Description Usage Arguments Value References Examples
This function computes a lag-1 autoregression time series analysis with Bayesian estimates for multiple-baseline designs. It can be used with data from a multiple-baseline design to examine the effect of the introduction of a treatment B after a baseline A.
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y |
outcome variable |
P |
phase identifier |
s |
session identifier |
model |
the model to be fitted. If set to "level" (the default), an intercepts only model is calculated. If set to "trend", an intercepts and slopes model is calculated. |
plots |
whether graphs are to be plotted. Defaults to TRUE. |
diagnostics |
whether diagnostic statistics are to be calculated. Defaults to FALSE. The diagnostics option retrieves the Gelman-Rubin statistic (which measures the ratio of the between-within variability within the chains); the ESS or effective sample size (which measures the independent information within the sampled chains given their length and autocorrelation); and the MCSE or Monte Carlo Standard Error (which measures the estimated standard deviation of the sample mean in the chains). |
adaptSteps |
number of steps to adapt the chain |
burnInSteps |
number of steps to burn in |
nChains |
number of chains to be computed |
numSavedSteps |
total number of steps to be computed |
thinSteps |
save every nth number of steps in the chain |
beta |
model regression coefficients estimates |
phase |
phase regression coefficients estimates |
delta |
phase change effect size estimates |
Kruschke, J. (2010). Doing Bayesian Data Analysis: A Tutorial Introduction with R. Boston, MA.: Academic Press.
Swaminathan, H., Rogers, H. J., & Horner, R. H. (2014). An effect size measure and Bayesian analysis of single-case designs. Journal of School Psychology, 52(2), 213-230. doi:10.1016/j.jsp.2013.12.002
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