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Last data update: 2014.03.03

R: Categorical Moderator Analysis

macat

R Documentation

Categorical Moderator Analysis

Description

Computes single predictor categorical moderator analysis under a fixed or random effects model.

Usage

macat(g, var, mod, data, method= "random")

Arguments

`g`	Hedges g (unbiased estimate of d) 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.

Details

See Konstantopoulos & Hedges (2009; pp. 280-288) for the computations used in this function.

Value

`mod`	Level of the categorical moderator.
`k`	Number of studies for each level of the moderator.
`estimate`	Mean effect size of each level of the moderator.
`ci.l`	Lower 95% confidence interval.
`ci.u`	Upper 95% confidence interval.
`z`	z-score (standardized value).
`p`	Significance level.
`var`	Variance of effect size.
`se`	Square root of variance.
`Q`	Q-statistic (measure of homogeneity).
`df`	Degrees of freedom for Q-statistic.
`p.h`	p-value for homogeneity within that level of the moderator.
`I2`	Proportion of total variation in effect size that is due to heterogeneity rather than chance (see Shadish & Haddock, 2009; pp. 263).
`Q`	Q-statistic overall. Note: Whether fixed or random effects analyses are conducted, the Q statistic reported is for the fixed effect model. Therefore, Qb + Qw != Q in the random effects output.
`Qw`	Q-within (or error). Measure of within-group heterogeneity.
`Qw.df`	Degrees of freedom for Q-within.
`Qw.p`	Q-within p-value (for homogeneity).
`Qb`	Q-between (or model). Measure of model fit.
`Qb.df`	Degrees of freedom for Q-between.
`Qb.p`	Q-between p-value (for homogeneity). Qb and Qb.p provide the test of whether the moderator variable(s) account for significant variance among effect sizes.

Author(s)

AC Del Re & William T. Hoyt

Maintainer: AC Del Re acdelre@gmail.com

References

Konstantopoulos & Hedges (2009). Analyzing effect sizes: Fixed-effects models. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta analysis (pp. 279-293). New York: Russell Sage Foundation.

Shadish & Haddock (2009). Analyzing effect sizes: Fixed-effects models. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta analysis (pp. 257-278). New York: Russell Sage Foundation.

Examples

id<-c(1:20)
n.1<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)
n.2 <- c(11,22,10,20,25,12,12,36,19,11,34,75,33,120,37,14,40,16,10,21)
g <- c(.68,.56,.23,.64,.49,-.04,1.49,1.33,.58,1.18,-.11,1.27,.26,.40,.49,
.51,.40,.34,.42,1.16)
var.g <- c(.08,.06,.03,.04,.09,.04,.009,.033,.0058,.018,.011,.027,.026,.0040,
.049,.0051,.040,.034,.0042,.016)
mod<-factor(c(rep(c(1,1,2,3),5)))
df<-data.frame(id, n.1,n.2, g, var.g,mod)

# Example

# Random effects
macat(g = g, var= var.g, mod = mod, data = df, method= "random")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(MAd)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MAd/macat.rd_%03d_medium.png", width=480, height=480)
> ### Name: macat
> ### Title: Categorical Moderator Analysis
> ### Aliases: macat
> ### Keywords: models
> 
> ### ** Examples
> 
> id<-c(1:20)
> n.1<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)
> n.2 <- c(11,22,10,20,25,12,12,36,19,11,34,75,33,120,37,14,40,16,10,21)
> g <- c(.68,.56,.23,.64,.49,-.04,1.49,1.33,.58,1.18,-.11,1.27,.26,.40,.49,
+ .51,.40,.34,.42,1.16)
> var.g <- c(.08,.06,.03,.04,.09,.04,.009,.033,.0058,.018,.011,.027,.026,.0040,
+ .049,.0051,.040,.034,.0042,.016)
> mod<-factor(c(rep(c(1,1,2,3),5)))
> df<-data.frame(id, n.1,n.2, g, var.g,mod)
> 
> # Example
> 
> # Random effects
> macat(g = g, var= var.g, mod = mod, data = df, method= "random")

 Model Results:  
 
      mod  k estimate   var    se  ci.l  ci.u     z     p       Q df p.h  I2
1       1 10    0.487 0.024 0.155 0.183 0.790 3.143 0.002  40.267  9   0 78%
2       2  5    0.510 0.045 0.211 0.097 0.924 2.419 0.016 145.283  4   0 97%
3       3  5    0.976 0.045 0.213 0.559 1.393 4.583 0.000  41.856  4   0 90%
4 Overall 20    0.618 0.012 0.108 0.407 0.829 5.739 0.000 246.723 19   0 92%

 Heterogeneity:  
 
        Q      Qw Qw.df Qw.p    Qb Qb.df  Qb.p
1 246.723 227.405    17    0 3.802     2 0.149
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>