> pattern.na(IA6x6Data, show.na = FALSE) x1 x2 x3 x4 x5 x6 x1 32 5 21 9 10 27 x2 5 32 7 5 4 11 x3 21 7 32 5 6 21 x4 9 5 5 32 4 10 x5 10 4 6 4 32 11 x6 27 11 21 10 11 32 > pattern.n(IA6x6Data, IA6x6N) x1 x2 x3 x4 x5 x6 x1 22604 1064 5112 2546 1937 7043 x2 1064 22604 15592 1515 963 16688 x3 5112 15592 22604 1142 871 19166 x4 2546 1515 1142 22604 1323 2957 x5 1937 963 871 1323 22604 2348 x6 7043 16688 19166 2957 2348 22604 > summary(random1) 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 0.4593925 0.0599080 0.3419750 0.5768100 7.6683 1.732e-14 *** Intercept2 0.5339698 0.0336099 0.4680956 0.5998439 15.8873 < 2.2e-16 *** Intercept3 0.6207772 0.0282623 0.5653842 0.6761702 21.9649 < 2.2e-16 *** Intercept4 0.5349957 0.0370266 0.4624249 0.6075666 14.4490 < 2.2e-16 *** Intercept5 0.5042071 0.0256351 0.4539632 0.5544509 19.6686 < 2.2e-16 *** Intercept6 0.3830093 0.0655644 0.2545055 0.5115132 5.8417 5.166e-09 *** Intercept7 0.5466412 0.0664818 0.4163393 0.6769430 8.2224 2.220e-16 *** Intercept8 0.4379985 0.0969759 0.2479293 0.6280677 4.5166 6.285e-06 *** Intercept9 0.4139781 0.0656236 0.2853582 0.5425979 6.3084 2.820e-10 *** Intercept10 0.6395690 0.0404746 0.5602403 0.7188977 15.8018 < 2.2e-16 *** Intercept11 0.4474121 0.0453716 0.3584854 0.5363388 9.8611 < 2.2e-16 *** Intercept12 0.4533202 0.0372505 0.3803104 0.5263299 12.1695 < 2.2e-16 *** Intercept13 0.6284656 0.0776408 0.4762925 0.7806387 8.0945 6.661e-16 *** Intercept14 0.6391970 0.0302072 0.5799919 0.6984021 21.1604 < 2.2e-16 *** Intercept15 0.6679005 0.0368541 0.5956678 0.7401331 18.1228 < 2.2e-16 *** Tau2_1_1 0.0122673 0.0104308 -0.0081768 0.0327113 1.1761 0.239572 Tau2_2_2 0.0199364 0.0070671 0.0060852 0.0337877 2.8210 0.004787 ** Tau2_3_3 0.0033942 0.0030225 -0.0025299 0.0093182 1.1230 0.261454 Tau2_4_4 0.0079525 0.0057335 -0.0032850 0.0191899 1.3870 0.165436 Tau2_5_5 0.0146945 0.0046604 0.0055603 0.0238288 3.1531 0.001616 ** Tau2_6_6 0.0255531 0.0149827 -0.0038124 0.0549186 1.7055 0.088100 . Tau2_7_7 0.0182786 0.0136485 -0.0084720 0.0450292 1.3392 0.180494 Tau2_8_8 0.0316741 0.0254318 -0.0181713 0.0815195 1.2455 0.212965 Tau2_9_9 0.0431249 0.0198183 0.0042818 0.0819680 2.1760 0.029554 * Tau2_10_10 0.0032741 0.0039164 -0.0044018 0.0109500 0.8360 0.403154 Tau2_11_11 0.0054109 0.0065997 -0.0075243 0.0183461 0.8199 0.412289 Tau2_12_12 0.0253719 0.0084920 0.0087278 0.0420160 2.9877 0.002811 ** Tau2_13_13 0.0209159 0.0173879 -0.0131637 0.0549954 1.2029 0.229015 Tau2_14_14 0.0056360 0.0043318 -0.0028541 0.0141262 1.3011 0.193230 Tau2_15_15 0.0099007 0.0059469 -0.0017550 0.0215563 1.6649 0.095941 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Q statistic on the homogeneity of