R version 3.5.1 (2018-07-02) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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[Workspace loaded from ~/R/RData/.RData]

Loading required package: OpenMx
Loading required package: semPlot
> require("metaSEM")
ÇÊ¿äÇÑ ÆÐÅ°Áö¸¦ ·ÎµùÁßÀÔ´Ï´Ù: metaSEM
"SLSQP" is set as the default optimizer in OpenMx.
mxOption(NULL, "Gradient algorithm") is set at "central".
mxOption(NULL, "Optimality tolerance") is set at "6.3e-14".
mxOption(NULL, "Gradient iterations") is set at "2".
> library(readxl)
> dataset <- read_excel("c:/users/use/Documents/R/paper6_2 data/masem_data_ass.xlsx")
Error: `path` does not exist: ¡®c:/users/use/Documents/R/paper6_2 data/masem_data_ass.xlsx¡¯
> dataset <- read_excel("c:/users/use/Documents/R/paper6_2 data/masem_data_add.xlsx")
> head(dataset)
# A tibble: 6 x 31
  study     N    A1a    A1b   A1c    A1d     A1e   A1f    A1g    A2a   A2b   A2c       A2d   A2e     A2f
  <chr> <dbl>  <dbl>  <dbl> <dbl>  <dbl>   <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>     <dbl> <dbl>   <dbl>
1 °­¸¸¿µ ~   204 NA      0.717  0.66  0.506 NA         NA NA     NA        NA    NA  NA          NA NA     
2 °­¼®¹Î ~   253 NA     NA     NA    NA     NA         NA NA     NA        NA    NA  NA          NA NA     
3 °í¿µ±Ç ~   374 NA     NA     NA    NA     NA         NA NA     NA        NA    NA  NA          NA NA     
4 ±Ç¿µ°ü ~   491  0.022  0.596 NA    NA      0.0124    NA  0.076  0.504    NA    NA  -0.00607    NA  0.0150
5 ±è±¤¿­ ~   142 NA     NA     NA     0.332 NA         NA NA     NA        NA    NA  NA          NA NA     
6 ±èµµ¿µ ~   332 NA     NA     NA    NA     NA         NA NA     NA        NA    NA  NA          NA NA     
# ... with 16 more variables: A4a <dbl>, A4b <dbl>, A4c <dbl>, A4d <dbl>, A4e <dbl>, A5a <dbl>, A5b <dbl>,
#   A5c <dbl>, A5d <dbl>, A6a <dbl>, A6b <dbl>, A6c <dbl>, B1a <dbl>, B1b <dbl>, C1a <dbl>, DAT <lgl>
> ## make list of cormatrices (cordat), NA on diagonal
> nvar <- 8
> varnames <- c("RND","CEO","ABS","COM","TEC","ENV","REL","PER")
> labels <- list(varnames, varnames)
> cordat <- list()
> for (i in 1:nrow(dataset)){	
+   cordat[[i]] <- vec2symMat(as.matrix(dataset[i,3:30]),diag = FALSE)
+   dimnames(cordat[[i]]) <- labels
+ }
> for (i in 1:length(cordat)){
+   for (j in 1:nrow(cordat[[i]])){	
+     if (sum(is.na(cordat[[i]][j,]))==nvar-1) 
+     {cordat[[i]][j,j] <- NA}
+   }}
> for (i in 1:length(cordat)){
+   for (j in 1:nrow(cordat[[i]])){
+     for (k in 1:nvar){				
+       if (is.na(cordat[[i]][j,k])==TRUE
+           &is.na(cordat[[i]][j,j])!=TRUE
+           &is.na(cordat[[i]][k,k])!=TRUE){
+         
+         if(sum(is.na(cordat[[i]])[j,])>sum(is.na(cordat[[i]])[k,]))
+         {cordat[[i]][k,k] = NA}
+         if(sum(is.na(cordat[[i]])[j,])<=sum(is.na(cordat[[i]])[k,]))
+         {cordat[[i]][j,j] = NA}	
+       }}}}
> head(cordat)
[[1]]
          RND CEO       ABS       COM       TEC ENV REL       PER
RND        NA  NA 0.7170000 0.6600000 0.5055429  NA  NA        NA
CEO        NA  NA        NA        NA        NA  NA  NA        NA
ABS 0.7170000  NA        NA        NA 0.5621258  NA  NA        NA
COM 0.6600000  NA        NA        NA 0.7195926  NA  NA 0.6346937
TEC 0.5055429  NA 0.5621258 0.7195926 1.0000000  NA  NA 0.5157708
ENV        NA  NA        NA        NA        NA  NA  NA        NA
REL        NA  NA        NA        NA        NA  NA  NA        NA
PER        NA  NA        NA 0.6346937 0.5157708  NA  NA 1.0000000

