unusualprofile

The goal of unusualprofile is to calculate conditional Mahalanobis distances and related statistics. Such statistics can help find cases that are unusual, even after controlling for specified predictors.

Installation

You can install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("wjschne/unusualprofile")

Example

To use the unusualprofile package, one needs to know the correlations, means, and standard deviations among a set of continuous variables and at least one row of data from that set of variables.

Suppose we have set of variables that have the following relationships:

Multivariate normal model

First, we load the unusualprofile package.

library(unusualprofile)

Included with the unusualprofile package, the d_example data set has a single row of data generated from the path diagram depicted above.

#>        X_1     X_2      X_3      Y_1        Y_2      Y_3        X        Y
#> 1 1.999498 1.44683 2.703249 1.664106 -0.8427126 1.811528 2.273441 1.201208

Also included with the unusualprofile package is the path diagram’s model-implied correlation matrix:

R_example
#>       X_1  X_2   X_3   Y_1   Y_2   Y_3    X    Y
#> X_1 1.000 0.35 0.560 0.336 0.294 0.378 0.70 0.42
#> X_2 0.350 1.00 0.400 0.240 0.210 0.270 0.50 0.30
#> X_3 0.560 0.40 1.000 0.384 0.336 0.432 0.80 0.48
#> Y_1 0.336 0.24 0.384 1.000 0.560 0.720 0.48 0.80
#> Y_2 0.294 0.21 0.336 0.560 1.000 0.630 0.42 0.70
#> Y_3 0.378 0.27 0.432 0.720 0.630 1.000 0.54 0.90
#> X   0.700 0.50 0.800 0.480 0.420 0.540 1.00 0.60
#> Y   0.420 0.30 0.480 0.800 0.700 0.900 0.60 1.00

Using the cond_maha function

We can specify the correlations (R), means (mu), standard deviations (sigma). independent variables (v_ind), and dependent variables (v_dep). In this case, the independent variables are composite scores summarizing the dependent variables.

# Conditional Mahalanobis distance
cm <- cond_maha(data = d_example, 
          R = R_example,
          mu = 0,
          sigma = 1,
          v_ind_composites = c("X", "Y"),
          v_dep = c("X_1", "X_2", "X_3",
                    "Y_1", "Y_2", "Y_3"))

cm
#> Conditional Mahalanobis Distance = 3.5167, df = 4, p = 0.9852

# Plot
plot(cm)

Shiny App

A user-friendly app that performs the functions of the unusualprofile package is here.

Publication

An introduction to the applications of conditional distributions and Mahalanobis distances:

Schneider, W. J., & Ji, F. (2023). Detecting unusual score patterns in the context of relevant predictors. Journal of Pediatric Neuropsychology, 9, 1–17. https://doi.org/10.1007/s40817-022-00137-x