PomaUnivariate {POMA} | R Documentation

## Univariate Statistical Methods for Mass Spectrometry Data

### Description

PomaUnivariate() allows users to perform different univariate statistical analysis on MS data.

### Usage

    PomaUnivariate(
      data,
      covariates = FALSE,
      method = "ttest",
      paired = FALSE,
      var_equal = FALSE,
      adjust = "fdr"
    )

### Arguments

`data` | A MSnSet object. First `pData` column must be the subject group/type.
`covariates` | Logical. If it's set to `TRUE` all metadata variables stored in `pData` will be used as covariables. Default = FALSE.
`method` | Univariate statistical method. Options are: "ttest", "anova", "mann" and "kruskal".
`paired` | Logical that indicates if the data is paired or not.
`var_equal` | Logical that indicates if the data variance is equal or not.
`adjust` | Multiple comparisons correction method. Options are: "fdr", "holm", "hochberg", "hommel", "bonferroni", "BH" and "BY".

### Value

A data frame with results.

### Author(s)

Pol Castellano-Escuder

### Examples

    data("st000336")
    data("st000284")

    # ttest
    st000336 %>%
      PomaImpute() %>%
      PomaNorm() %>%
      PomaOutliers() %>%
      PomaUnivariate(method = "ttest")

    # ANOVA
    st000284 %>%
      PomaImpute() %>%
      PomaNorm() %>%
      PomaOutliers() %>%
      PomaUnivariate(method = "anova")

[Package POMA version 1.2.0 Index]