[[2]]
    RND CEO ABS COM      TEC      ENV REL      PER
RND  NA  NA  NA  NA       NA       NA  NA       NA
CEO  NA  NA  NA  NA       NA       NA  NA       NA
ABS  NA  NA  NA  NA       NA       NA  NA       NA
COM  NA  NA  NA  NA       NA       NA  NA       NA
TEC  NA  NA  NA  NA       NA 0.357322  NA       NA
ENV  NA  NA  NA  NA 0.357322 1.000000  NA 0.357322
REL  NA  NA  NA  NA       NA       NA  NA       NA
PER  NA  NA  NA  NA       NA 0.357322  NA 1.000000

[[3]]
    RND CEO ABS       COM   TEC ENV   REL       PER
RND  NA  NA  NA        NA    NA  NA    NA        NA
CEO  NA  NA  NA        NA    NA  NA    NA        NA
ABS  NA  NA  NA        NA    NA  NA    NA        NA
COM  NA  NA  NA        NA    NA  NA 0.451 0.3250712
TEC  NA  NA  NA        NA    NA  NA    NA 0.4480000
ENV  NA  NA  NA        NA    NA  NA    NA        NA
REL  NA  NA  NA 0.4510000    NA  NA    NA        NA
PER  NA  NA  NA 0.3250712 0.448  NA    NA        NA

[[4]]
           RND          CEO        ABS COM TEC          ENV REL        PER
RND 1.00000000  0.022000000 0.59571198  NA  NA  0.012417831  NA 0.07600000
CEO 0.02200000  1.000000000 0.50400068  NA  NA -0.006065314  NA 0.01500000
ABS 0.59571198  0.504000676 1.00000000  NA  NA  0.007487570  NA 0.02553593
COM         NA           NA         NA  NA  NA           NA  NA         NA
TEC         NA           NA         NA  NA  NA           NA  NA         NA
ENV 0.01241783 -0.006065314 0.00748757  NA  NA  1.000000000  NA 0.15190935
REL         NA           NA         NA  NA  NA           NA  NA         NA
PER 0.07600000  0.015000000 0.02553593  NA  NA  0.151909350  NA 1.00000000

[[5]]
      RND CEO       ABS       COM       TEC ENV REL       PER
RND    NA  NA        NA        NA 0.3320000  NA  NA        NA
CEO    NA  NA        NA        NA        NA  NA  NA        NA
ABS    NA  NA        NA        NA        NA  NA  NA 0.1080283
COM    NA  NA        NA        NA 0.3518115  NA  NA 0.2764815
TEC 0.332  NA        NA 0.3518115 1.0000000  NA  NA 0.1835258
ENV    NA  NA        NA        NA        NA  NA  NA        NA
REL    NA  NA        NA        NA        NA  NA  NA        NA
PER    NA  NA 0.1080283 0.2764815 0.1835258  NA  NA 1.0000000

[[6]]
    RND CEO ABS COM       TEC ENV REL       PER
RND  NA  NA  NA  NA        NA  NA  NA        NA
CEO  NA  NA  NA  NA        NA  NA  NA        NA
ABS  NA  NA  NA  NA        NA  NA  NA        NA
COM  NA  NA  NA  NA        NA  NA  NA        NA
TEC  NA  NA  NA  NA 1.0000000  NA  NA 0.1275144
ENV  NA  NA  NA  NA        NA  NA  NA        NA
REL  NA  NA  NA  NA        NA  NA  NA        NA
PER  NA  NA  NA  NA 0.1275144  NA  NA 1.0000000

