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Help with TSSEM Problem encountered

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No user picture. Kwabenaaaddo Joined: 02/01/2021
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Dear Forum (Mike),

I have been working on a TSSEM using R and encountering the error below, which I do not know how to address. I would be grateful if you could help me proceed with your suggestions and diagnosis. I have attached the data and R codes for your perusal. 

Thank you so much for your help in advance.

Regards,

Kwabena.

Error:
> #### Running of Stage1 model
> stage1random <- tssem1(Cov=cordat, n=data$NUMBER.OF.BANKS, method="REM", RE.type="Diag")
> summary(stage1random)
Error in 1:(rsum * csum) : NA/NaN argument
In addition: Warning message:
In rsum * csum : NAs produced by integer overflow

File attachments
Data in CSV file
TSSEM codes
Replied on Fri, 05/22/2026 - 09:53
Picture of user. AdminNeale Joined: Mar 01, 2013

Hi Kwabena

It seems that the number of banks is very large, and the Ns are too.  In particular, 

> pattern.n(cordat, data$NUMBER.OF.BANKS)
     A     B     C     D     E      F    G     H      I      J      K      L      M
A 40107  1160  1990   562  7395   2401  212   110  32738  10018   1174   2181  21420
B  1160 38462  3213  1087  6360   4280  212   362  30272  12692   4633   1553  15011
C  1990  3213 35496  2527  2898   2166   66    30  29131  13577   2337   3175  10920
D   562  1087  2527 33424  1820   1163   33    75  28039  15896   1878   1850  18458
E  7395  6360  2898  1820 90522  25454 1091  1664  68290  32573  18437  15652  44277
F  2401  4280  2166  1163 25454 128437 1459  3168 116760  37982  29447  14349  43101
G   212   212    66    33  1091   1459 8585  1783   5885   2322    429    501   3320
H   110   362    30    75  1664   3168 1783 12785  12272   4158    724     97   4536
I 32738 30272 29131 28039 68290 116760 5885 12272 587520 355681 270337  46350 464526
J 10018 12692 13577 15896 32573  37982 2322  4158 355681 419044  85388  26101 163423
K  1174  4633  2337  1878 18437  29447  429   724 270337  85388 285698  17235  29530
L  2181  1553  3175  1850 15652  14349  501    97  46350  26101  17235 171035 138945
M 21420 15011 10920 18458 44277  43101 3320  4536 464526 163423  29530 138945 480288

The row sums and column sums are the same as this matrix is symmetric.

These numbers, when processed, seem to be too large for representation as integers in R:

> rowSums(xx)
     A       B       C       D       E       F       G       H       I       J       K       L       M 
121468  119297  107526  106812  316433  410167   25898   41764 2047801 1178855  747247  439024 1437755 
> colSums(xx)
     A       B       C       D       E       F       G       H       I       J       K       L       M 
121468  119297  107526  106812  316433  410167   25898   41764 2047801 1178855  747247  439024 1437755 
> colSums(xx)*rowSum(xx)
Error in rowSum(xx) : could not find function "rowSum"
> colSums(xx)*rowSums(xx)
          A            B            C            D            E            F            G            H            I            J            K            L            M 
1.475448e+10 1.423177e+10 1.156184e+10 1.140880e+10 1.001298e+11 1.682370e+11 6.707064e+08 1.744232e+09 4.193489e+12 1.389699e+12 5.583781e+11 1.927421e+11 2.067139e+12 
> rsum <- sum(rowSums(xx))
> rsum^2
[1] 5.041067e+13
> is.integer(rsum^2)
[1] FALSE
> as.integer(rsum^2)
[1] NA
Warning message:
NAs introduced by coercion to integer range 

Mike Chung may have ideas here, but what I'm wondering is if the meta analysis could be done piecemeal, i.e., meta-analyze manageable subsets of the datasets until all are done, then meta-analyze the results of these multiple meta-analyses.  This is pure conjecture, I have no idea if this is a workable or suitable way out of your dilemma.  Usually, having too much data is a nice problem to have.