effect sizes: 1914.822 Degrees of freedom of the Q statistic: 141 P value of the Q statistic: 0 Heterogeneity indices (based on the estimated Tau2): Estimate Intercept1: I2 (Q statistic) 0.8497 Intercept2: I2 (Q statistic) 0.9222 Intercept3: I2 (Q statistic) 0.6217 Intercept4: I2 (Q statistic) 0.7926 Intercept5: I2 (Q statistic) 0.9090 Intercept6: I2 (Q statistic) 0.9221 Intercept7: I2 (Q statistic) 0.8942 Intercept8: I2 (Q statistic) 0.9356 Intercept9: I2 (Q statistic) 0.9536 Intercept10: I2 (Q statistic) 0.6038 Intercept11: I2 (Q statistic) 0.7141 Intercept12: I2 (Q statistic) 0.9334 Intercept13: I2 (Q statistic) 0.9061 Intercept14: I2 (Q statistic) 0.7358 Intercept15: I2 (Q statistic) 0.8336 Number of studies (or clusters): 32 Number of observed statistics: 156 Number of estimated parameters: 30 Degrees of freedom: 126 -2 log likelihood: -173.3952 OpenMx status1: 0 ("0" or "1": The optimization is considered fine. Other values may indicate problems.) > summary(random2) Call: wls(Cov = pooledS, aCov = aCov, n = tssem1.obj$total.n, RAM = RAM, Amatrix = Amatrix, Smatrix = Smatrix, Fmatrix = Fmatrix, diag.constraints = diag.constraints, cor.analysis = cor.analysis, intervals.type = intervals.type, mx.algebras = mx.algebras, model.name = model.name, suppressWarnings = suppressWarnings, silent = silent, run = run) 95% confidence intervals: Likelihood-based statistic Coefficients: Estimate Std.Error lbound ubound z value Pr(>|z|) SA2IC 0.498270 NA 0.368037 0.628510 NA NA Ql2IH 0.507851 NA 0.435920 0.579795 NA NA RC2IH 0.337110 NA 0.278794 0.406290 NA NA Ql2IA 0.296286 NA 0.198201 0.392845 NA NA SA2IA 0.164441 NA -0.032843 0.340850 NA NA IC2IA 0.407437 NA 0.289345 0.532021 NA NA IH2IA 0.357549 NA 0.216511 0.483235 NA NA ErrVarIC 0.751727 NA 0.604977 0.864549 NA NA ErrVarIH 0.628444 NA 0.530445 0.710485 NA NA ErrVarIA 0.222050 NA 0.116169 0.315331 NA NA mxAlgebras objects (and their 95% likelihood-based CIs): lbound Estimate ubound IndirectQlIH[1,1] 0.11237797 0.1815816 0.2545042 IndirectRCIH[1,1] 0.08361247 0.1205334 0.1516379 IndirectSAIC[1,1] 0.13841439 0.2030139 0.3085896 CIndirectIH[1,1] 0.19730587 0.3021150 0.3999934 Goodness-of-fit indices: Value Sample size 22604.0000 Chi-square of target model 1039.6711 DF of target model 8.0000 p value of target model 0.0000 Number of constraints imposed on "Smatrix" 3.0000 DF manually adjusted 0.0000 Chi-square of independence model 2271.8034 DF of independence model 15.0000 RMSEA 0.0755 RMSEA lower 95% CI 0.0717 RMSEA upper 95% CI 0.0794 SRMR 0.3712 TLI 0.1429 CFI 0.5429 AIC 1023.6711 BIC 959.4640 OpenMx status1: 0 ("0" or "1": The optimization is considered fine. Other values indicate problems.)