caceCRTBoot: Complier Average Causal Effect (CACE) Analysis of Cluster...
In ewouddt/eefAnalytics: Analysing Education Trials

Description Usage Arguments Value Examples

caceCRTBoot performs exploratoty CACE analysis of cluster randomised education trials.

1	caceCRTBoot(formula, random, intervention, compliance, nBoot, data)

`formula`	the model to be analysed. It is of the form y ~ x1+x2+.... Where y is the outcome variable and Xs are the predictors.
`random`	a string variable specifying the "clustering variable" as contained in the data. See example below
`intervention`	a string variable specifying the "intervention variable" as appeared in the formula. See example below
`compliance`	a string variable specifying the "compliance variable" as contained in the data. The data must be in percentages ranging from 0 - 100.
`nBoot`	number of bootstraps required to generate bootstrap confidence interval. Default is NULL.
`data`	data frame containing the data to be analysed.

S3 object; a list consisting of

CACE. Estimates of CACE adjusted effect sizes based on pre-specified thresholds. Only produced for threshold with at least 50
Compliers. Percentage of pupils that achieved a pre-specified threshold of compliance.

if(interactive()){

data(crtData)

######################## weighted ITT ##############################################
caceOutput<- caceCRTBoot(Posttest~ Prettest+ Intervention,
			random="School",intervention="Intervention",
			compliance = "Percentage_Attendance",nBoot=1000,data=crtData)

cace <- caceOutput$CACE
cace

Complier <- caceOutput$Compliers
Complier 

### visualising CACE effect size

plot(caceOutput)
}