# Computes hotelling's multivariate t-test

`smdi_hotelling.Rd`

Hotelling's multivariate t-test, which examines variable
differences conditional on having an observed covariate value or not.
As the power of statistical hypothesis tests can be influenced by
sample size, the combined investigation along with `smdi_asmd`

is highly recommended.

Important: don't include variables like ID variables, ZIP codes, dates, etc.

## Arguments

- data
dataframe or tibble object with partially observed/missing variables

- covar
character covariate or covariate vector with partially observed variable/column name(s) to investigate. If NULL, the function automatically includes all columns with at least one missing observation and all remaining covariates will be used as predictors

- n_cores
integer, if >1, computations will be parallelized across amount of cores specified in n_cores (only UNIX systems)

## Value

returns a hotelling object with statistics on hotellings test by covariate. That is, for each covar, the following outputs are provided:

stats: hotelling test statistics (for more information see

`hotelling.test`

)pval: p-value of hotelling test

## Details

CAVE: Hotelling's and Little's show high susceptibility with large sample sizes and it is recommended to always interpret the results along with the other diagnostics.