PomaEDA {POMA} | R Documentation

## Automatic Exploratory Data Analysis PDF Report

### Description

This function automatically generates a PDF report with different exploratory plots and tables from an MSnSet object.

### Usage

    PomaEDA(
      data,
      imputation = "knn",
      normalization = "log_pareto",
      clean_outliers = TRUE,
      coeff_outliers = 1.5,
      username = "Username"
    )

### Arguments

`data` | A MSnSet object. First `pData` column must be the subject group/type.
`imputation` | Imputation method. Options are "none", "half_min", "median", "mean", "min" and "knn" (default). If "none", all missing values will be replaced by zero.
`normalization` | Normalization method. Options are "none", "auto_scaling", "level_scaling", "log_scaling", "log_transformation", "vast_scaling" and "log_pareto" (default).
`clean_outliers` | Logical. If it's set to TRUE, outliers will be removed from EDA.
`coeff_outliers` | This value corresponds to the classical 1.5 in Q3 + 1.5*IQR formula to detect outliers. By changing this value, the permissiveness in outlier detection will change.
`username` | This name will be included as a report subtitle.

### Value

An exploratory data analysis PDF report.

### Author(s)

Pol Castellano-Escuder

[Package POMA version 1.0.0 Index]