bayes_explore: bayes_explore
In grieman/grieman:

Description Usage Arguments Value Examples

This function uses stan to allow the user to make a bayesian model from a frequentist one, allowing for easy bayesian conclusions (intervals, estimates, etc.). It will return the bayesian model, and output a couple plots - an estimate of the population's y-value distribution, and confidence intervals for the model parameters. Be aware of shinystan::launch_shinystan for a more interactive environment - use bayes_explore %>% shinystan::launch_shinystan

1 2	bayes_explore(model, show_intercept = T, extra_plots = F, chains = 4, chain_length = 1000)

`model`	a model, in lm or glm format
`show_intercept`	boolean. Toggles if the intercept is included in plots
`extra_plots`	boolean. Toggles trace and hex plots, if applicable (hex plot needs only two parameters)
`chains`	number of markov chains to calculate. Lower numbers trade accuracy for speed
`chain_length`	length of calculated markov chains. Lower numbers trade accuracy for speed

a model and a series of plots

Estimated Population Curve: This plot shows the estimated values of the dependent (y) variable in the population at large
Posterior Distributions: This shows confidence intervals for each of the model's coefficients. These distributions can be used to make proper bayesian predictions - e.g. we are 80% confident that the true value of β_i is in this interval - as opposed to frequentist ones which use the calculated value and uncertainty to a null hypothesis' probabilty of error.

bayes_explore(lm(mpg~., mtcars))
bayes_explore(lm(stack.loss~., stackloss), show_intercept=FALSE)
bayes_explore(lm(Fertility~0+., swiss))
bayes_explore(glm(am~0+mpg+wt, mtcars, family=binomial(link='logit')), extra_plots=TRUE)