plot.valmeta {metamisc} | R Documentation |
Function to create forest plots for objects of class "valmeta"
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## S3 method for class 'valmeta' plot(x, sort = "asc", ...)
x |
An object of class |
sort |
By default, studies are ordered by ascending effect size ( |
... |
Additional arguments which are passed to forest. |
The forest plot shows the performance estimates of each validation with corresponding confidence intervals. A polygon is added to the bottom of the forest plot, showing the summary estimate based on the model. A 95% prediction interval is added by default for random-effects models, the dotted line indicates its (approximate) bounds.
An object of class ggplot
Thomas Debray <thomas.debray@gmail.com>
Debray TPA, Damen JAAG, Snell KIE, Ensor J, Hooft L, Reitsma JB, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ. 2017;356:i6460.
Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ. 2001; 322(7300):1479–80.
Riley RD, Higgins JPT, Deeks JJ. Interpretation of random effects meta-analyses. BMJ. 2011 342:d549–d549.
data(EuroSCORE) fit <- with(EuroSCORE, valmeta(cstat=c.index, cstat.se=se.c.index, cstat.95CI=cbind(c.index.95CIl,c.index.95CIu), N=n, O=n.events)) plot(fit) library(ggplot2) plot(fit, theme=theme_grey())