PomaEDA: Automatic Exploratory Data Analysis PDF Report

Description Usage Arguments Value Author(s)

View source: R/PomaEDA.R

Description

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

Usage

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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


pcastellanoescuder/POMA documentation built on May 14, 2021, 11:07 p.m.