Help: TSSEM Mediation model

#Load MetaSEM package
library(metaSEM)

# locate studies with information on at least 1 correlation, and no missing sample size
keepstudy <- rowSums(is.na(my.data[,2:37]))!=36 & is.na(my.data$NUMBER.OF.BANKS)==FALSE
# keep only the studies with information
data <- my.data[keepstudy,]

# check data
head(data)

## Study_id X1_X2 X1_X3 X1_X4 X1_X5 X1_X6 X1_X7 X1_X8 X1 X2_X3 X2_X4
## 1 2 0.075 0.1625 NA 0.05 0.1375 NA NA 0.0375 0.4125 NA
## 2 2 0.075 0.1625 NA 0.05 0.1375 NA NA 0.0375 0.4125 NA
## 3 2 0.075 0.1625 NA 0.05 0.1375 NA NA 0.0250 0.4125 NA
## 4 2 0.075 0.1625 NA 0.05 0.1375 NA NA 0.0375 0.4125 NA
## 5 2 0.075 0.1625 NA 0.05 0.1375 NA NA -0.1000 0.4125 NA
## 6 2 0.075 0.1625 NA 0.05 0.1375 NA NA -0.0125 0.4125 NA
## X2_X5 X2_X6 X2_X7 X2_X8 X2 X3_X4 X3_X5 X3_X6 X3_X7 X3_X8 X3 X4_X5
## 1 0.05 0.175 NA NA -0.2250 NA 0.0125 0.225 NA NA -0.1125 NA
## 2 0.05 0.175 NA NA -0.2500 NA 0.0125 0.225 NA NA -0.1750 NA
## 3 0.05 0.175 NA NA -0.2000 NA 0.0125 0.225 NA NA -0.1250 NA
## 4 0.05 0.175 NA NA -0.2625 NA 0.0125 0.225 NA NA -0.1625 NA
## 5 0.05 0.175 NA NA -0.2625 NA 0.0125 0.225 NA NA -0.1875 NA
## 6 0.05 0.175 NA NA -0.1875 NA 0.0125 0.225 NA NA -0.0500 NA
## X4_X6 X4_X7 X4_X8 X4 X5_X6 X5_X7 X5_X8 X5 X6_X7 X6_X8 X6 X7_X8 X7 X8
## 1 NA NA NA NA -0.05 NA NA 0.0750 NA NA -0.0750 NA NA NA
## 2 NA NA NA NA NA NA NA 0.4250 NA NA -0.0125 NA NA NA
## 3 NA NA NA NA NA NA NA 0.2375 NA NA 0.0000 NA NA NA
## 4 NA NA NA NA NA NA NA 0.3250 NA NA -0.0375 NA NA NA
## 5 NA NA NA NA NA NA NA 0.2500 NA NA -0.1625 NA NA NA
## 6 NA NA NA NA NA NA NA 0.0375 NA NA -0.0250 NA NA NA
## BANK.YEAR.OX1 NUMBER.OF.BANKS
## 1 2426 212
## 2 2426 212
## 3 2426 212
## 4 2426 212
## 5 2426 212
## 6 2426 212

length(data)

## [1] 39

## summary(data)

# varnames and labels
nvar <- 9
varnames <- c("X1","X2","X3","X4","X5","X6","X7","X8","X9")
labels <- list(varnames,varnames)
# create list with correlation matrices
cordat <- list()
for (i in 1:nrow(data)){
cordat[[i]] <- vec2symMat(as.matrix(data[i,2:37]),diag = FALSE)
dimnames(cordat[[i]]) <- labels
}
# put NA on diagonal of correlation matrix if variable is missing
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}
}}

# show number of studies per correlation coefficient
pattern.na(cordat, show.na = FALSE)

## X1 X2 X3 X4 X5 X6 X7 X8 X9
## X1 219 173 66 69 147 179 42 78 189
## X2 173 198 51 64 125 150 36 60 168
## X3 66 51 69 25 50 61 17 34 62
## X4 69 64 25 86 55 72 18 28 66
## X5 147 125 50 55 196 154 29 60 163
## X6 179 150 61 72 154 236 40 78 204
## X7 42 36 17 18 29 40 48 9 44
## X8 78 60 34 28 60 78 9 93 81
## X9 189 168 62 66 163 204 44 81 249

# show total N per correlation coefficient
pattern.n(cordat, data$NUMBER.OF.BANKS)

## X1 X2 X3 X4 X5 X6 X7 X8 X9
## X1 20434 13161 11296 6655 15604 18361 2349 10004 16931
## X2 13161 15875 5859 7623 11027 12986 2196 5282 12268
## X3 11296 5859 11543 3248 9824 10696 1013 6478 10129
## X4 6655 7623 3248 29544 12872 27613 1374 22770 26661
## X5 15604 11027 9824 12872 44614 41298 1753 28733 40607
## X6 18361 12986 10696 27613 41298 47652 2338 30099 44030
## X7 2349 2196 1013 1374 1753 2338 2539 667 2160
## X8 10004 5282 6478 22770 28733 30099 667 30881 29667
## X9 16931 12268 10129 26661 40607 44030 2160 29667 45661

cordat <- lapply(cordat, function(x) {diag(x) <- 1; x})

summary(unlist(cordat))