> stage1random <- tssem1(Cov=cordat, n=data$N, method="REM") > stage1random <- rerun(stage1random, autofixtau2 = TRUE) Error in mxTryHard(object$mx.fit, greenOK = TRUE, paste = FALSE, bestInitsOutput = FALSE, : unused argument (autofixtau2 = TRUE) > summary(stage1random) 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 Coefficients: Estimate Std.Error lbound ubound z value Pr(>|z|) Intercept1 3.5674e-02 2.3793e-02 -1.0959e-02 8.2307e-02 1.4994 0.133779 Intercept2 1.0534e-01 1.6971e-01 -2.2728e-01 4.3797e-01 0.6207 0.534779 Intercept3 4.9244e-01 5.1627e-02 3.9126e-01 5.9363e-01 9.5385 < 2.2e-16 *** Intercept4 7.9443e-02 5.8847e-02 -3.5895e-02 1.9478e-01 1.3500 0.177020 Intercept5 1.4850e-01 1.0990e-01 -6.6903e-02 3.6391e-01 1.3512 0.176625 Intercept6 1.3393e-01 2.4096e-02 8.6707e-02 1.8116e-01 5.5583 2.724e-08 *** Intercept7 2.2790e-01 3.4890e-02 1.5952e-01 2.9629e-01 6.5320 6.490e-11 *** Intercept8 2.1995e-01 1.0410e-02 1.9954e-01 2.4035e-01 21.1278 < 2.2e-16 *** Intercept9 2.8060e-01 3.7708e-02 2.0669e-01 3.5450e-01 7.4413 9.970e-14 *** Intercept10 1.6877e-01 9.4175e-02 -1.5812e-02 3.5335e-01 1.7921 0.073123 . Intercept11 3.1030e-01 2.5045e-02 2.6122e-01 3.5939e-01 12.3899 < 2.2e-16 *** Intercept12 1.2881e-01 1.2664e-01 -1.1941e-01 3.7702e-01 1.0171 0.309105 Intercept13 2.6362e-01 2.4469e-02 2.1567e-01 3.1158e-01 10.7739 < 2.2e-16 *** Intercept14 -5.7898e-02 5.7696e-02 -1.7098e-01 5.5184e-02 -1.0035 0.315616 Intercept15 3.5509e-01 3.3831e-02 2.8878e-01 4.2139e-01 10.4959 < 2.2e-16 *** Tau2_1_1 7.6624e-10 NA NA NA NA NA Tau2_2_2 5.1880e-02 5.7493e-02 -6.0804e-02 1.6456e-01 0.9024 0.366857 Tau2_3_3 2.2703e-10 5.7266e-03 -1.1224e-02 1.1224e-02 0.0000 1.000000 Tau2_4_4 3.5346e-02 1.8502e-02 -9.1706e-04 7.1609e-02 1.9104 0.056082 . Tau2_5_5 7.2719e-02 4.6165e-02 -1.7762e-02 1.6320e-01 1.5752 0.115209 Tau2_6_6 8.1903e-03 3.7012e-03 9.3600e-04 1.5445e-02 2.2129 0.026908 * Tau2_7_7 1.9862e-02 9.1420e-03 1.9439e-03 3.7780e-02 2.1726 0.029810 * Tau2_8_8 1.7525e-02 2.2351e-03 1.3144e-02 2.1906e-02 7.8408 4.441e-15 *** Tau2_9_9 1.2313e-02 7.2248e-03 -1.8471e-03 2.6474e-02 1.7043 0.088325 . Tau2_10_10 3.0139e-02 2.6186e-02 -2.1185e-02 8.1462e-02 1.1510 0.249752 Tau2_11_11 2.6842e-02 6.8011e-03 1.3512e-02 4.0172e-02 3.9466 7.926e-05 *** Tau2_12_12 8.4209e-02 5.6044e-02 -2.5635e-02 1.9405e-01 1.5026 0.132952 Tau2_13_13 1.5596e-02 5.1774e-03 5.4482e-03 2.5743e-02 3.0123 0.002593 ** Tau2_14_14 4.3752e-02 2.0684e-02 3.2112e-03 8.4293e-02 2.1152 0.034412 * Tau2_15_15 9.6711e-03 5.6447e-03 -1.3923e-03 2.0734e-02 1.7133 0.086657 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Q statistic on the homogeneity of effect sizes: 2673.109 Degrees of freedom of the Q statistic: 423 P value of the Q statistic: 0 Heterogeneity indices (based on the estimated Tau2): Estimate Intercept1: I2 (Q statistic) 0.0000 Intercept2: I2 (Q statistic) 0.9474 Intercept3: I2 (Q statistic) 0.0000 Intercept4: I2 (Q statistic) 0.9246 Intercept5: I2 (Q statistic) 0.9619 Intercept6: I2 (Q statistic) 0.7398 Intercept7: I2 (Q statistic) 0.8733 Intercept8: I2 (Q statistic) 0.8643 Intercept9: I2 (Q statistic) 0.8104 Intercept10: I2 (Q statistic) 0.9127 Intercept11: I2 (Q statistic) 0.9032 Intercept12: I2 (Q statistic) 0.9669 Intercept13: I2 (Q statistic) 0.8441 Intercept14: I2 (Q statistic) 0.9382 Intercept15: I2 (Q statistic) 0.7705 Number of studies (or clusters): 379 Number of observed statistics: 438 Number of estimated parameters: 30 Degrees of freedom: 408 -2 log likelihood: -341.7506 OpenMx status1: 5 ("0" or "1": The optimization is considered fine. Other values may indicate problems.) > A<-create.mxMatrix (c(0,0,0,0,0,0 + ,0,0,0,0,0,0, + 0,0,0,0,0,0, + "0.1*b41","0.1*b42",0,0,0,0, + 0,0,"0.1*b53","0.1*b54",0,0, + 0,0,"0.1*b63","0.1*b64",0,0), + type = "Full", + nrow = 6, + ncol = 6, + byrow = TRUE, + name = "A", + dimnames = list(varnames,varnames)) > S <- create.mxMatrix( + c(1, + ".1*p21",1, + ".1*p31",".1*p32",1, + 0,0,0,"1*p44", + 0,0,0,0,"1*p55", + 0,0,0,0,0,"1*p66"), + type="Symm", byrow = TRUE, name="S", + dimnames = list(varnames,varnames)) > stage2 <- tssem2(stage1random, Amatrix=A, Smatrix=S, + diag.constraints=TRUE, intervals="LB") 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.