srtBayes | R Documentation |
srtBayes
performs Bayesian multilevel analysis of Simple Randomised Education Trials (SRT), utilising vague priors
and JAGS language to fit the model.
This can also be used with schools as fixed effects.
srtBayes(
formula,
intervention,
baseln,
nsim = 2000,
data,
alpha = 0.05,
digits = 3,
threshold = 1:10/10,
condopt,
uncopt,
...
)
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. |
nsim |
number of MCMC iterations per chain. Default is 2000. |
data |
Data frame containing the data to be analysed. |
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 |
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 model object from jags
and an MCMCsummary
object containing only the mean and credible intervals (CIs) as columns.
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,
alpha = 0.2,
digits=4,
intervention="Intervention",
nsim=10000,
data=mstData)
### Fixed effects
beta <- output$Beta
beta
### Effect size
ES1 <- output$ES
ES1
### Effect size
ES2 <- output$Unconditional$ES
ES2
## Covariance matrix
covParm1 <- output$sigma2
covParm1
## Unconditional Covariance matrix
covParm2 <- output$Unconditional$sigma2
covParm2
## Prob ES
ProbES1 <- output$ProbES
ProbES1
## Prob based on Unconditional ES
ProbES2 <- output$Unconditional$ProbES
ProbES2
### plot posterior probability of an effect size to be bigger than a pre-specified threshold
plot(output,group=1)
}
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