linear: Simulated data for a continuous linear regression and its...

In ggmcmc: Tools for Analyzing MCMC Simulations from Bayesian Inference

Description

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.

Usage

data(linear)

Format

Three objects, namely:

s

A coda object containing posterior distributions of the intercept (beta[1]) and slope (beta[2]) of a linear regression with simulated data.

s.y.rep

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).

y

A numeric vector containing the observed values of the outcome in the linear regression with simulated data.

Source

Simulated data for ggmcmc

Examples

data(linear)
str(s)
str(s.y.rep)
str(y)

ggmcmc documentation built on Feb. 10, 2021, 5:10 p.m.