PD_AL, var_AL), 
    data = Berkey98)

95% confidence intervals: z statistic approximation (robust=FALSE)
Coefficients:
             Estimate  Std.Error     lbound     ubound z value  Pr(>|z|)    
Intercept1  0.3448392  0.0536312  0.2397239  0.4499544  6.4298 1.278e-10 ***
Intercept2 -0.3379381  0.0812479 -0.4971812 -0.1786951 -4.1593 3.192e-05 ***
Tau2_1_1    0.0070020  0.0090497 -0.0107351  0.0247391  0.7737    0.4391    
Tau2_2_1    0.0094607  0.0099698 -0.0100797  0.0290010  0.9489    0.3427    
Tau2_2_2    0.0261445  0.0177409 -0.0086270  0.0609161  1.4737    0.1406    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Q statistic on the homogeneity of effect sizes: 128.2267
Degrees of freedom of the Q statistic: 8
P value of the Q statistic: 0

Heterogeneity indices (based on the estimated Tau2):
                             Estimate
Intercept1: I2 (Q statistic)   0.6021
Intercept2: I2 (Q statistic)   0.9250

Number of studies (or clusters): 5
Number of observed statistics: 10
Number of estimated parameters: 5
Degrees of freedom: 5
-2 log likelihood: -11.68131 
OpenMx status1: 0 ("0" or "1": The optimization is considered fine.
Other values may indicate problems.)
> plot(x)
> 
> ## Plot individual studies proportional to the weights
> plot(x, study.weight.plot=TRUE)
> 
> ## Include forest plot from the metafor package
> library(metafor)
Loading required package: Matrix

Attaching package: ‘Matrix’

The following objects are masked from ‘package:OpenMx’:

    %&%, expm

Loading 'metafor' package (version 2.4-0). For an overview 
and introduction to the package please type: help(metafor).
> plot(x, diag.panel=TRUE, main="Multivariate meta-analysis",
+      axis.label=c("PD", "AL"))
>      forest( rma(yi=PD, vi=var_PD, data=Berkey98) )
>      title("Forest plot of PD")
>      forest( rma(yi=AL, vi=var_AL, data=Berkey98) )
>      title("Forest plot of AL")
> 
> ## Multivariate meta-analysis with "publication year-1979" as the predictor
> summary( meta(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL),
+               x=scale(pub_year, center=1979), data=Berkey98,
+               RE.lbound=NA) )

Call:
meta(y = cbind(PD, AL), v = cbind(var_PD, cov_PD_AL, var_AL), 
    x = scale(pub_year, center = 1979), data = Berkey98, RE.lbound = NA)

95% confidence intervals: z statistic approximation (robust=FALSE)
Coefficients:
              Estimate   Std.Error      lbound      ubound     z value Pr(>|z|)
Intercept1  3.4400e-01  3.1559e-06  3.4399e-01  3.4401e-01 109003.3809   <2e-16
Intercept2 -2.9182e-01  5.2308e-05 -2.9192e-01 -2.9171e-01  -5578.8813   <2e-16
Slope1_1    6.3540e-03  4.2153e-02 -7.6264e-02  8.8972e-02      0.1507   0.8802
Slope2_1   -7.0589e-02  4.5315e-02 -1.5940e-01  1.8227e-02     -1.5577   0.1193
Tau2_1_1    8.0405e-03  9.2154e-03 -1.0021e-02  2.6102e-02      0.8725   0.3829
Tau2_2_1    9.3413e-03  9.6744e-03 -9.6202e-03  2.8303e-02      0.9656   0.3343
Tau2_2_2    2.5013e-02  1.6635e-02 -7.5904e-03  5.7617e-02      1.5037   0.1327
              
Intercept1 ***
Intercept2 ***
Slope1_1      
Slope2_1      
Tau2_1_1      
Tau2_2_1      
Tau2_2_2      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Q statistic on the homogeneity of effect sizes: 128.2267
Degrees of freedom of the Q statistic: 8
P value of the Q statistic: 0

Explained variances (R2):
                              y1     y2
Tau2 (no predictor)    0.0070020 0.0261
Tau2 (with predictors) 0.0080405 0.0250
R2                     0.0000000 0.0433

Number of studies (or clusters): 5
Number of observed statistics: 10
Number of estimated parameters: 7
Degrees of freedom: 3
-2 log likelihood: -12.00859 
OpenMx status1: 6 ("0" or "1": The optimization is considered fine.
Other values may indicate problems.)
Warning in print.summary.meta(x) :
  OpenMx status1 is neither 0 or 1. You are advised to 'rerun' it again.

> 
> ## Multivariate meta-analysis with equality constraint on the regression coefficients
> summary( meta(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL),
+               x=scale(pub_year, center=1979), data=Berkey98,
+               coef.constraints=matrix(c("0.3*Eq_slope", "0.3*Eq_slope"),
+               nrow=2)) )

Call:
meta(y = cbind(PD, AL), v = cbind(var_PD, cov_PD_AL, var_AL), 
    x = scale(pub_year, center = 1979), data = Berkey98, coef.constraints = matrix(c("0.3*Eq_slope", 
        "0.3*Eq_slope"), nrow = 2))

95% confidence intervals: z statistic approximation (robust=FALSE)
Coefficients:
              Estimate   Std.Error      lbound      ubound    z value Pr(>|z|)
Intercept1  3.4376e-01  4.2699e-05  3.4368e-01  3.4384e-01  8050.7266   <2e-16
Intercept2 -3.3900e-01  1.0544e-04 -3.3921e-01 -3.3879e-01 -3215.1160   <2e-16
Eq_slope    1.6748e-03  2.5127e-02 -4.7573e-02  5.0923e-02     0.0667   0.9469
Tau2_1_1    7.0474e-03  7.6032e-03 -7.8545e-03  2.1949e-02     0.9269   0.3540
Tau2_2_1    9.5165e-03  8.3910e-03 -6.9297e-03  2.5963e-02     1.1341   0.2567
Tau2_2_2    2.6198e-02  1.5956e-02 -5.0748e-03  5.7471e-02     1.6419   0.1006
              
Intercept1 ***
Intercept2 ***
Eq_slope      
Tau2_1_1      
Tau2_2_1      
Tau2_2_2      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Q statistic on the homogeneity of effect sizes: 128.2267
Degrees of freedom of the Q statistic: 8
P value of the Q statistic: 0

Explained variances (R2):
                              y1     y2
Tau2 (no predictor)    0.0070020 0.0261
Tau2 (with predictors) 0.0070474 0.0262
R2                     0.0000000 0.0000

Number of studies (or clusters): 5
Number of observed statistics: 10
Number of estimated parameters: 6
Degrees of freedom: 4
-2 log likelihood: -11.68158 
OpenMx status1: 6 ("0" or "1": The optimization is considered fine.
Other values may indicate problems.)
Warning in print.summary.meta(x) :
  OpenMx status1 is neither 0 or 1. You are advised to 'rerun' it again.

> 
> #### REML estimation method
> ## Multivariate meta-analysis
> summary( reml(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL),
+               data=Berkey98,
+               model.name="Multivariate meta analysis with REML") )
Error: C stack usage  279221396 is too close to the limit
Error in running the mxModel:
<simpleError: The job for model 'Multivariate meta analysis with REML' exited abnormally with the error message: User interrupt>
Error in (function (cond)  : 
  error in evaluating the argument 'object' in selecting a method for function 'summary': The job for model 'Multivariate meta analysis with REML' exited abnormally with the error message: User interrupt
Calls: summary -> reml
Execution halted