plotcon: Meta Regression Scatterplot

In MAc: Meta-Analysis with Correlations

Description

Outputs a scatterplot from a fixed or random effects meta regression (continuous and/or categorical).

Usage

plotcon(es, var, mod, data, method= "random", modname=NULL, 
  title=NULL, ylim=c(0, 1), ...)

Arguments

es	r or z' effect size.
var	Vaiance of g.
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.
ylim	Limits of y-axis with the first argument for the minimum y-value and the second for the maximum y value. Default is c(0, 1).
...	Additional arguments to be passed to ggplot.

Value

Scatterplot with fixed or random effects regression line with size of visual points based on study weights, where the more precise studies have larger points. The ggplot2 package outputs the rich 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

mareg, plotcat

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: plotcon(es = r, var = var.r, mod = mod1, data = dat, method= "random", 
modname= "Moderator") 
## End(Not run)

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