mstBayes | R Documentation |
mstBayes
performs Bayesian multilevel analysis of multisite randomised education trials, utilising vague priors
and JAGS language to fit the model. It assumes hierarchical clustering, such as students within schools, and estimates
treatment effects while accounting for this structure and assuming that all random effects are independent.
mstBayes(
formula,
random,
intervention,
baseln,
nsim = 10000,
data,
alpha = 0.05,
digits = 3,
threshold = c(0, 0.05, seq(0.1, 1, 0.1)),
condopt,
uncopt,
...
)
formula |
The model to be analysed. It should be of the form y ~ x1 + x2 + ..., where y is the outcome variable and Xs are the predictors. |
random |
A string specifying the "clustering variable" (e.g., schools or sites) as found in the dataset. |
intervention |
A string specifying the "intervention variable" as it appears in the formula. |
baseln |
A string specifying the reference category for the intervention variable. If not provided, the first level will be used as the reference (e.g., baseln = "0" for an intervention with levels 0 and 1). |
nsim |
Number of MCMC iterations to be performed. A minimum of 10,000 is recommended to ensure convergence. |
data |
A data frame containing the variables referenced in the formula, including predictors, the clustering variable, and the intervention. |
alpha |
significant level, default alpha = 0.05. |
digits |
number of decimal places, by default digits=3 |
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). |
condopt |
additional arguments of |
uncopt |
additional arguments of |
... |
Common additional arguments of |
The function provides posterior estimates for fixed effects (predictors) and random effects (clustering) under a Bayesian framework. Effect sizes are computed using Hedges' g, and variance components are decomposed into between-cluster and within-cluster variances.
S3 object; a list consisting of:
Beta
: Estimates and credible intervals for the predictors specified in the model (posterior distributions).
ES
: Hedges' g effect size for the intervention(s). If bootstrapping is not used, 95% credible intervals are computed based on MCMC sampling.
covParm
: Variance components broken down into between-cluster variance (e.g., between schools), within-cluster variance (e.g., within pupils), and intra-cluster correlation (ICC).
randomEffects
: Posterior estimates of random intercepts for each cluster (e.g., schools).
ProbES
: A matrix showing the probability of observing an effect size larger than various thresholds (0, 0.05, 0.10, ...).
Model
: A model object from jags
and an MCMCsummary
object containing only the mean and credible intervals (CIs) as columns.
Unconditional
: A list containing the unconditional effect size and variance decomposition.
if(interactive()){
data(mstData)
########################################################
## Bayesian analysis of multisite randomised trials ##
########################################################
output <- mstBayes(formula = Posttest ~ Prettest + Intervention,
random = "School",
intervention = "Intervention",
alpha = 0.05,
digits = 3,
nsim = 10000,
data = mstData)
output
### Fixed effects
beta <- output$Beta
beta
### Effect size
ES1 <- output$ES
ES1
## Covariance matrix
covParm <- output$covParm
covParm
## Prob ES
ProbES <- output$ProbES
ProbES
## Unconditional
Unconditional <- output$Unconditional
Unconditional
## Random Effect
randomEffects <- output$SchEffects
randomEffects
### 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)
}
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