regressiondata.nz.all

<p>20,000 samples of simulated data. The response values $y_i$ are simulated according to the following: $$y_i = 0.76 - 0.92*x_{i1} + 0.64*x_{i2} + 0.57*x_{i3} - 1.65*x_{i4} + \epsilon_i. $$
Here, each predictor variable is simulated from a Normal distribution with mean  = 0 and variance = 1.0, and each $\epsilon_i$ is simulated from a Normal distribution with mean = 0 and variance = 0.25; the predictor variables are assumed to be independent.</p>

datasets

Methods for generating Markov Chain Monte Carlo (MCMC) posterior samples of Bayesian linear regression model parameters that require only summary statistics of data as input. Summary statistics are useful for systems with very limited amounts of physical memory. The package provides two functions:  one function that computes summary statistics of data and one function that carries out the MCMC posterior sampling for Bayesian linear regression models where summary statistics are used as input. The function read.regress.data.ff utilizes the R package 'ff' to handle data sets that are too large to fit into a user's physical memory, by reading in data in chunks. See Miroshnikov, Savel'ev and Conlon (2015) <arXiv:1503.00635>.

Erin Conlon

BayesSummaryStatLM

MCMC Sampling of Bayesian Linear Models via Summary Statistics

regressiondata.nz.all function

<p>A data frame with 20,000 observations for five variables: $y, x_1, x_2, x_3, x_4$</p>

regressiondata.nz.all: Simulated data for Bayesian linear regression models, for use in package examples.

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