srtBayes | R Documentation |
srtBayes
performs analysis of educational trials under the assumption of independent errors among pupils using Bayesian framework with Stan.
This can also be used with schools as fixed effects.
srtBayes( formula, 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. |
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 the 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.
sigma2
: Residual variance.
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, sigma2 and ProbES obtained based on residual variance from the unconditional model (model with only the intercept as a fixed effect).
if(interactive()){ data(mstData) ######################################################## ## Bayesian analysis of simple randomised trials ## ######################################################## output <- srtBayes(Posttest~ Intervention+Prettest, intervention="Intervention",nsim=2000,data=mstData) ### Fixed effects beta <- output$Beta beta ### Effect size ES1 <- output$ES ES1 ## Covariance matrix covParm <- output$covParm covParm ### plot random effects for schools plot(output) ### plot posterior probability of an effect size to be bigger than a pre-specified threshold plot(output,group=1) ########################################################################################### ## Bayesian analysis of simple randomised trials using informative priors for treatment ## ########################################################################################### ### define priors for explanatory variables my_prior <- normal(location = c(0,6), scale = c(10,1)) ### specify the priors for the conditional model only output2 <- srtBayes(Posttest~ Prettest+Intervention, intervention="Intervention", nsim=2000,data=mstData, condopt=list(prior=my_prior)) ### Fixed effects beta2 <- output2$Beta beta2 ### Effect size ES2 <- output2$ES ES2 }
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