> dataset$N
 [1]  204  253  374  491  142  332   88  235  170  254  160  222  182 2200  154  105   72  151  157  119
[21]   97  300  143  214  242  253  133  133  346  309  140   33  242  150  125  305  248  238  111   23
[41]   87  183  144  558  118  567  333  111  228  421  120  156  142  140  240  125  426  919  100  288
[61]  215 2200  104   89  114  127  149  161  102  109  103  173
> #### stage 1 
> stage1random <- tssem1(Cov=cordat, n=dataset$N, method="REM", RE.type="Diag")
> # pattern of corelation matrices
> pattern.na(cordat,show.na=FALSE)
    RND CEO ABS COM TEC ENV REL PER
RND   8  12  14  16  24  14  13  37
CEO  12  10   9  10   9  11  11  28
ABS  14   9   8  16   7   7  13  28
COM  16  10  16  22  25  10  15  46
TEC  24   9   7  25  20  14   9  35
ENV  14  11   7  10  14  19  10  30
REL  13  11  13  15   9  10  22  34
PER  37  28  28  46  35  30  34  64
> 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   1.8423e-01  7.7637e-02  3.2068e-02  3.3640e-01  2.3730 0.0176435 *  
Intercept2   5.9986e-01  4.4720e-02  5.1220e-01  6.8751e-01 13.4135 < 2.2e-16 ***
Intercept3   3.3504e-01  1.0867e-01  1.2205e-01  5.4804e-01  3.0830 0.0020492 ** 
Intercept4   7.0876e-02  7.3350e-02 -7.2886e-02  2.1464e-01  0.9663 0.3339043    
Intercept5   5.5658e-02  3.6226e-02 -1.5344e-02  1.2666e-01  1.5364 0.1244403    
Intercept6   4.0767e-01  6.4432e-02  2.8139e-01  5.3395e-01  6.3272 2.497e-10 ***
Intercept7   2.1481e-01  7.5164e-02  6.7488e-02  3.6213e-01  2.8578 0.0042653 ** 
Intercept8   5.2241e-01  2.9403e-02  4.6479e-01  5.8004e-01 17.7674 < 2.2e-16 ***
Intercept9   3.8163e-01  8.3283e-02  2.1840e-01  5.4486e-01  4.5823 4.599e-06 ***
Intercept10  3.9044e-01  4.9390e-02  2.9363e-01  4.8724e-01  7.9051 2.665e-15 ***
Intercept11  1.0524e-01  5.2309e-02  2.7128e-03  2.0776e-01  2.0118 0.0442384 *  
Intercept12  4.4422e-01  5.5427e-02  3.3559e-01  5.5286e-01  8.0145 1.110e-15 ***
Intercept13  3.6202e-01  7.4149e-02  2.1669e-01  5.0735e-01  4.8823 1.049e-06 ***
Intercept14  5.0376e-01  3.7683e-02  4.2990e-01  5.7762e-01 13.3685 < 2.2e-16 ***
Intercept15  2.9187e-01  9.0790e-02  1.1392e-01  4.6981e-01  3.2148 0.0013056 ** 
Intercept16  5.6119e-03  3.9700e-02 -7.2198e-02  8.3422e-02  0.1414 0.8875861    
Intercept17  4.1796e-01  4.7962e-02  3.2396e-01  5.1197e-01  8.7143 < 2.2e-16 ***
Intercept18  3.1251e-01  7.0806e-02  1.7374e-01  4.5129e-01  4.4137 1.016e-05 ***
Intercept19  2.7792e-02  9.3575e-02 -1.5561e-01  2.1120e-01  0.2970 0.7664608    
Intercept20  4.0095e-01  1.0551e-01  1.9415e-01  6.0775e-01  3.8001 0.0001447 ***
Intercept21  5.0458e-01  6.3556e-02  3.8002e-01  6.2915e-01  7.9392 1.998e-15 ***
Intercept22  3.8785e-01  4.4992e-02  2.9967e-01  4.7604e-01  8.6205 < 2.2e-16 ***
Intercept23  2.6076e-01  6.0796e-02  1.4161e-01  3.7992e-01  4.2891 1.794e-05 ***
Intercept24  4.4497e-01  7.0754e-02  3.0629e-01  5.8364e-01  6.2889 3.197e-10 ***
Intercept25  2.8390e-01  5.6462e-02  1.7324e-01  3.9457e-01  5.0282 4.950e-07 ***
Intercept26  1.8376e-01  1.1638e-01 -4.4334e-02  4.1186e-01  1.5790 0.1143314    
Intercept27  2.1968e-01  4.0949e-02  1.3942e-01  2.9994e-01  5.3647 8.109e-08 ***
Intercept28  3.9646e-01  3.5137e-02  3.2760e-01  4.6533e-01 11.2833 < 2.2e-16 ***
Tau2_1_1     1.5222e-02  1.4949e-02 -1.4078e-02  4.4522e-02  1.0183 0.3085523    
Tau2_2_2     2.2690e-10          NA          NA          NA      NA        NA    
Tau2_3_3     3.1510e-02  2.8281e-02 -2.3919e-02  8.6940e-02  1.1142 0.2651963    
Tau2_4_4     1.4711e-10          NA          NA          NA      NA        NA    
Tau2_5_5     1.0733e-03  3.0008e-03 -4.8082e-03  6.9548e-03  0.3577 0.7205952    
Tau2_6_6     7.8484e-03  1.0501e-02 -1.2734e-02  2.8430e-02  0.7474 0.4548353    
Tau2_7_7     3.6031e-02  2.1293e-02 -5.7020e-03  7.7763e-02  1.6922 0.0906135 .  
Tau2_8_8     3.0963e-10          NA          NA          NA      NA        NA    
Tau2_9_9     1.7719e-02  1.6664e-02 -1.4941e-02  5.0379e-02  1.0633 0.2876299    
Tau2_10_10   1.5596e-10          NA          NA          NA      NA        NA    
Tau2_11_11   5.3670e-03  6.7164e-03 -7.7969e-03  1.8531e-02  0.7991 0.4242395    
Tau2_12_12   5.9641e-03  9.7188e-03 -1.3084e-02  2.5013e-02  0.6137 0.5394319    
Tau2_13_13   4.5418e-02  2.3024e-02  2.9128e-04  9.0544e-02  1.9726 0.0485394 *  
Tau2_14_14   1.4952e-10          NA          NA          NA      NA        NA    
Tau2_15_15   1.3079e-10          NA          NA          NA      NA        NA    
Tau2_16_16   2.4687e-10          NA          NA          NA      NA        NA    
Tau2_17_17   2.0953e-10          NA          NA          NA      NA        NA    
Tau2_18_18   3.0527e-02  1.7877e-02 -4.5115e-03  6.5565e-02  1.7076 0.0877104 .  
Tau2_19_19   1.2873e-10          NA          NA          NA      NA        NA    
Tau2_20_20   4.0284e-02  3.0565e-02 -1.9623e-02  1.0019e-01  1.3180 0.1875152    
Tau2_21_21   1.8544e-02  1.3859e-02 -8.6196e-03  4.5708e-02  1.3380 0.1808866    
Tau2_22_22   3.2106e-02  1.2496e-02  7.6145e-03  5.6598e-02  2.5693 0.0101899 *  
Tau2_23_23   8.2544e-03  1.2116e-02 -1.5492e-02  3.2001e-02  0.6813 0.4956894    
Tau2_24_24   4.8065e-03  1.1740e-02 -1.8203e-02  2.7816e-02  0.4094 0.6822329    
Tau2_25_25   4.6497e-02  1.8607e-02  1.0027e-02  8.2967e-02  2.4989 0.0124595 *  
Tau2_26_26   3.2830e-02  3.2842e-02 -3.1539e-02  9.7199e-02  0.9996 0.3174832    
Tau2_27_27   2.4373e-02  1.0071e-02  4.6349e-03  4.4112e-02  2.4202 0.0155124 *  
Tau2_28_28   1.9397e-02  7.3514e-03  4.9886e-03  3.3805e-02  2.6386 0.0083258 ** 
---
Signif. codes:  0 ¡®***¡¯ 0.001 ¡®**¡¯ 0.01 ¡®*¡¯ 0.05 ¡®.¡¯ 0.1 ¡® ¡¯ 1

