Description Usage Arguments Examples
View source: R/univariate_feature_selection.R
This function computes p-values for features of a single data frame or a list of data frames using, glm, lmer and glmer functions
1 | univariate_feature_selection(data_object)
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data_object |
argument is the output produced by as.MLinput function, which contains a single x data frame or a list of x data frames, a y data frames and attributes |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | dontrun{
library(peppuR)
library(missForest)
library(mice)
data("single_source")
data("multi_source")
x_multi = multi_source$X
y_multi = multi_source$Y
x_single = single_source$X
y_single = single_source$Y
sample_cname = "ID"
outcome_cname = "Group"
pair_cname = "paircol"
result = as.MLinput(X = x_single, Y = y_single, categorical_features = T , sample_cname = sample_cname, outcome_cname = outcome_cname, pair_cname = pair_cname)
result2 = as.MLinput(X = x_multi, Y = y_multi, categorical_features = T, sample_cname = sample_cname, outcome_cname = outcome_cname, pair_cname = pair_cname)
imputed_res = impute_missing(result, method = "randomforest")
imputed_res2 = impute_missing(result2, method = "randomforest")
ufs_result = univariate_feature_selection(imputed_res)
ufs_result2 = univariate_feature_selection(imputed_res2)
}
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