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Replied on Sun, 05/24/2026 - 01:29
Picture of user. Mike Cheung Joined: Oct 08, 2009

Hi Kwabena,

Michael's observation is accurate. It appears that the overflow occurs due to large sample sizes and too many effect sizes. This overflow seems to happen when calculating the homogeneity test of the correlation matrices. You can avoid this issue by using the command `summary(stage1random, homoStat=FALSE)`.

Best regards,  
Mike

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Replied on Wed, 05/27/2026 - 03:42
No user picture. Kwabenaaaddo Joined: Feb 01, 2021

Dear Neale and Mike,

Many thanks for your detailed response. I appreciate it. Updating the relevant part of the code to "summary(stage1random, homoStat=FALSE)" worked. However, I encountered another problem, which I share below, when I tried estimating the model in the second stage. Can you help me address that?

Error in wls(Cov = pooledS, aCov = aCov, n = tssem1.obj$total.n, RAM = RAM, :
"aCov" is not positive definite.
In addition: Warning message:
In .solve(x = object$mx.fit@output$calculatedHessian, parameters = my.name) :
  Error in solving the Hessian matrix. Generalized inverse is used. The standard errors may not be trustworthy.

Thank you in advance.

 

Regards,

Kwabena.
 

File attachments
Codes_OpenMX_May2026 (Autosaved).R
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Replied on Fri, 05/29/2026 - 23:14
Picture of user. Mike Cheung Joined: Oct 08, 2009

Hi Kwabena,

Your stage1random results may not be converged. You need to refit it again. As there are very few studies in some of the pairs of the correlations, one "ad-hoc" approach is to fix these variances at 0 automatically. I don't have any simulation results to support how good this approach is. You should probably acknowledge this in your limitations. After fixing these variances at 0, the asyptomtic sampling covariance matrix becomes positive definite.
 

By the way, there are some errors in your model. The variables RISK and PERF in the model are not present in your data.

<code>

> aCov=vcov(stage1random, select="fixed")
Warning message:
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.

> stage1_rerun <- metaSEM::rerun(stage1random, autofixtau2 = TRUE)
Solution found!  Final fit=-3383.1841 (started at -3383.1209)  (2 attempt(s): 2 valid, 0 errors)
Solution found!  Final fit=-3383.1841 (started at -3383.1841)  (1 attempt(s): 1 valid, 0 errors)

> aCov=vcov(stage1_rerun, select="fixed")
> is.pd(aCov)
[1] TRUE

> summary(stage1_rerun, homoStat=FALSE)

Call:
meta(y = ES, v = acovR, RE.constraints = Diag(paste0(RE.startvalues, 
   "*Tau2_", 1:no.es, "_", 1:no.es)), RE.lbound = RE.lbound, 
   I2 = I2, model.name = model.name, suppressWarnings = TRUE, 
   silent = silent, run = run)

