[R-meta] Calculating the var-covar matrix for dependent effect sizes for ROM

[Michael Dewey]

Dear Jonas

I cannot answer your earlier questions but I think you have to roll your 
own qqplot of the residuals for which there is an extractor function.

On 08/11/2017 08:19, Jonas Duus Stevens Lekfeldt wrote:
> Thank you, Wolfgang for the fast and clear reply!
> 
> In an earlier reply by Wolfgang I found the following formula for calculating the covariance for ROM (log response ratio) when controls are shared among some of the datasets (if I have understood it correctly):
> 
> Covariance = sd^2/(n*mean^2), from the group whose data is being re-used.
> 
> I have calculated a new covariance column in the dataset data of the individual effect sizes based on the control data in the following way:
> 
> data <- data %>%
>    dplyr::mutate (covar=((data$sd2i)^2)/(data$n2i*(data$m2i^2))),
> 
> where:
> sd2i is the standard deviation of the control group
> n2i is the sample size of the control group
> m2i is the mean of the control group
> 
> Subsequently I have calculated the variance-covariance matrix (here called VarC) using the following code (again inspired by Wolfgang):
> 
> calc.v.ROM <- function(x) {
>    v <- matrix(x$covar[1],nrow=nrow(x),ncol=nrow(x))
>    diag(v) <- x$vi
>    v
> }
> 
> covar_list <- lapply(split(data,data$ControlName),calc.v.ROM)
> VarC <- bldiag(covar_list)
> 
> 
> Where "ControlName" is the column in "data" where the names of the control groups are stored.
> 
> Using VarC as the argument to V in the following code gives meaningful results so it seems to work, but I would like to ask if it seems correct?
> 
> meta_list <- rma.mv(yi=data$yi,
>                      V=VarC,
>                      random = ~1|ExpName/ControlName/ID,
>                      test="t")
> 
> Another question: drawing a qqnorm-plot does not seem to be implemented for rma.mv(). Is that right?
> 
> 
> Best regards
> 
> Jonas Duus Stevens Lekfeldt
> 
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-- 
Michael
http://www.dewey.myzen.co.uk/home.html