ComparePlot {eefAnalytics} | R Documentation |
A plot function to compare different eefAnalytics S3 objects from the eefAnalytics package.
Description
It generates bar plot that compares the effect size from eefAnalytics' methods.
Usage
ComparePlot(
eefAnalyticsList,
group,
Conditional = TRUE,
ES_Total = TRUE,
modelNames
)
Arguments
eefAnalyticsList |
A list of eefAnalytics S3 objects from eefAnalytics package. |
group |
a string/scalar value indicating which intervention to plot. This must be one of the values of intervention variable excluding the control group. For a two arm trial, the maximum number of values to consider is 1 and 2 for three arm trial. |
Conditional |
a logical value to indicate whether to plot conditional effect size. The default is Conditional=TRUE, otherwise Conditional=FALSE should be specified for plot based on unconditional effect size. Conditional variance is total or residual variance a multilevel model with fixed effects, whilst unconditional variance is total variance or residual variance from a multilevel model with only intercept as fixed effect. |
ES_Total |
A logical value indicating whether to plot the effect size based on total variance or within school variance. The default is ES_Total=TRUE, to plot effect size using total variance. ES_Total=FALSE should be specified for effect size based on within school or residuals variance. |
modelNames |
a string factor containing the names of model to compare. See examples below. |
Details
ComparePlot
produces a bar plot which compares the effect sizes and the associated confidence intervals from the different models.
For a multilevel model, it shows the effect size based on residual variance and total variance.
Value
Returns a bar plot to compare the different methods. The returned figure can be further modified as any ggplot
Examples
if(interactive()){
data(mstData)
###############
##### SRT #####
###############
outputSRT <- srtFREQ(Posttest~ Intervention + Prettest,
intervention = "Intervention", data = mstData)
outputSRTBoot <- srtFREQ(Posttest~ Intervention + Prettest,
intervention = "Intervention",nBoot=1000, data = mstData)
###############
##### MST #####
###############
outputMST <- mstFREQ(Posttest~ Intervention + Prettest,
random = "School", intervention = "Intervention", data = mstData)
outputMSTBoot <- mstFREQ(Posttest~ Intervention + Prettest,
random = "School", intervention = "Intervention",
nBoot = 1000, data = mstData)
##################
##### Bayesian #####
##################
outputSRTbayes <- srtBayes(Posttest~ Intervention + Prettest,
intervention = "Intervention",
nsim = 2000, data = mstData)
## comparing different results
ComparePlot(list(outputSRT,outputSRTBoot,outputMST,outputMSTBoot,outputSRTbayes),
modelNames =c("ols", "olsBoot","MLM","MLMBoot","OLSBayes"),group=1)
}