Simulate a dataset with one explanatory variable and one continuous outcome variable using (y ~ dnorm(mu, sigma); mu = beta[1] + beta[2] * X). The data loads three objects: the observed y values, a coda object containing simulated values from the posterior distribution of the intercept and slope of a linear regression, and a coda object containing simulated values from the posterior predictive distribution. The purpose of the dataset is only to show the possibilities of the ggmcmc package.
data(linear)Three objects, namely:
A coda object containing posterior distributions of the intercept (beta[1]) and slope (beta[2]) of a linear regression with simulated data.
A coda object containing simulated values from the posterior predictive distribution of the outcome of a linear regression with simulated data (y ~ N(mu, sigma); mu = beta[1] + beta[2] * X; y.rep ~ N(mu, sigma); where y.rep is a replicated outcome, originally missing data).
A numeric vector containing the observed values of the outcome in the linear regression with simulated data.
data(linear)
str(s)
str(s.y.rep)
str(y)
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