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

> ### Name: BCG
> ### Title: Dataset on the Effectiveness of the BCG Vaccine for Preventing
> ###   Tuberculosis
> ### Aliases: BCG
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(BCG)
> 
> ## Univariate meta-analysis on the log of the odds ratio
> summary( meta(y=ln_OR, v=v_ln_OR, data=BCG,
+               x=cbind(scale(Latitude,scale=FALSE),
+               scale(Year,scale=FALSE))) )

Call:
meta(y = ln_OR, v = v_ln_OR, x = cbind(scale(Latitude, scale = FALSE), 
    scale(Year, scale = FALSE)), data = BCG)

95% confidence intervals: z statistic approximation (robust=FALSE)
Coefficients:
             Estimate  Std.Error     lbound     ubound z value Pr(>|z|)
Intercept1 -0.7166884         NA         NA         NA      NA       NA
Slope1_1   -0.0335019         NA         NA         NA      NA       NA
Slope1_2   -0.0013515  0.0043420 -0.0098617  0.0071587 -0.3113   0.7556
Tau2_1_1    0.0020944  0.0043412 -0.0064142  0.0106030  0.4824   0.6295

Q statistic on the homogeneity of effect sizes: 163.1649
Degrees of freedom of the Q statistic: 12
P value of the Q statistic: 0

Explained variances (R2):
                           y1
Tau2 (no predictor)    0.3025
Tau2 (with predictors) 0.0021
R2                     0.9931

Number of studies (or clusters): 13
Number of observed statistics: 13
Number of estimated parameters: 4
Degrees of freedom: 9
-2 log likelihood: 13.89208 
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 on the log of the odds
> ## The conditional sampling covariance is 0
> bcg <- meta(y=cbind(ln_Odd_V, ln_Odd_NV), data=BCG,
+             v=cbind(v_ln_Odd_V, cov_V_NV, v_ln_Odd_NV))
> summary(bcg)

Call:
meta(y = cbind(ln_Odd_V, ln_Odd_NV), v = cbind(v_ln_Odd_V, cov_V_NV, 
    v_ln_Odd_NV), data = BCG)

95% confidence intervals: z statistic approximation (robust=FALSE)
Coefficients:
            Estimate Std.Error    lbound    ubound z value  Pr(>|z|)    
Intercept1 -4.833744        NA        NA        NA      NA        NA    
Intercept2 -4.095975        NA        NA        NA      NA        NA    
Tau2_1_1    1.431371  0.155074  1.127431  1.735310  9.2302 < 2.2e-16 ***
Tau2_2_1    1.757327  0.034542  1.689626  1.825027 50.8755 < 2.2e-16 ***
Tau2_2_2    2.407333  0.265609  1.886749  2.927916  9.0635 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Q statistic on the homogeneity of effect sizes: 5270.386
Degrees of freedom of the Q statistic: 24
P value of the Q statistic: 0

Heterogeneity indices (based on the estimated Tau2):
                             Estimate
Intercept1: I2 (Q statistic)   0.9887
Intercept2: I2 (Q statistic)   0.9955

Number of studies (or clusters): 13
Number of observed statistics: 26
Number of estimated parameters: 5
Degrees of freedom: 21
-2 log likelihood: 66.17587 
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.

> 
> plot(bcg)
Warning in .solve(x = object$mx.fit@output$calculatedHessian, parameters = my.name) :
  Error in solving the Hessian matrix. Generalized inverse is used. The standard errors may not be trustworthy.

Warning in sqrt(c(x[xind, xind], x[yind, yind])) : NaNs produced
Error in if (scale[1] > 0) r <- r/scale[1] : 
  missing value where TRUE/FALSE needed
Calls: plot -> plot.meta -> points -> ellipse -> ellipse.default
Execution halted