mstBayes: Bayesian analysis of Multisite Randomised Education Trials...

Description Usage Arguments Value Examples

View source: R/mstBayes.R

Description

mstBayes performs analysis of multisite randomised education trials using a multilevel model under a Bayesian setting assuming vague priors.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
mstBayes(
  formula,
  random,
  intervention,
  baseln,
  adaptD,
  nsim = 2000,
  data,
  threshold = 1:10/10,
  ...
)

Arguments

formula

the model to be analysed is of the form y ~ x1+x2+.... Where y is the outcome variable and Xs are the independent variables.

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 appearing in the formula and the data. See example below.

baseln

A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference.

adaptD

As this function uses rstanarm, this term provides 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 scalar or vector of pre-specified threshold(s) for estimating Bayesian posterior probability that the observed effect size is greater than or equal to the threshold(s).

...

additional arguments of stan_lmer to be passed to the function.

Value

S3 object; a list consisting of

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
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)
}

eefAnalytics documentation built on March 16, 2021, 5:08 p.m.