Computes hotelling's multivariate t-test

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.

Usage

smdi_hotelling(data = NULL, covar = NULL, n_cores = 1)

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.

References

Hotelling H. The Generalization of Student’s Ratio. Ann Math Stat. 1931;2(3):360-378.

See also

hotelling.test

Examples

library(smdi)

smdi_hotelling(data = smdi_data)
#>   covariate hotteling_p
#> 1  ecog_cat       0.783
#> 2  egfr_cat       <.001
#> 3  pdl1_num       <.001