Solved – Funnel plot with asymmetrical point sizes

data visualizationfunnel-plotmeta-analysisr

Lets say I want to take the usual funnel plot for meta-analysis, and add another dimension to it, visually changing the points used for each study by a covariate. While it might be easier to change the marker or color for categorical variables, for a continuous variable, this gets a little harder.

Lets say we want to see if there's obvious asymmetry not just overall, but by say, length of follow-up. Does anyone know a way in either R or Stata to either change the size of the points plotted – so you essentially have a funnel-bubble plot, or to color them on a continuous gradient?

Clearly, this can be done by just modifying a scatterplot, but I'd like to save the steps in manipulating the access to get it looking like a conventional funnel plot.

Best Answer

You can do that with the funnel() function from the metafor package. Here is an example:

library(metafor)

data(dat.bcg)
res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg, measure="RR", method="REML")

ablat.scaled <- with(dat.bcg, (ablat - min(ablat))/(max(ablat) - min(ablat)))
ablat.scaled <- ablat.scaled * 2 + 0.5

funnel(res, cex=ablat.scaled)

Resulting figure shown below. Adapt to your taste.

funnel plot with asymmetrical point sizes

Related Solutions

Solved – How to draw funnel plot using ggplot2 in R

Although there's room for improvement, here is a small attempt with simulated (heteroscedastic) data:

library(ggplot2)
set.seed(101)
x <- runif(100, min=1, max=10)
y <- rnorm(length(x), mean=5, sd=0.1*x)
df <- data.frame(x=x*70, y=y)
m <- lm(y ~ x, data=df) 
fit95 <- predict(m, interval="conf", level=.95)
fit99 <- predict(m, interval="conf", level=.999)
df <- cbind.data.frame(df, 
                       lwr95=fit95[,"lwr"],  upr95=fit95[,"upr"],     
                       lwr99=fit99[,"lwr"],  upr99=fit99[,"upr"])

p <- ggplot(df, aes(x, y)) 
p + geom_point() + 
    geom_smooth(method="lm", colour="black", lwd=1.1, se=FALSE) + 
    geom_line(aes(y = upr95), color="black", linetype=2) + 
    geom_line(aes(y = lwr95), color="black", linetype=2) +
    geom_line(aes(y = upr99), color="red", linetype=3) + 
    geom_line(aes(y = lwr99), color="red", linetype=3)  + 
    annotate("text", 100, 6.5, label="95% limit", colour="black", 
             size=3, hjust=0) +
    annotate("text", 100, 6.4, label="99.9% limit", colour="red", 
             size=3, hjust=0) +
    labs(x="No. admissions...", y="Percentage of patients...") +    
    theme_bw()

alt text

R – Alternative to Sieve and Mosaic Plots for Contingency Tables

The book you described sounds like, 'Visualizing Categorical Data,' Michael Friendly. The plot described in the 1st chapter that seems to match your request was described as a type of conceptual model for visualizing contingency table data (loosely described by the author as a dynamic pressure model with observational density), and can be seen in the google preview for Ch 1. The book is geared towards SAS users.

A paper on the topic is referenced here: www.datavis.ca/papers/koln/kolnpapr.pdf

'Conceptual Models for Visualizing Contingency Table Data,' Michael Friendly .

enter image description here

*incidentally, the author is also listed as one of the authors of the vcd package (as it was specifically inspired by his book mentioned above) -- maybe you could ask him directly if there's a simple modification to one of the built in functions that's not readily apparent.

** The coloring scheme seems to relate the color blue with positive deviations from independence, and red for negative deviations. Although the red scheme makes sense in that context, maybe it would have been more apt to have used green to represent positive deviations.

http://www.datavis.ca/papers/asa92.html

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Related Solutions

Solved – How to draw funnel plot using ggplot2 in R

R – Alternative to Sieve and Mosaic Plots for Contingency Tables

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