95% confidence intervals: z statistic approximation (robust=FALSE)
Coefficients:
              Estimate   Std.Error      lbound      ubound  z value  Pr(>|z|)    
Intercept1   1.7712e-01  6.8024e-02  4.3794e-02  3.1045e-01   2.6038 0.0092206 ** 
Intercept2  -9.5526e-02  7.8316e-02 -2.4902e-01  5.7971e-02  -1.2197 0.2225624    
Intercept3   1.6001e-02  7.1196e-02 -1.2354e-01  1.5554e-01   0.2247 0.8221828    
Intercept4   4.9934e-02  2.9177e-02 -7.2517e-03  1.0712e-01   1.7114 0.0870030 .  
Intercept5   1.5823e-03  3.4989e-02 -6.6994e-02  7.0159e-02   0.0452 0.9639292    
Intercept6   3.4693e-03  6.4398e-02 -1.2275e-01  1.2969e-01   0.0539 0.9570373    
Intercept7   8.0646e-02  1.1376e-01 -1.4232e-01  3.0361e-01   0.7089 0.4783788    
Intercept8  -3.8636e-02  1.8155e-02 -7.4219e-02 -3.0538e-03  -2.1282 0.0333226 *  
Intercept9   8.8937e-02  4.0450e-02  9.6564e-03  1.6822e-01   2.1987 0.0279000 *  
Intercept10 -3.7478e-02  4.7905e-02 -1.3137e-01  5.6415e-02  -0.7823 0.4340183    
Intercept11  1.8734e-02  4.1453e-02 -6.2513e-02  9.9981e-02   0.4519 0.6513175    
Intercept12  3.7671e-02  1.4222e-02  9.7953e-03  6.5546e-02   2.6487 0.0080805 ** 
Intercept13  8.7988e-02  3.8970e-02  1.1607e-02  1.6437e-01   2.2578 0.0239574 *  
Intercept14  4.8745e-02  7.9884e-02 -1.0782e-01  2.0531e-01   0.6102 0.5417333    
Intercept15 -5.8540e-02  2.3640e-02 -1.0487e-01 -1.2206e-02  -2.4763 0.0132751 *  
Intercept16 -7.9997e-02  3.3723e-02 -1.4609e-01 -1.3900e-02  -2.3721 0.0176853 *  
Intercept17 -5.3103e-02  6.7849e-02 -1.8608e-01  7.9878e-02  -0.7827 0.4338234    
Intercept18  1.4631e-01  4.9979e-02  4.8356e-02  2.4427e-01   2.9275 0.0034172 ** 
Intercept19  1.3885e-02  1.1185e-02 -8.0376e-03  3.5807e-02   1.2414 0.2144699    
Intercept20 -2.8911e-02  2.0157e-02 -6.8418e-02  1.0596e-02  -1.4343 0.1514847    
Intercept21 -2.2602e-02  1.9742e-02 -6.1297e-02  1.6092e-02  -1.1449 0.2522667    
Intercept22  2.4561e-02  3.4196e-02 -4.2463e-02  9.1585e-02   0.7182 0.4726115    
Intercept23  3.3482e-02  1.3181e-02  7.6488e-03  5.9315e-02   2.5403 0.0110766 *  
Intercept24 -1.4567e-01  3.0132e-02 -2.0472e-01 -8.6607e-02  -4.8342 1.337e-06 ***
Intercept25 -3.3018e-02  4.6373e-02 -1.2391e-01  5.7872e-02  -0.7120 0.4764651    
Intercept26 -1.2234e-01  5.9106e-02 -2.3819e-01 -6.4941e-03  -2.0698 0.0384678 *  
Intercept27 -2.0034e-01  1.1809e-01 -4.3180e-01  3.1121e-02  -1.6964 0.0898030 .  
