Classification as typical and deviant"

In MMRcaseselection: Case Classification and Selection Based on Regression Results

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(MMRcaseselection)
library(ggplot2) # for illustration of plot customization

The classification (designation, assignment) of cases as typical and deviant needs to be distinguished from the actual choice of cases that are of a specific type. The package offers two ways of classifying cases. First, one can designate cases using the prediction interval or the standard deviation of the residuals. Both classification techniques require an lm object as input.

The approach using the prediction interval additionally requires the specification of the width of the interval. The % prediction interval represents the range of outcome values within which 95% of future outcome values are expected to fall in repeated samples. Following Rohlfing and Starke (2013), a case is designated as typical if the observed outcome value is inside the interval and as deviant otherwise.

Here is a simple example using the mtcars dataset and a 90% prediction interval (for no specific reason, the default value is 0.95). The output of the function is a dataframe with the observed and fitted outcome values, the upper and lower bound of the prediction interval and the case status based on the fitted value and the prediction interval that is specific to each case.

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

The designation of cases based on the prediction interval can be visualized with the predint_plot() command. The graph plots observed vs. fitted outcome values together with the % prediction interval. The plot is a gg object and can be edited with the usual commands of the ggplot2 package.

The input into the plotting function has to be a dataframe produced with predint(). The color scheme is the viridis color palette that is accustomed for colorblind readers and also works when printed in grey scale. If the viridis palette is not preferred, one can substitute it with any other scale_color_* scheme by adding it to the command.

predint_plot(pi_df)
# Using scale_color_brewer() instead of the viridis palette.
predint_plot(pi_df) + scale_color_brewer()

An alternative classification technique following Lieberman (2005) uses the standard deviation of the residuals. The command for assigning cases and typical and deviant is residstd(). The specification of stdshare determines how large the share of the residual standard deviation should be to distinguish typical from deviant cases. The default value is 1 (for no specific reason).

resid_df <- residstd(df, stdshare = 1.5)
head(resid_df)

To my knowledge, it has not been proposed to plot the case classification resulting from this technique. However, it can be done and is available in this package with the command residstd_plot(). The standard deviation of the residual is necessarily the same for all cases and is represented by a symmetric corridor (parallel grey lines) around the bisecting line in a plot of observed vs. fitted outcome values. The plot is a gg object that can be accustomed with ggplot2 commands.

residstd_plot(resid_df)

Packages used in this vignette

• base (R Core Team 2020)
• ggplot2 (Wickham 2016)
• grateful (Rodriguez-Sanchez 2018)

R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Rodriguez-Sanchez, Francisco. 2018. grateful: Facilitate Citation of R Packages. https://github.com/Pakillo/grateful.

Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

MMRcaseselection documentation built on July 1, 2020, 9:55 p.m.