Storing meta-analysis results using for loop - r

Question

I am trying to conduct a meta analysis of a group of studies. I am trying to figure out the code to properly output what I would like. Essentially I am trying to run a fixed effects meta-analysis test using the metafor package and collect the coefficient estimates to store them inside a matrix.
I have several problems. For example, I want to run these tests only for research that have multiple results. So studies whose study numbers turn up more than once (see code for example). When I try this using my existing code, it does not work, it spits out a number, but it isn't the right one. Also, some study-numbers are larger than the actual amount of studies I have. In my personal dataset there is a study numbered 3500 for example. When I run my loop, R spits out the results for that particular fixed effects model on the 3500th row, instead of just placing it in the next empty row.
I have a basic example below that anyone can run in R.
library(metafor)
origdata <- data.frame(matrix(data=NA, nrow=15, ncol=3))
colnames(origdata) <- c("studynum", "Mail_b", "Mail_SE")
origdata$studynum <- c(1, 1, 1, 1, 1, 2, 3, 3, 3, 4, 7, 7, 7, 7, 7)
origdata$Mail_b <- c(1.8, 0.8, 1.2, 1, 1, 5, 3, 3, 6, 4, 5, 8, 5, 9, 2)
origdata$Mail_SE <- c(1.6, 0.8, 1.3, 1, 1, 1, 3, 2.9, 6, 4, 5, 8, 5, 8, 1)
collapsedtest <- data.frame(matrix(data=NA, nrow=5, ncol=3))
colnames(collapsedtest) <- c("studynum", "Meta_b", "Meta_SE")
collapsedtest$studynum <- unique(origdata$studynum)
for(i in unique(origdata$studynum)) {
if((table(origdata$studynum) == 1) == FALSE){
collapsedtest[i, 2] <- (coef(summary(rma(yi=Mail_b[origdata$studynum == i],
sei=Mail_SE[origdata$studynum == i],
method="FE",
data=origdata)))$estimate)
collapsedtest[i, 3] <- (coef(summary(rma(yi=Mail_b[origdata$studynum == i ],
sei=Mail_SE[origdata$studynum == i],
method="FE", data=origdata)))$ci.ub
-
(coef(summary(rma(yi=Mail_b[origdata$studynum == i],
sei=Mail_SE[origdata$studynum == i],
method="FE",
data=origdata)))$estimate)) / 1.96
} else {
collapsed[i, 2] <- origdata$Mail_b[origdata$studynum == i]
collapsed[i, 3] <- origdata$Mail_SE[origdata$studynum == i]
}
}

Answer 1

Consider using nrow and adjust your row indexer with a conditional on studynum. Also, be sure to run your rma method only once and then use resulting estimates.
for(i in unique(origdata$studynum)) {
coef_data <- coef(summary(rma(yi=Mail_b[origdata$studynum == i],
sei=Mail_SE[origdata$studynum == i],
method="FE",
data=origdata)))
if(nrow(origdata[origdata$studynum==i,]) > 1) {
collapsedtest[collapsedtest$studynum==i, 2] <- coef_data$estimate
collapsedtest[collapsedtest$studynum==i, 3] <- (coef_data$$ci.ub - coef_data$estimate) / 1.96
collapsedtest[collapsedtest$studynum==i, 2] <- origdata$Mail_b[origdata$studynum == i]
collapsedtest[collapsedtest$studynum==i, 3] <- origdata$Mail_SE[origdata$studynum == i]
}
}
However, you can avoid the studynum filters by using by, the method that subsets a dataframe by factor(s) and runs subsets into an operation. Then after processing, row bind all subsetted dataframes with do.call:
df_list <- by(origdata, origdata$studynum, function(sub){
coef_data <- coef(summary(rma(yi=Mail_b, sei=Mail_SE, method="FE", data=origdata)))
if(nrow(sub) > 1) {
df <- data.frame(studynum = sub$studynum[[1]],
Meta_b = coef_data$estimate,
Meta_SE = (coef_data$$ci.ub - coef_data$estimate) / 1.96
} else {
df <- data.frame(studynum = sub$studynum[[1]],
Meta_b = sub$Mail_b,
Meta_SE = sub$Mail_SE)
}
return(df)
})
collapsedtest <- do.call(rbind, df_list)