mrregression {mrregression}

mrregression: Frequentist and Bayesian linear regression using Merge and Reduce.

Description

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. The package contains the two main functions mrfrequentist and mrbayes as well as several S3 methods listed below. Note, that currently only numerical predictors are supported. Factor variables can be included in the model in dummy-coded form, e.g. using model.matrix. However, this may lead to highly variable or even unreliable estimates / posterior distributions if levels are not represented well in every single block. It is solely the user's responsibility to check that this is not the case!

Usage

## S3 method for class 'mrfrequentist'
coef(object, ...)

## S3 method for class 'mrfrequentist'
nobs(object, ...)

## S3 method for class 'mrfrequentist'
predict(object, data, ...)

## S3 method for class 'mrfrequentist'
summary(object, ...)

## S3 method for class 'summary.mrfrequentist'
print(x, ...)

## S3 method for class 'mrbayes'
nobs(object, ...)

## S3 method for class 'mrbayes'
summary(object, ...)

## S3 method for class 'summary.mrbayes'
print(x, ...)

Arguments

object

Object of class "mrfrequentist" or "mrbayes", respectively.

...

Currently only useful for method print.summary.mrfrequentist and approach "3". See arguments to function printCoefmat, especially digits and signif.stars.

data

A data.frame used to predict values of the dependent variable. Data has to contain all variables in the model, additional columns are ignored. Note that this is not an optional argument.

x

Object of class "summary.mrfrequentist" or "summary.mrbayes", respectively.

References

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: https://doi.org/10.1007/s41060-020-00226-0