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Type 'q()' to quit R. > ## library(metaSEM) > > ## ## Clear all objects > ## rm(list=ls()) > > ## ## Set seed for replication > ## set.seed(1000000) > > ## ## Copy Bornmann07 to my.df > ## my.df <- Bornmann07 > ## ## "Fellowship": 1; "Grant": 0 > ## my.df$Type_MCAR <- ifelse(Bornmann07$Type=="Fellowship", yes=1, no=0) > > ## ## Create 17 out of 66 missingness with MCAR > ## my.df$Type_MCAR[sample(1:66, 17)] <- NA > > ## metaSEM.fit <- meta3X(y=logOR, v=v, cluster=Cluster, x2=Type_MCAR, data=my.df) > > ## ## Get the OpenMx model > ## OpenMx.fit <- metaSEM.fit$mx.fit > > ## save(metaSEM.fit, OpenMx.fit, my.df, file="test5a.txt") > > ## Version "1.4-3532" > library(OpenMx) > > sessionInfo() R version 3.1.0 (2014-04-10) Platform: i686-pc-linux-gnu (32-bit) locale: [1] LC_CTYPE=en_SG.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_SG.UTF-8 LC_COLLATE=en_SG.UTF-8 [5] LC_MONETARY=en_SG.UTF-8 LC_MESSAGES=en_SG.UTF-8 [7] LC_PAPER=en_SG.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_SG.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] OpenMx_1.4-3532 > > ## Clear all objects > rm(list=ls()) > > ## Load the workspace > load("test5a.txt") > > summary(OpenMx.fit) data: $`Meta analysis with ML.data` y_1 v_1 x2_1_1 y_2 Min. :-1.42885 Min. :0.0009904 Min. :0.0000 Min. :-0.87855 1st Qu.:-0.40108 1st Qu.:0.0139169 1st Qu.:0.0000 1st Qu.:-0.22889 Median :-0.07833 Median :0.0438567 Median :1.0000 Median :-0.09825 Mean :-0.16197 Mean :0.1361091 Mean :0.5294 Mean :-0.13718 3rd Qu.: 0.08573 3rd Qu.:0.1470953 3rd Qu.:1.0000 3rd Qu.:-0.02447 Max. : 0.71562 Max. :0.6091334 Max. :1.0000 Max. : 0.48551 NA's :4 NA's :9 v_2 x2_1_2 y_3 v_3 Min. :0.000e+00 Min. :0.0 Min. :-0.89097 Min. :0.000e+00 1st Qu.:0.000e+00 1st Qu.:0.0 1st Qu.:-0.29172 1st Qu.:0.000e+00 Median :0.000e+00 Median :0.0 Median :-0.11820 Median :1.000e+10 Mean :4.286e+09 Mean :0.4 Mean :-0.01582 Mean :5.238e+09 3rd Qu.:1.000e+10 3rd Qu.:1.0 3rd Qu.: 0.10200 3rd Qu.:1.000e+10 Max. :1.000e+10 Max. :1.0 Max. : 1.50539 Max. :1.000e+10 NA's :11 NA's :11 x2_1_3 y_4 v_4 x2_1_4 Min. :0.0 Min. :-1.48881 Min. :0.000e+00 Min. :0.0 1st Qu.:0.0 1st Qu.:-0.70225 1st Qu.:0.000e+00 1st Qu.:0.0 Median :0.5 Median :-0.25672 Median :1.000e+10 Median :1.0 Mean :0.5 Mean :-0.39430 Mean :6.667e+09 Mean :0.6 3rd Qu.:1.0 3rd Qu.: 0.01451 3rd Qu.:1.000e+10 3rd Qu.:1.0 Max. :1.0 Max. : 0.36094 Max. :1.000e+10 Max. :1.0 NA's :15 NA's :14 NA's :16 y_5 v_5 