PomaCorr {POMA} | R Documentation

## Correlation Analysis

### Description

This function returns different correlation plots (correlogram and network plots) and a table with all pairwise correlations in the data.

### Usage

    PomaCorr(
      data,
      method = "pearson",
      shape = "square",
      type = "full",
      show_corr = FALSE,
      low = "#336B87",
      outline = "white",
      high = "#EA8620",
      label_size = 12,
      corr_type = "cor",
      coeff = 0.7
    )

### Arguments

`data` | A MSnSet object. First `pData` column must be the subject group/type.
`method` | Character indicating which correlation coefficient has to be computed. Options are "pearson" (default), "kendall" and "spearman".
`shape` | Character ingicating shape of correlogram. Options are "square" (default) and "circle".
`type` | Character indicating type of correlogram. Options are "full" (default), "lower" or "upper".
`show_corr` | Logical indicating if correlation coefficient for each pair of features should be plotted in correlogram or not (default = FALSE). Only recomended for a low number of features.
`low` | Colour for low end of the gradient in correlogram.
`outline` | Colour for the outline of the gradient in correlogram.
`high` | Colour for high end of the gradient in correlogram.
`label_size` | Numeric indicating label size in correlogram.
`corr_type` | Type of network to be made with correlation matrix. Options are "cor" (for global correlations) and "glasso" (for gaussian graphical model). Default is "cor". See `glasso` R package for the second option.
`coeff` | Numeric indicatin correlation coefficient. Edges with absolute weight below this value will be removed from the network. If "corr_type" is set to "glasso", this parameter indicates the regularization parameter for lasso (rho = 0 means no regularization). See `glasso::glasso()`.

### Value

A list with the results.

### Author(s)

Pol Castellano-Escuder

### References

Jerome Friedman, Trevor Hastie and Rob Tibshirani (2019). glasso: Graphical Lasso: Estimation of Gaussian Graphical Models. R package version 1.11. https://CRAN.R-project.org/package=glasso

### Examples

    data("st000284")

    # pearson correlation
    PomaCorr(st000284)$correlations
    PomaCorr(st000284)$corrplot

    # gaussian graphical model
    # library(ggraph)
    # PomaCorr(st000284, corr_type = "glasso")

[Package POMA version 1.2.0 Index]