exampleData

<p>Simulated data set with 1500 observations for illustrational
purposes.</p>

datasets

Frequentist and Bayesian linear regression for large data sets. Useful
when the data does not fit into memory (for both frequentist and Bayesian regression),
to make running time manageable (mainly for Bayesian regression), and to reduce
the total running time because of reduced or less severe memory-spillover into
the virtual memory. This is an implementation of Merge & Reduce for linear regression
as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020).
'Streaming statistical models via Merge & Reduce'. International Journal of
Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.

Leo Nils Geppert

mrregression

Regression Analysis for Very Large Data Sets via Merge and
Reduce

exampleData function

<p>A data frame with 1500 rows and 11 variables where V1-V10 are the
predictors and V11 is the dependent variable.</p>

exampleData: Simulated Example Data

Description

Usage

Arguments

Format