mstBayes: 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

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

mstBayes(
  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. A list of variance decomposition into between cluster variance-covariance mtrix (Schools and school by intervention) and within cluster variance (Pupils). It also contains the intra-cluster correlation (ICC).
SchEffects. Random intercepts for clusters, e.g schools and random slope.
ProbES. A maxtrix 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(mstData)

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

  output <- mstBayes(Posttest~ Intervention+Prettest,random="School",
                     intervention="Intervention",nsim=2000,data=mstData)

  ### 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)
}