Q statistic on the homogeneity of effect sizes: 1008.888
Degrees of freedom of the Q statistic: 129
P value of the Q statistic: 0

Heterogeneity indices (based on the estimated Tau2):
                              Estimate
Intercept1: I2 (Q statistic)    0.7959
Intercept2: I2 (Q statistic)    0.0000
Intercept3: I2 (Q statistic)    0.8907
Intercept4: I2 (Q statistic)    0.0000
Intercept5: I2 (Q statistic)    0.2161
Intercept6: I2 (Q statistic)    0.6683
Intercept7: I2 (Q statistic)    0.9039
Intercept8: I2 (Q statistic)    0.0000
Intercept9: I2 (Q statistic)    0.8196
Intercept10: I2 (Q statistic)   0.0000
Intercept11: I2 (Q statistic)   0.5789
Intercept12: I2 (Q statistic)   0.6047
Intercept13: I2 (Q statistic)   0.9223
Intercept14: I2 (Q statistic)   0.0000
Intercept15: I2 (Q statistic)   0.0000
Intercept16: I2 (Q statistic)   0.0000
Intercept17: I2 (Q statistic)   0.0000
Intercept18: I2 (Q statistic)   0.8901
Intercept19: I2 (Q statistic)   0.0000
Intercept20: I2 (Q statistic)   0.9118
Intercept21: I2 (Q statistic)   0.8274
Intercept22: I2 (Q statistic)   0.9008
Intercept23: I2 (Q statistic)   0.6796
Intercept24: I2 (Q statistic)   0.5519
Intercept25: I2 (Q statistic)   0.9239
Intercept26: I2 (Q statistic)   0.8938
Intercept27: I2 (Q statistic)   0.8641
Intercept28: I2 (Q statistic)   0.8376

Number of studies (or clusters): 72
Number of observed statistics: 157
Number of estimated parameters: 56
Degrees of freedom: 101
-2 log likelihood: -147.526 
OpenMx status1: 6 ("0" or "1": The optimization is considered fine.
Other values may indicate problems.)
>