Package {MMRcaseselection}

Type:	Package
Title:	Case Classification and Selection Based on Regression Results
Version:	0.2.0
Depends:	R (≥ 2.10)
Description:	Researchers doing a mixed-methods analysis (nested analysis as developed by Lieberman (2005) <doi:10.1017/S0003055405051762>) can use the package for the classification of cases and case selection using results of a linear regression. One can designate cases as typical, deviant, extreme and pathway case and use different case selection strategies for the choice of a case belonging to one of these types.
License:	GPL-3
Encoding:	UTF-8
Imports:	stats, ggplot2
URL:	https://github.com/ingorohlfing/MMRcaseselection
BugReports:	https://github.com/ingorohlfing/MMRcaseselection/issues
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
Language:	en-US
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-05-17 17:35:46 UTC; ingor
Author:	Ingo Rohlfing [aut, cre]
Maintainer:	Ingo Rohlfing <ingo.rohlfing@uni-passau.de>
Repository:	CRAN
Date/Publication:	2026-05-21 09:32:15 UTC

Extremeness of cases on an independent variable

Description

Extremeness of a case is calculated by the difference between a case's value on the independent variable and the variable's mean value.

Usage

extreme_on_x(lmobject = NULL, ind_var = NULL)

Arguments

Details

Calculating the absolute value of the difference between the cases' values and the variable's mean value is proposed by Seawright, Jason (2016): The Case for Selecting Cases That Are Deviant or Extreme on the Independent Variable. Sociological Methods & Research 45 (3): 493-525. (doi:10.1177/0049124116643556)

Value

A dataframe with

- all variables in the linear model,

- absolute extremeness (absolute value of difference between variable score and mean value of variable),

- extremeness (difference between variable score and mean value of variable), which can be useful when the direction of extremeness is relevant.

The rows are ordered in decreasing order of the absolute extreme values.

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
extreme_on_x(df, "wt")

Extremeness of cases on the dependent variable

Description

Extremeness of a case is calculated by the difference between a case's value on the dependent variable and the variable's mean value.

Usage

extreme_on_y(lmobject)

Arguments

Details

Calculating the absolute value of the difference between the cases' values and the variable's mean value is proposed by Seawright, Jason (2016): The Case for Selecting Cases That Are Deviant or Extreme on the Independent Variable. Sociological Methods & Research 45 (3): 493-525. (doi:10.1177/0049124116643556)

Value

A dataframe with

- all variables in the linear model,

- absolute extremeness (absolute value of difference between variable score and mean value of variable),

- extremeness (difference between variable score and mean value of variable), which can be useful when the direction of extremeness is relevant.

The rows are ordered in decreasing order of the absolute extreme values.

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
extreme_on_y(df)

Identification of the most deviant case

Description

Identification of the most deviant case (= worst predicted case), based on regression estimates.

Usage

most_deviant(lmobject)

Arguments

Details

Proposed by Seawright, Jason and John Gerring (2008): Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options. Political Research Quarterly 61 (2): 294-308. (doi:10.1177/1065912907313077)

Value

The most deviant case with the largest absolute residual of all cases.

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
most_deviant(df)

Identification of the most overpredicted case

Description

The case with the largest negative difference between the observed value and the predicted value on the outcome. Depending on the research question, there might be a specific interest in the case for which the model performs worst and yields a larger predicted value.

Usage

most_overpredicted(lmobject)

Arguments

Value

The most overpredicted case with the largest negative residual (the most negative residual).

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
most_overpredicted(df)

Identification of the most typical case

Description

The most typical case (= best predicted case) based on regression estimates.

Usage

most_typical(lmobject)

Arguments

Details

Proposed by Seawright, Jason and John Gerring (2008): Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options. Political Research Quarterly 61 (2): 294-308. (doi:10.1177/1065912907313077)

Value

The most typical case having the smallest absolute residual of all cases.

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
most_typical(df)

Identification of the most underpredicted case

Description

The case with the largest positive difference between the observed value and the predicted value on the outcome. Depending on the research question, there might be a specific interest in the case for which the model performs worst and yields a smaller predicted value.

Usage

most_underpredicted(lmobject)

Arguments

Value

The most underpredicted case with the largest positive residual (the most positive residual).

