crtBayes | R Documentation |
crtBayes
performs analysis of cluster randomised education trials using a multilevel model under a Bayesian setting,
assuming vague priors.
crtBayes( formula, random, intervention, baseln, adaptD, nsim = 2000, condopt, uncopt, data, threshold = 1:10/10, ... )
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 Stanâ€™s adaptation period. Default is NULL. |
nsim |
number of MCMC iterations per chain. Default is 2000. |
condopt |
additional arguments of |
uncopt |
additional arguments of |
data |
data frame containing the data to be analysed. |
threshold |
a scalar or vector of pre-specified threshold(s) for estimating Bayesian posterior probability such that the observed effect size is greater than or equal to the threshold(s). |
... |
additional arguments of |
S3 object; a list consisting of
Beta
: Estimates and credible intervals for variables specified in the model. Use summary.eefAnalytics
to get Rhat and effective sample size for each estimate.
ES
: Conditional Hedges' g effect size and its 95% credible intervals.
covParm
: A vector of variance decomposition into between cluster variance (Schools) and within cluster variance (Pupils). It also contains intra-cluster correlation (ICC).
SchEffects
: A vector of the estimated deviation of each school from the intercept.
ProbES
: A matrix of Bayesian Posterior Probabilities such that the observed effect size is greater than or equal to a pre-specified threshold(s).
Model
: A stan_glm object used in ES computation, this object can be used for convergence diagnostic.
Unconditional
: A list of unconditional effect sizes, covParm and ProbES obtained based on between and within cluster variances from the unconditional model (model with only the intercept as a fixed effect).
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$covParm covParm ### plot random effects for schools plot(output) ### plot posterior probability of an effect size to be bigger than a pre-specified threshold plot(output,group=1) ########################################################################################### ## Bayesian analysis of cluster randomised trials using informative priors for treatment ## ########################################################################################### ### define priors for explanatory variables my_prior <- normal(location = c(0,6), scale = c(10,1)) ### specify the priors for the conditional model only output2 <- crtBayes(Posttest~ Prettest+Intervention,random="School", intervention="Intervention",nsim=2000,data=crtData, condopt=list(prior=my_prior)) ### Fixed effects beta2 <- output2$Beta beta2 ### Effect size ES2 <- output2$ES ES2 }
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