x2_1_5 y_6 Min. :-0.42900 Min. :0.000e+00 Min. :0.00 Min. :-0.5521 1st Qu.:-0.33336 1st Qu.:1.000e+10 1st Qu.:0.00 1st Qu.:-0.1918 Median :-0.15985 Median :1.000e+10 Median :0.00 Median :-0.0315 Mean :-0.18859 Mean :7.619e+09 Mean :0.25 Mean :-0.1251 3rd Qu.:-0.09465 3rd Qu.:1.000e+10 3rd Qu.:0.25 3rd Qu.: 0.0352 Max. : 0.07390 Max. :1.000e+10 Max. :1.00 Max. : 0.1146 NA's :16 NA's :17 NA's :17 v_6 x2_1_6 y_7 v_7 Min. :0.000e+00 Min. :0.0000 Min. :-1.11300 Min. :0.000e+00 1st Qu.:1.000e+10 1st Qu.:0.0000 1st Qu.:-0.40647 1st Qu.:1.000e+10 Median :1.000e+10 Median :0.0000 Median :-0.16945 Median :1.000e+10 Mean :8.095e+09 Mean :0.3333 Mean :-0.34680 Mean :8.095e+09 3rd Qu.:1.000e+10 3rd Qu.:0.5000 3rd Qu.:-0.10978 3rd Qu.:1.000e+10 Max. :1.000e+10 Max. :1.0000 Max. : 0.06467 Max. :1.000e+10 NA's :18 NA's :17 x2_1_7 y_8 v_8 x2_1_8 Min. :0 Min. :0.02763 Min. :0.000e+00 Min. :0 1st Qu.:0 1st Qu.:0.03886 1st Qu.:1.000e+10 1st Qu.:0 Median :0 Median :0.05010 Median :1.000e+10 Median :0 Mean :0 Mean :0.05010 Mean :9.048e+09 Mean :0 3rd Qu.:0 3rd Qu.:0.06134 3rd Qu.:1.000e+10 3rd Qu.:0 Max. :0 Max. :0.07257 Max. :1.000e+10 Max. :0 NA's :19 NA's :19 NA's :19 y_9 v_9 x2_1_9 Min. :0.6615 Min. :0.000e+00 Min. : NA 1st Qu.:0.6615 1st Qu.:1.000e+10 1st Qu.: NA Median :0.6615 Median :1.000e+10 Median : NA Mean :0.6615 Mean :9.524e+09 Mean :NaN 3rd Qu.:0.6615 3rd Qu.:1.000e+10 3rd Qu.: NA Max. :0.6615 Max. :1.000e+10 Max. : NA NA's :20 NA's :21 free parameters: name matrix row col Estimate Std.Error lbound ubound 1 Intercept Inter 1 1 -0.010631758 0.039768517 2 Mean_x2_1 intercept_x 1 1 0.494435012 0.105147695 3 SlopeX2_1 Beta 1 1 -0.175324900 0.058261912 4 Tau2_2 Tau 1 1 0.003033750 0.002683876 1e-10 5 Tau2_3 Tau 2 2 0.003683869 0.004281732 1e-10 6 Wvarx2_1 covW 2 2 0.040482049 0.010652807 1e-10 7 Bvarx2_1 covB 2 2 0.199353881 0.070158142 1e-10 observed statistics: 115 estimated parameters: 7 degrees of freedom: 108 -2 log likelihood: 49.76372 saturated -2 log likelihood: NA number of observations: 21 chi-square: NA p: NA Information Criteria: df Penalty Parameters Penalty Sample-Size Adjusted AIC: -166.2363 63.76372 NA BIC: -279.0447 71.07537 49.4658 CFI: NA TLI: NA RMSEA: NA timestamp: 2014-06-21 11:33:53 frontend time: 0.2262442 secs backend time: 0.3313093 secs independent submodels time: 2.43187e-05 secs wall clock time: 0.5575778 secs cpu time: 0.5575778 secs openmx version number: 1.4-3532 > > proc.time() user system elapsed 0.516 0.020 0.530