d_example

A dataset with 1 row of data for a single case.

datasets

Calculates a Mahalanobis distance for every row of a set of
outcome variables (Mahalanobis, 1936
<doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis
distance is calculated using a conditional covariance matrix (i.e., a
covariance matrix of the outcome variables after controlling for a set
of predictors). Plotting the output of the cond_maha() function can
help identify which elements of a profile are unusual after
controlling for the predictors.

W. Joel Schneider

unusualprofile

Calculates Conditional Mahalanobis Distances

Feng Ji

d_example function

A data frame with 1 row and 8 variables:<dl>
<dt>X_1</dt>
<dd>A predictor variable</dd><dt>X_2</dt>
<dd>A predictor variable</dd><dt>X_3</dt>
<dd>A predictor variable</dd><dt>Y_1</dt>
<dd>An outcome variable</dd><dt>Y_2</dt>
<dd>An outcome variable</dd><dt>Y_3</dt>
<dd>An outcome variable</dd><dt>X</dt>
<dd>A latent predictor variable</dd><dt>Y</dt>
<dd>A latent outcome variable</dd>
</dl>

Format

An example data.frame — d_example

A data frame with 1 row and 8 variables:<dl>
<dt>X_1</dt>
<dd>A predictor variable</dd>

<dt>X_2</dt>
<dd>A predictor variable</dd>

<dt>X_3</dt>
<dd>A predictor variable</dd>

<dt>Y_1</dt>
<dd>An outcome variable</dd>

<dt>Y_2</dt>
<dd>An outcome variable</dd>

<dt>Y_3</dt>
<dd>An outcome variable</dd>

<dt>X</dt>
<dd>A latent predictor variable</dd>

<dt>Y</dt>
<dd>A latent outcome variable</dd>


</dl>

d_example: An example data.frame

Description

Usage

Arguments

Format