crtBayes: Bayesian analysis of cluster randomised educatuon trials...
In germaine86/eefAnalytics2020: Analysing Education Trials

Description Usage Arguments Value Examples

View source: R/eefAnalytics_14_09_2020.R

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

crtBayes(
  formula,
  random,
  intervention,
  adaptD = NULL,
  nsim = 2000,
  data,
  threshold = 1:10/10,
  ...
)

`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
`adaptD`	As this function uses stan, this term means the target average proposal acceptance probability during Stan’s adaptation period. Default is NULL.
`nsim`	number of MCMC iterations per chain. Default is 2000.
`data`	data frame containing the data to be analysed.
`threshold`	a vector of pre-specified threshold(s) to be compared with effect size.
`...`	additional arguments of stan_lmer to be passed to the function.

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) with its(their) credible intervals are 95
covParm. Vector of variance decomposition into between cluster variance (Schools) and within cluster variance (Pupils). It also contains the intra-cluster correlation (ICC).
SchEffects. Random intercepts for clusters, e.g schools.
ProbES. A matrix containing the probability of observing effect size greater than a pre-specified threshold.
Unconditional. A list of ES, covParm and ProbES obtained based on between and within cluster variances from the unconditional model (model without covariate).

if(interactive()){

  data(crtData)

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

  output <- crtBayes(Posttest~ Intervention+Prettest,random="School",
                     intervention="Intervention",nsim=2000,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)
}