bayes_updater: Bayesian Updating of Experimental Results

In anniejw6/testingtools: Helper functions to run and analyze experiments

Description

Generates posterior based on prior (Distribution 1) and likelihood (Distribution 2). This function implements the Bayesian averaging employed by Green, Krasno, and Coppock et al. in The Effects of Lawn Signs on Vote Outcomes: Results from Four Randomized Field Experiments. Their replication code is available here: https://doi.org/10.7910/DVN/K2TLDB.

Usage

bayes_updater(est_1, se_1, est_2, se_2, graph = TRUE, ...)

Arguments

est_1	Point estimate of Distribution 1
se_1	Standard error on point estimate of Distribution 2
est_2	Point estimate of Distribution 2
se_2	Standard error on point estimate of Distribution 2
graph	Boolean, should a graph be generated
...	Any additional parameters that should be passed to ?graph_bayes

Value

vector of the posterior point estimate and SE. Optionally, graph

Examples

bayes_updater(0, 0.1, 2.5, 0.1, x_min = -5, x_max = 5)

anniejw6/testingtools documentation built on Jan. 26, 2021, 6:41 a.m.