plotcat: Categorical Moderator Graph

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

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.

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.

Examples

Run this code

# NOT RUN {
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") 
# }