Intercept28 -5.2813e-01  1.3145e-01 -7.8576e-01 -2.7050e-01  -4.0178 5.875e-05 ***
Intercept29  4.9436e-02  1.2315e-02  2.5299e-02  7.3572e-02   4.0144 5.961e-05 ***
Intercept30  1.4518e-01  2.6504e-02  9.3229e-02  1.9712e-01   5.4775 4.315e-08 ***
Intercept31 -1.5332e-02  2.3337e-02 -6.1072e-02  3.0408e-02  -0.6570 0.5111950    
Intercept32  1.1007e-01  4.3281e-02  2.5239e-02  1.9490e-01   2.5431 0.0109866 *  
Intercept33 -4.1896e-02  1.8182e-02 -7.7533e-02 -6.2588e-03  -2.3042 0.0212121 *  
Intercept34  5.7359e-02  3.7341e-02 -1.5827e-02  1.3055e-01   1.5361 0.1245124    
Intercept35  7.0032e-02  4.7415e-02 -2.2899e-02  1.6296e-01   1.4770 0.1396724    
Intercept36  6.9726e-02  1.6837e-01 -2.6028e-01  3.9973e-01   0.4141 0.6787858    
Intercept37  8.9409e-03  1.0023e-01 -1.8750e-01  2.0538e-01   0.0892 0.9289180    
Intercept38 -1.9145e-02  1.2344e-02 -4.3339e-02  5.0494e-03  -1.5509 0.1209213    
Intercept39  1.4210e-02  2.7932e-02 -4.0535e-02  6.8955e-02   0.5088 0.6109245    
Intercept40 -1.4655e-02  2.7602e-02 -6.8755e-02  3.9444e-02  -0.5309 0.5954546    
Intercept41  6.4401e-02  4.2967e-02 -1.9814e-02  1.4861e-01   1.4988 0.1339170    
Intercept42 -9.1065e-03  1.5767e-02 -4.0010e-02  2.1797e-02  -0.5776 0.5635672    
Intercept43 -1.2399e-02  1.6805e-02 -4.5336e-02  2.0539e-02  -0.7378 0.4606443    
Intercept44 -1.6255e-01  3.5285e-02 -2.3171e-01 -9.3389e-02  -4.6067 4.092e-06 ***
Intercept45  1.4055e-01  5.1536e-02  3.9542e-02  2.4156e-01   2.7272 0.0063865 ** 
Intercept46 -2.5630e-02  7.3877e-03 -4.0110e-02 -1.1151e-02  -3.4693 0.0005218 ***
Intercept47  7.9606e-02  1.4754e-02  5.0688e-02  1.0852e-01   5.3954 6.837e-08 ***
Intercept48  3.3693e-02  1.7301e-02 -2.1572e-04  6.7602e-02   1.9475 0.0514754 .  
Intercept49  3.5768e-02  2.1997e-02 -7.3465e-03  7.8882e-02   1.6260 0.1039516    
Intercept50 -3.7519e-03  7.5986e-03 -1.8645e-02  1.1141e-02  -0.4938 0.6214725    
Intercept51  1.5675e-01  4.9542e-02  5.9647e-02  2.5385e-01   3.1639 0.0015565 ** 
Intercept52  1.5221e-01  2.0987e-02  1.1108e-01  1.9334e-01   7.2526 4.088e-13 ***
Intercept53  6.8378e-03  8.4760e-03 -9.7749e-03  2.3451e-02   0.8067 0.4198265    
Intercept54  8.4906e-02  1.5922e-02  5.3700e-02  1.1611e-01   5.3326 9.680e-08 ***
Intercept55 -2.7552e-03  1.7235e-02 -3.6536e-02  3.1025e-02  -0.1599 0.8729949    
Intercept56  1.0057e-01  2.1478e-02  5.8475e-02  1.4267e-01   4.6826 2.833e-06 ***
Intercept57  1.7347e-02  1.0257e-02 -2.7564e-03  3.7451e-02   1.6912 0.0907923 .  
