runMINE: QRF Variable Selection - MINE

Description Usage Arguments Author(s) References Examples

Description

Calculates the maximal information criteria for all pairwise groups of the response metric and covariates.

Usage

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runMINE(data = NULL, covariates = NULL, response = NULL,
  covar_dict = NULL, save_csv_file = F)

Arguments

data

data.frame that was used to fit the mod

covariates

character vector of names of possible covariates

response

column name of response value

covar_dict

data.frame containing columns called ShortName, Name, and MetricCategory. ShortName corresponds to the covariates that went into the mod, while Name is a longer version for plotting purposes. MetricCategory is a grouping factor

save_csv_file

should the results be written to a .csv file?

Author(s)

Kevin See

References

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.

Examples

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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)

KevinSee/qRfish documentation built on May 8, 2019, 4:50 p.m.