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unusualprofile

Calculates Conditional Mahalanobis Distances

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.

Functions (10)

R_example

An example correlation matrix

cond_maha

Calculate the conditional Mahalanobis distance for any variables.

d_example

An example data.frame

is_singular

Test if matrix is singular

p2label

Range label associated with probability

plot.cond_maha

Plot the variables from the results of the cond_maha function.

plot.maha

Plot objects of the maha class (i.e, the results of the cond_maha function using dependent variables only).

proportion2percentile

Rounds proportions to significant digits both near 0 and 1, then converts to percentiles

proportion_round

Rounds proportions to significant digits both near 0 and 1

unusualprofile

unusualprofile: Calculates Conditional Mahalanobis Distances

unusualprofile

Calculates Conditional Mahalanobis Distances

v0.1.0
GPL (>= 3)
Authors
W. Joel Schneider [aut, cre] (<https://orcid.org/0000-0002-8393-5316>), Feng Ji [aut]
Initial release

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