MAc: plotcat – R documentation

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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

`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 acdelre@gmail.com

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

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

Meta-Analysis with Correlations

v1.1.1

GPL-2

Authors

AC Del Re & William T. Hoyt

Initial release

2010-07-06