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MMRcaseselection

Overview

Classification and choice of cases for case studies based on regression results.

This is work in progress. For now, the package contains functions for the classification of cases as typical, deviant, extreme or pathway cases and the choice of these types of cases. At this stage, the focus is on single cases and case selection based on linear regression models (class lm).

The package has not been submitted to CRAN yet. If you want to use it, please install it from Github. Any feedback is welcomed.

devtools::install_github("ingorohlfing/MMRcaseselection")

The documentation of the package and examples can be found here.


Work on the package was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement number 638425, Enhanced Qualitative and Multimethod Research).

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Install

install.packages('MMRcaseselection')

Monthly Downloads

21

Version

0.1.0

License

GPL-3

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Maintainer

Ingo Rohlfing

Last Published

June 3rd, 2020

Functions in MMRcaseselection (0.1.0)

pathway_xvr

Plot of residuals against pathway variable
residstd_plot

Plot of typical and deviant cases based on residuals' standard deviation
residstd

Classification of cases as typical and deviant using the standard deviation of the residuals.
most_typical

Identification of the most typical case
most_underpredicted

Identification of the most underpredicted case
pathway

Pathway case
most_deviant

Identification of the most deviant case
predint

Classification of cases as typical and deviant using a prediction interval.
predint_plot

Plot of typical and deviant cases with prediction intervals
most_overpredicted

Identification of the most overpredicted case
extreme_on_x

Extremeness of cases on an independent variable
extreme_on_y

Extremeness of cases on the dependent variable