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ml_model

Source: R/ml_model.R
ml_model.Rd

This function constructs the machine learning model, the output object can be used as input for plotting and further analyses.

Usage

ml_model(
  processed_se,
  char = "none",
  transform = "log10",
  ranking_method = "Random_forest",
  ml_method = "Random_forest",
  split_prop = 0.7,
  nfold = 10,
  alpha = NULL
)

Arguments

processed_se

A SummarizedExperiment object constructed by as_summarized_experiment and processed by data_process.

char

Character list. Lipid characteristics selected from the ml_char list returned by list_lipid_char. Select 'none' to exclude all lipid characteristics.

transform

Character. Method for data transformation. Allowed methods include "none", "log10", "square", "cube". Select 'none' to skip data transformation. Default is 'log10'.

ranking_method

Character. The ranking method to be computed. Allowed methods include 'p_value', 'pvalue_FC', 'ROC', 'Random_forest', 'SVM', 'Lasso', 'Ridge', 'ElasticNet'. Default is 'Random_forest'.

ml_method

Character. The machine learning method to be computed. Allowed methods include 'Random_forest', 'SVM', 'Lasso', 'Ridge', 'ElasticNet', 'xgboost'. Default is 'Random_forest'.

split_prop

Numeric. The proportion of data to be retained for modeling/analysis. The range is 0.5 to 1. Default is 0.7.

nfold

Numeric. The number of fold that the original dataset is randomly partitioned into equal-sized subsamples. Must be a positive interger. Default is 10.

alpha

Numeric. The alpha value between 0 and 1 when choosing "ElasticNet" as ranking_method or ml_method. 0 is for Ridge and 1 is for Lasso. If "ElasticNet" is not selected as either ranking_method or ml_method, set the value to NULL.

Value

Return a SummarizedExperiment object containing analysis results.

Examples

data("ml_sub")
processed_se <- data_process(
    ml_sub, exclude_missing=TRUE, exclude_missing_pct=70,
    replace_na_method='min', replace_na_method_ref=0.5,
    normalization='Percentage', transform='log10')
char_list <- list_lipid_char(processed_se)
ml_se <- ml_model(
    processed_se, char=c("class","Total.DB"), transform='log10',
    ranking_method='Random_forest', ml_method='Random_forest', split_prop=0.7,
    nfold=5, alpha=NULL)
#> Processing CV fold1
#> Registered S3 methods overwritten by 'proxy':
#>   method               from    
#>   print.registry_field registry
#>   print.registry_entry registry
#> Processing CV fold2
#> Processing CV fold3
#> Processing CV fold4
#> Processing CV fold5