plotcat: Categorical Moderator Graph

In MAc: Meta-Analysis with Correlations

Description

Outputs a rich and detailed boxplot graphic for each level of the specified moderator (under a fixed or random effects model).

Usage

plotcat(es, var, mod, data,  method="random",  modname=NULL,  title=NULL, ...)

Arguments

es	r or z' effect size.
var	Vaiance of es.
mod	Categorical moderator variable used for moderator analysis.
method	Default is random. For fixed effects, use fixed.
data	data.frame with values above.
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.

Value

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.

Author(s)

AC Del Re & William T. Hoyt

Maintainer: AC Del Re [e-mail]

References

Cooper, H., Hedges, L.V., & Valentine, J.C. (2009). The handbook of research synthesis and meta-analysis (2nd edition). New York: Russell Sage Foundation.

See Also

macat, plotcon

Examples

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)

MAc documentation built on May 1, 2019, 10:55 p.m.