Description Usage Arguments Author(s) References Examples
Calculates the maximal information criteria for all pairwise groups of the response metric and covariates.
1 2 |
data |
|
covariates |
character vector of names of possible covariates |
response |
column name of response value |
covar_dict |
|
save_csv_file |
should the results be written to a .csv file? |
Kevin See
Reshef, D. N., Y. A. Reshef, H. K. Finucane, S. R. Grossman, G. McVean, P. J. Turnbaugh, E. S. Lander, M. Mitzenmacher, and P. C. Sabeti (2011). Detecting novel associations in large data sets. Science, 334(6062):1518–1524.
1 2 3 4 5 6 | varSelect_df = all_data_clean %>%
filter(chnk_per_m > 0) %>%
select(chnk_per_m,
one_of(as.character(hab_dict$ShortName[hab_dict$ShortName %in% names(all_data_clean)]))) %>%
select(-one_of(as.character(hab_dict$ShortName[hab_dict$MetricCategory == 'Categorical'])))
runMINE(varSelect_df, names(varSelect_df)[-1], names(varSelect_df)[1], hab_dict)
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