Intercept58  4.2388e-03  2.2862e-02 -4.0570e-02  4.9047e-02   0.1854 0.8529071    
Intercept59 -3.0238e-02  1.2916e-02 -5.5553e-02 -4.9226e-03  -2.3411 0.0192279 *  
Intercept60 -4.8345e-02  2.0216e-02 -8.7969e-02 -8.7217e-03  -2.3914 0.0167851 *  
Intercept61  8.9759e-02  4.7129e-02 -2.6119e-03  1.8213e-01   1.9045 0.0568395 .  
Intercept62 -5.1574e-02  8.0714e-02 -2.0977e-01  1.0662e-01  -0.6390 0.5228435    
Intercept63  3.1129e-02  1.7117e-02 -2.4192e-03  6.4677e-02   1.8186 0.0689682 .  
Intercept64 -1.7481e-02  1.6704e-02 -5.0220e-02  1.5257e-02  -1.0466 0.2953056    
Intercept65  3.8807e-01  3.9370e-02  3.1091e-01  4.6524e-01   9.8572 < 2.2e-16 ***
Intercept66 -1.9433e-01  5.5146e-02 -3.0241e-01 -8.6247e-02  -3.5239 0.0004252 ***
Intercept67  2.4685e-01  9.4092e-02  6.2434e-02  4.3127e-01   2.6235 0.0087029 ** 
Intercept68  9.5771e-02  1.9791e-02  5.6981e-02  1.3456e-01   4.8390 1.305e-06 ***
Intercept69  5.8091e-02  8.6676e-03  4.1103e-02  7.5079e-02   6.7021 2.054e-11 ***
Intercept70 -5.6012e-03  8.4013e-03 -2.2068e-02  1.0865e-02  -0.6667 0.5049583    
Intercept71  4.0697e-02  1.5515e-02  1.0288e-02  7.1107e-02   2.6230 0.0087154 ** 
Intercept72 -1.0186e-01  6.7040e-03 -1.1500e-01 -8.8719e-02 -15.1936 < 2.2e-16 ***
Intercept73 -3.6597e-02  1.7632e-02 -7.1155e-02 -2.0385e-03  -2.0756 0.0379331 *  
Intercept74  2.1143e-01  2.4745e-02  1.6293e-01  2.5993e-01   8.5442 < 2.2e-16 ***
Intercept75  2.5249e-02  8.7682e-03  8.0639e-03  4.2435e-02   2.8796 0.0039813 ** 
Intercept76 -5.1968e-03  3.3166e-02 -7.0201e-02  5.9807e-02  -0.1567 0.8754894    
Intercept77  6.8057e-03  1.8285e-02 -2.9032e-02  4.2644e-02   0.3722 0.7097441    
Intercept78  1.9152e-02  7.6545e-03  4.1499e-03  3.4155e-02   2.5021 0.0123455 *  
Tau2_1_1     5.0983e-02  2.1966e-02  7.9309e-03  9.4035e-02   2.3210 0.0202856 *  
Tau2_2_2     4.9590e-02  3.3948e-02 -1.6947e-02  1.1613e-01   1.4608 0.1440810    
Tau2_3_3     5.0718e-02  3.0997e-02 -1.0035e-02  1.1147e-01   1.6362 0.1017910    
Tau2_4_4     1.4320e-02  6.1450e-03  2.2763e-03  2.6364e-02   2.3304 0.0197849 *  
Tau2_5_5     8.8260e-03  6.5711e-03 -4.0532e-03  2.1705e-02   1.3431 0.1792260    
Tau2_7_7     5.0325e-03  2.9747e-02 -5.3271e-02  6.3336e-02   0.1692 0.8656583    
Tau2_8_8     2.0734e-02  3.7963e-03  1.3294e-02  2.8175e-02   5.4617 4.717e-08 ***
Tau2_9_9     5.2922e-02  1.3811e-02  2.5853e-02  7.9990e-02   3.8319 0.0001271 ***
