Alternative visualisation to forest plot for large dataset of effect sizes in R I have a data set of ~700 studies with response variables. Currently to view a forest plot of the data looks like .
If I use forest(dat$yi[1:100], dat$vi[1:100]) I get .
I think the second output looks great, there is an opportunity to visualise the effect, but this is lost if I use the full dataset.
Any advice from the brains trust on alternative ways to display this information in a digestible way without sacrificing samples?
 A: This question raises the issue of why we use forest plots. What do they tell us? In general they display (a) the identity of the study, (b) the corresponding estimate, (c) the precision of that estimate, and (d) optionally, by using the order vertically the effect of a proposed moderator.
If we are willing to sacrifice the identity then we can plot the estimate against the precision with the choice of symbols representing the values of a categorical moderator if any. This is usually called a funnel plot and has the advantage that it will be familiar to the reader. Doing this for a continuous moderator is not viable so that would have to be sacrificed. For a large number of studies there would be over-printing so hollow symbols would be best or even using hexagonal binning.
If we have a continuous moderator we could plot the estimates against the value of the moderator with the size of the symbols proportional to the precision of the estimates. Again hollow symbols would work best here and hexagonal binning would not be an answer.
If the desire is to spot studies which might differ in some way from the others then calculating a suitable diagnostic, perhaps a leave-one-out statistic, and using it as the moderator would distinguish the influential or anomalous studies more readily than the standard forest plot.
