Description Usage Arguments Value Author(s) References See Also Examples
Outputs a rich and detailed boxplot graphic for each level of the specified moderator (under a fixed or random effects model).
1 |
es |
r or z' effect size. |
var |
Vaiance of es. |
mod |
Categorical moderator variable used for moderator analysis. |
method |
Default is |
data |
|
modname |
Name of moderator to appear on x axis of plot. Default is NULL. |
title |
Plot title. Default is NULL. |
... |
Additional arguments to be passed to ggplot. |
Boxplot graph with median, max, min, and outliers from a fixed or random effects categorical moderator analysis. Places jitter points (for each study) on the boxplots. The size of each point (representing a study in the analysis) are based on study weights where more precise studies have larger points. The ggplot2 package outputs the graphics.
AC Del Re & William T. Hoyt
Maintainer: AC Del Re acdelre@gmail.com
Cooper, H., Hedges, L.V., & Valentine, J.C. (2009). The handbook of research synthesis and meta-analysis (2nd edition). New York: Russell Sage Foundation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | id<-c(1:20)
n<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)
r<-c(.68,.56,.23,.64,.49,-.04,.49,.33,.58,.18,-.11,.27,.26,.40,.49,
.51,.40,.34,.42,.16)
mod1<-c(1,2,3,4,1,2,8,7,5,3,9,7,5,4,3,2,3,5,7,1)
dat<-data.frame(id,n,r,mod1)
dat$var.r <- var_r(dat$r, dat$n) # MAc function to derive variance
dat$z <- r_to_z(dat$r) # MAc function to convert to Fisher's z (z')
dat$var.z <- var_z(dat$n) # MAc function for variance of z'
dat$mods2 <- factor(rep(1:2, 10))
# Example
## Not run: plotcat(es = r, var = var.r, mod = mods2, data = dat, method= "random",
modname= "Moderator")
## End(Not run)
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