Tau2_10_10   1.7239e-02  1.2252e-02 -6.7752e-03  4.1252e-02   1.4070 0.1594319    
Tau2_11_11   8.1568e-03  1.3136e-02 -1.7590e-02  3.3903e-02   0.6209 0.5346376    
Tau2_12_12   5.0191e-03  1.7679e-03  1.5541e-03  8.4841e-03   2.8390 0.0045252 ** 
Tau2_13_13   2.1969e-02  1.0707e-02  9.8405e-04  4.2955e-02   2.0519 0.0401821 *  
Tau2_14_14   8.1178e-02  3.5423e-02  1.1750e-02  1.5061e-01   2.2917 0.0219250 *  
Tau2_15_15   1.5355e-02  5.4509e-03  4.6718e-03  2.6039e-02   2.8170 0.0048471 ** 
Tau2_16_16   1.8384e-02  7.3416e-03  3.9949e-03  3.2773e-02   2.5041 0.0122757 *  
Tau2_19_19   1.2061e-02  2.0491e-03  8.0446e-03  1.6077e-02   5.8859 3.958e-09 ***
Tau2_20_20   1.9918e-02  4.6453e-03  1.0813e-02  2.9022e-02   4.2877 1.806e-05 ***
Tau2_21_21   7.5367e-03  3.6027e-03  4.7551e-04  1.4598e-02   2.0920 0.0364429 *  
Tau2_22_22   8.0348e-03  6.5647e-03 -4.8317e-03  2.0901e-02   1.2239 0.2209717    
Tau2_23_23   8.0283e-03  2.3663e-03  3.3904e-03  1.2666e-02   3.3927 0.0006920 ***
Tau2_24_24   1.3480e-02  7.0601e-03 -3.5782e-04  2.7317e-02   1.9093 0.0562258 .  
Tau2_25_25   2.8341e-02  1.2971e-02  2.9180e-03  5.3764e-02   2.1849 0.0288942 *  
Tau2_26_26   1.9636e-02  1.4219e-02 -8.2327e-03  4.7504e-02   1.3810 0.1672911    
Tau2_27_27   1.0000e-10  3.9171e-02 -7.6774e-02  7.6774e-02   0.0000 1.0000000    
Tau2_29_29   6.7597e-03  2.8012e-03  1.2695e-03  1.2250e-02   2.4132 0.0158150 *  
Tau2_30_30   2.8596e-02  8.9851e-03  1.0986e-02  4.6207e-02   3.1826 0.0014596 ** 
Tau2_31_31   2.2338e-03  5.0980e-03 -7.7580e-03  1.2226e-02   0.4382 0.6612604    
Tau2_32_32   1.7804e-02  1.0076e-02 -1.9448e-03  3.7552e-02   1.7670 0.0772366 .  
Tau2_33_33   8.5483e-04  2.9246e-03 -4.8774e-03  6.5870e-03   0.2923 0.7700678    
Tau2_34_34   1.9038e-02  9.4764e-03  4.6488e-04  3.7612e-02   2.0090 0.0445350 *  
Tau2_35_35   1.4924e-02  9.7770e-03 -4.2385e-03  3.4087e-02   1.5264 0.1268981    
Tau2_38_38   5.9572e-03  1.8761e-03  2.2801e-03  9.6344e-03   3.1753 0.0014968 ** 
Tau2_39_39   3.1899e-02  8.3828e-03  1.5469e-02  4.8329e-02   3.8053 0.0001416 ***
Tau2_40_40   4.3124e-03  4.1404e-03 -3.8026e-03  1.2427e-02   1.0416 0.2976202    
Tau2_41_41   2.6486e-02  1.2114e-02  2.7427e-03  5.0230e-02   2.1864 0.0287888 *  
Tau2_42_42   1.1321e-02  3.3299e-03  4.7947e-03  1.7848e-02   3.3999 0.0006742 ***
Tau2_43_43   1.7067e-02  3.6850e-03  9.8442e-03  2.4289e-02   4.6314 3.633e-06 ***
Tau2_44_44   2.5940e-03  4.4627e-03 -6.1527e-03  1.1341e-02   0.5813 0.5610628    
