This is related to the issue I raised which has been closed (Multiple response variables in meta-analysis. · Issue #1212 · paul-buerkner/brms · GitHub) which Paul has already kindly reviewed. I suggested a feature to allow users to meta-analyse results which were based on correlated outcome (response) variables.
Paul explained that the feature was not needed as the desired model could be obtained by arranging the data into long format and using fcor. However, I have been unable to do get this to work (I read the response to a previous question (Brms: Multivariate meta-analysis syntax) to help with the model specification on the RHS). Can someone please advise where I am going wrong in trying to specify the known standard errors and known correlation?
Commented reproducible example below.
## Dataset in long format. Two trials, two response variables. Effect estimates and standard errors for each combination (4 effect estimates) mydf_lng <- structure(list(trial = c(1L, 1L, 2L, 2L), response = c("1", "2", "1", "2"), y = c(1.405, -1.652, 0.047, -0.169), se = c(0.642,0.697, 0.055, 0.114)), row.names = c(NA, -4L), class = "data.frame") ## Correlation matrix indicating correlation between each effect estimate. Effect estimates within trials are correlated. Those between trials are not. cors <- structure(c(1, -0.921, 0, 0, -0.921, 1, 0, 0, 0, 0, 1, -0.48, 0, 0, -0.48, 1), .Dim = c(4L, 4L), .Dimnames = list(NULL, NULL)) ## Works, specify se but not cors mod1 <- brm(y|se(se) ~ 0 + response + (0 + response | trial) , data = mydf_lng, data2 = list(cors = cors)) ## Works, specify cors but not se mod2 <- brm(y ~ 0 + response + (0 + response | trial) + fcor(cors), data = mydf_lng, data2 = list(cors = cors)) ## Error message "Error: Invalid addition arguments for this model" when try to specify both the se and correlation mod3 <- brm(y|se(se) ~ 0 + response + (0 + response | trial) + fcor(cors), data = mydf_lng, data2 = list(cors = cors))