mlmBayes: Bayesia multilevel analysis of randomised educatuon trials...
In eefAnalytics: Analysing Education Trials

Description Usage Arguments Value Examples

View source: R/eefAnalytics_03_2017.r

mlmBayes performs analysis of randomised eduation trials using multilevel model under Bayesian framework assuming vague priors.

1	mlmBayes(formula, random, intervention, nSim, 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
`nSim`	number of MCMC iterations. A minimum of 10,000 is recommended.
`data`	data frame containing the data to be analysed.

S3 object; a list consisting of

Beta. Estimates and confidence intervals for preditors specified in the model.
ES. Hedges' g effect size for the intervention(s). If nBoot is not specified, the confidence intervals are 95
covParm. Vector of variance decomposition into between cluster variance (Schools) and within cluster variance (Pupils). It also contains the intral-cluster correlation (ICC).
SchEffects. Random intercepts for clusters, e.g schools.
ProbES. A maxtrix containing the probability of observing effect size greater than a pre-specified threshold.

if(interactive()){
  
data(crtData)

########################################################
## Bayesian analysis of cluster randomised trials     ##
########################################################

output <- mlmBayes(Posttest~ Intervention+Prettest,random="School",
		intervention="Intervention",nSim=10000,data=crtData)

### Fixed effects
beta <- output$Beta
beta

### Effect size
ES1 <- output$ES
ES1

## Covariance matrix
covParm <- output$covParm
covParm

### plot random effects for schools

plot(output)

### plot posterior probability of an effect size to be bigger than a pre-specified threshold

plot(output,group=1)
}