Tau2_45_45   2.4446e-02  1.3016e-02 -1.0656e-03  4.9958e-02   1.8781 0.0603675 .  
Tau2_46_46   7.8180e-03  1.2148e-03  5.4370e-03  1.0199e-02   6.4357 1.229e-10 ***
Tau2_47_47   2.7225e-02  4.2704e-03  1.8855e-02  3.5595e-02   6.3753 1.826e-10 ***
Tau2_48_48   9.3214e-03  3.1574e-03  3.1331e-03  1.5510e-02   2.9523 0.0031544 ** 
Tau2_49_49   1.1640e-02  4.5663e-03  2.6907e-03  2.0590e-02   2.5492 0.0107963 *  
Tau2_50_50   5.3691e-03  1.1555e-03  3.1043e-03  7.6338e-03   4.6464 3.378e-06 ***
Tau2_51_51   1.6235e-02  1.2060e-02 -7.4026e-03  3.9874e-02   1.3462 0.1782465    
Tau2_52_52   1.9607e-03  2.7619e-03 -3.4526e-03  7.3740e-03   0.7099 0.4777619    
Tau2_53_53   7.1941e-03  1.4383e-03  4.3751e-03  1.0013e-02   5.0018 5.679e-07 ***
Tau2_54_54   1.4332e-02  3.3002e-03  7.8642e-03  2.0801e-02   4.3429 1.406e-05 ***
Tau2_55_55   3.5405e-03  2.3113e-03 -9.8950e-04  8.0706e-03   1.5318 0.1255601    
Tau2_56_56   2.8968e-03  2.1074e-03 -1.2335e-03  7.0271e-03   1.3746 0.1692516    
Tau2_57_57   5.4278e-03  1.5315e-03  2.4262e-03  8.4294e-03   3.5442 0.0003938 ***
Tau2_62_62   2.4178e-02  2.7282e-02 -2.9294e-02  7.7650e-02   0.8862 0.3754986    
Tau2_64_64   1.2469e-02  3.1543e-03  6.2869e-03  1.8652e-02   3.9531 7.715e-05 ***
Tau2_65_65   3.5617e-02  1.1236e-02  1.3595e-02  5.7639e-02   3.1699 0.0015247 ** 
Tau2_66_66   8.8088e-03  8.9664e-03 -8.7649e-03  2.6383e-02   0.9824 0.3258885    
Tau2_67_67   1.0000e-10  2.3601e-02 -4.6257e-02  4.6257e-02   0.0000 1.0000000    
Tau2_68_68   4.2605e-03  2.5860e-03 -8.0787e-04  9.3290e-03   1.6476 0.0994433 .  
Tau2_69_69   4.8438e-02  2.9844e-03  4.2589e-02  5.4288e-02  16.2303 < 2.2e-16 ***
Tau2_70_70   2.3541e-02  2.0086e-03  1.9605e-02  2.7478e-02  11.7203 < 2.2e-16 ***
Tau2_71_71   1.8572e-02  3.7647e-03  1.1194e-02  2.5951e-02   4.9333 8.086e-07 ***
Tau2_72_72   4.6959e-02  2.2842e-03  4.2482e-02  5.1436e-02  20.5584 < 2.2e-16 ***
Tau2_73_73   4.8183e-02  5.9537e-03  3.6514e-02  5.9852e-02   8.0929 6.661e-16 ***
Tau2_74_74   4.2730e-02  8.5781e-03  2.5918e-02  5.9543e-02   4.9813 6.315e-07 ***
Tau2_75_75   3.3016e-02  2.6140e-03  2.7893e-02  3.8140e-02  12.6305 < 2.2e-16 ***
[ reached 'max' / getOption("max.print") -- omitted 3 rows ]
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Q statistic on the homogeneity of effect sizes: NA
Degrees of freedom of the Q statistic: NA
P value of the Q statistic: NA

