plotVarExplained {BioMM} | R Documentation

## Plot data summary statistics

### Description

Plot data summary statistics in terms of the proportion of variance explained.

### Usage

    plotVarExplained(
      data,
      posF = TRUE,
      binarize = FALSE,
      core = MulticoreParam(),
      pathTitle = "GO pathways",
      fileName = NULL
    )

### Arguments

`data` | The input dataset (either data.frame or matrix). Rows are the samples, columns are the probes/genes, except that the first column is the label (the outcome).
`posF` | A logical value indicating if only positively outcome-associated features should be used. (Default: TRUE)
`binarize` | A logical value indicating if the individual features under investigation should be binarized. The default is FALSE, which provides the estimated class probabilities for each pathway-level feature. If TRUE, then the binary output is given for each feature.
`core` | The number of cores used for computation. (Default: 1)
`pathTitle` | A string indicating the name of pathway under investigation. This will be displayed as the name of y-axis.
`fileName` | The file name specified for the plot. If it is not NULL, then the plot will be generated. The plot will project the data on the first two components. (Default: 'R2explained.png')

### Value

An output image file with '.png' format.

### References

Yu, Guangchuang, et al. 'clusterProfiler: an R package for comparing biological themes among gene clusters.' Omics: a journal of integrative biology 16.5 (2012): 284-287.

Perlich, C., & Swirszcz, G. (2011). On cross-validation and stacking: Building seemingly predictive models on random data. ACM SIGKDD Explorations Newsletter, 12(2), 11-15.

[Package BioMM version 1.6.0 Index]