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
most_underpredicted(df)

Pathway case

Description

Calculation of pathway values, defined as the difference between residuals of full model and reduced model lacking the pathway variable. The larger the difference, the more a case qualifies as a pathway case suitable for the analysis of mechanisms.

Usage

pathway(full_model, reduced_model)

Arguments

Details

The difference between the absolute residuals of the full and reduced model follows the approach developed by Weller and Barnes (2014): Finding Pathways: Mixed-Method Research for Studying Causal Mechanisms. Cambridge: Cambridge University Press. doi:10.1017/CBO9781139644501).

The calculation of the absolute difference between the full-model and reduced-model residuals, given a case's reduced-model residual is larger than its full-model residual, follows the proposal by Gerring (2007): Is There a (Viable) Crucial-Case Method? Comparative Political Studies 40 (3): 231-253. doi:10.1177/0010414006290784)

Value

A dataframe with

- all full model variables,

- full model residuals (full_resid),

- reduced model residuals (reduced_resid),

- pathway values following Weller/Barnes (pathway_wb),

- pathway values following Gerring (pathway_gvalue),

- variable showing whether Gerring's criterion for a pathway case is met (pathway_gstatus)

Examples

df_full <- lm(mpg ~ disp + wt, data = mtcars)
df_reduced <- lm(mpg ~ wt, data = mtcars)
pathway(df_full, df_reduced)

Plot of residuals against pathway variable

Description

Plot of residuals against pathway variable

Usage

pathway_xvr(full_model, reduced_model, pathway_type)

Arguments

Value

A plot of the chosen type of pathway values against the pathway variable created with ggplot2.

Examples

df_full <- lm(mpg ~ disp + wt, data = mtcars)
df_reduced <- lm(mpg ~ wt, data = mtcars)
pathway_xvr(df_full, df_reduced, pathway_type = "pathway_wb")

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

Description

Case are designated as typical (= well predicted) and deviant (= badly predicted) based on the prediction interval. The x% prediction interval represents the range that we expect to include x% of outcome values in repeated samples. For example, a 95% prediction interval ranging from 0-5 conveys that 95% of future outcome values will be in the range of 0-5. If the observed outcome is inside the prediction interval, the case is classified (or designated) as typical and as deviant otherwise.

Usage

predint(lmobject, piwidth = 0.95)

Arguments

Details

Proposed by Rohlfing, Ingo and Peter Starke (2013): Building on Solid Ground: Robust Case Selection in Multi-Method Research. *Swiss Political Science Review* 19 (4): 492-512. (doi:10.1111/spsr.12052)

Value

A dataframe with the observed outcome, fitted outcome, upper and lower bound of the % prediction interval and classification of cases as typical or deviant.

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
predint(df, piwidth = 0.9)

Plot of typical and deviant cases with prediction intervals

Description

Presented in Rohlfing, Ingo and Peter Starke (2013): Building on Solid Ground: Robust Case Selection in Multi-Method Research. Swiss Political Science Review 19 (4): 492-512. (doi:10.1111/spsr.12052)

Usage

predint_plot(pred_df)

Arguments

Value

A plot of the observed outcome against the fitted outcome with prediction intervals and case classifications. Created with ggplot2.

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
predint_status <- predint(df, piwidth = 0.9)
predint_plot(predint_status)

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

Description

The share of the standard deviation of the residuals is used to designate cases as typical or deviant.

Usage

residstd(lmobject, stdshare = 1)

Arguments

Details

Proposed by Lieberman, Evan S. (2005): Nested Analysis as a Mixed-Method Strategy for Comparative Research. American Political Science Review 99 (3): 435-452. doi:10.1017/S0003055405051762.

Value

A dataframe with the observed outcome, fitted outcome, residual standard deviation and classification of cases as typical or deviant.

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
residstd(df, stdshare = 1)

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

Description

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

Usage

residstd_plot(resid_df)

Arguments

Value

A plot of the observed outcome against the fitted outcome with interval and case classifications. Created with ggplot2.

Examples

df <- lm(mpg ~ disp + wt, data = mtcars)
residstd_status <- residstd(df, stdshare = 1)
residstd_plot(residstd_status)