Heterogeneity indices (based on the estimated Tau2):
                             Estimate
Intercept1: I2 (Q statistic)    0.9458
Intercept2: I2 (Q statistic)    0.9387
Intercept3: I2 (Q statistic)    0.9398
Intercept4: I2 (Q statistic)    0.8157
Intercept5: I2 (Q statistic)    0.7311
Intercept6: I2 (Q statistic)    0.0000
Intercept7: I2 (Q statistic)    0.6138
Intercept8: I2 (Q statistic)    0.8664
Intercept9: I2 (Q statistic)    0.9529
Intercept10: I2 (Q statistic)   0.8417
Intercept11: I2 (Q statistic)   0.7153
Intercept12: I2 (Q statistic)   0.6081
Intercept13: I2 (Q statistic)   0.8796
Intercept14: I2 (Q statistic)   0.9634
Intercept15: I2 (Q statistic)   0.8255
Intercept16: I2 (Q statistic)   0.8530
Intercept17: I2 (Q statistic)   0.0000
Intercept18: I2 (Q statistic)   0.0000
Intercept19: I2 (Q statistic)   0.7877
Intercept20: I2 (Q statistic)   0.8599
Intercept21: I2 (Q statistic)   0.6989
Intercept22: I2 (Q statistic)   0.7121
Intercept23: I2 (Q statistic)   0.7122
Intercept24: I2 (Q statistic)   0.8134
Intercept25: I2 (Q statistic)   0.8978
Intercept26: I2 (Q statistic)   0.8603
Intercept27: I2 (Q statistic)   0.0000
Intercept28: I2 (Q statistic)   0.0000
Intercept29: I2 (Q statistic)   0.6756
Intercept30: I2 (Q statistic)   0.8996
Intercept31: I2 (Q statistic)   0.4075
Intercept32: I2 (Q statistic)   0.8462
Intercept33: I2 (Q statistic)   0.2087
Intercept34: I2 (Q statistic)   0.8550
Intercept35: I2 (Q statistic)   0.8219
Intercept36: I2 (Q statistic)   0.0000
Intercept37: I2 (Q statistic)   0.0000
Intercept38: I2 (Q statistic)   0.6470
Intercept39: I2 (Q statistic)   0.9121
Intercept40: I2 (Q statistic)   0.5703
Intercept41: I2 (Q statistic)   0.8940
Intercept42: I2 (Q statistic)   0.7790
Intercept43: I2 (Q statistic)   0.8400
Intercept44: I2 (Q statistic)   0.4569
Intercept45: I2 (Q statistic)   0.8840
Intercept46: I2 (Q statistic)   0.7065
Intercept47: I2 (Q statistic)   0.8951
Intercept48: I2 (Q statistic)   0.7415
Intercept49: I2 (Q statistic)   0.7857
Intercept50: I2 (Q statistic)   0.6231
Intercept51: I2 (Q statistic)   0.8425
Intercept52: I2 (Q statistic)   0.3886
Intercept53: I2 (Q statistic)   0.6889
Intercept54: I2 (Q statistic)   0.8153
Intercept55: I2 (Q statistic)   0.5216
Intercept56: I2 (Q statistic)   0.4720
Intercept57: I2 (Q statistic)   0.6256
Intercept58: I2 (Q statistic)   0.0000
Intercept59: I2 (Q statistic)   0.0000
Intercept60: I2 (Q statistic)   0.0000
Intercept61: I2 (Q statistic)   0.0000
Intercept62: I2 (Q statistic)   0.8815
Intercept63: I2 (Q statistic)   0.0000
Intercept64: I2 (Q statistic)   0.7935
Intercept65: I2 (Q statistic)   0.9419
Intercept66: I2 (Q statistic)   0.7451
Intercept67: I2 (Q statistic)   0.0000
Intercept68: I2 (Q statistic)   0.5720
Intercept69: I2 (Q statistic)   0.9376
Intercept70: I2 (Q statistic)   0.8787
Intercept71: I2 (Q statistic)   0.8513
Intercept72: I2 (Q statistic)   0.9373
Intercept73: I2 (Q statistic)   0.9373
Intercept74: I2 (Q statistic)   0.9348
Intercept75: I2 (Q statistic)   0.9104
Intercept76: I2 (Q statistic)   0.7769
Intercept77: I2 (Q statistic)   0.8983
Intercept78: I2 (Q statistic)   0.7481

Number of studies (or clusters): 1940
Number of observed statistics: 8464
Number of estimated parameters: 145
Degrees of freedom: 8319
-2 log likelihood: -3383.184 
OpenMx status1: 0 ("0" or "1": The optimization is considered fine.
Other values may indicate problems.)
</code>

 

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Replied on Sat, 05/30/2026 - 07:58
No user picture. Kwabenaaaddo Joined: Feb 01, 2021

Dear Mike,

Many thanks for the new suggestion. I see that it works well based on your output. I have adjusted the variance to 0, using the new command. To address the first point you made: PERF and RISK were mistakes; they are M and I, respectively, in the shared file. I am presuming you adjusted them accordingly to arrive at the output you shared.

Finally, I am unable to estimate the model anymore as the process crashes in the stage1 estimation. Since you have been able to estimate both stages, my only guess is that my computer (Mac OS Catalina, 16 GB, with R version: R 4.2.3, GUI 1.79, High Sierra build (8198)) is under-capacitated. Could that be the case?

 

Regards,

Kwabena.

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