# maha_dist

##### Computes mahalanobis distance for each row of data frame

This function will return a vector, with the same length as the number of rows of the provided data frame, corresponding to the average mahalanobis distances of each row from the whole data set.

##### Usage

`maha_dist(data, keep.NA = TRUE, robust = FALSE, stringsAsFactors = FALSE)`

##### Arguments

- data
A data frame

- keep.NA
Ensure that every row with missing data remains NA in the output? TRUE by default.

- robust
Attempt to compute mahalanobis distance based on robust covariance matrix? FALSE by default

- stringsAsFactors
Convert non-factor string columns into factors? FALSE by default

##### Details

This is useful for finding anomalous observations, row-wise.

It will convert any categorical variables in the data frame into numerics
as long as they are factors. For example, in order for a character
column to be used as a component in the distance calculations, it must
either be a factor, or converted to a factor by using the
`stringsAsFactors`

parameter.

##### Value

A vector of observation-wise mahalanobis distances.

##### See Also

##### Examples

```
# NOT RUN {
maha_dist(mtcars)
maha_dist(iris, robust=TRUE)
library(magrittr) # for piping operator
library(dplyr) # for "everything()" function
# using every column from mtcars, compute mahalanobis distance
# for each observation, and ensure that each distance is within 10
# median absolute deviations from the median
mtcars %>%
insist_rows(maha_dist, within_n_mads(10), everything())
## anything here will run
# }
```

*Documentation reproduced from package assertr, version 2.7, License: MIT + file LICENSE*