corr_coef {structToolbox} | R Documentation

## Correlation coefficient

### Description

The correlation between features and a set of continuous factor are calculated. Multiple-test corrected p-values are used to indicate whether the computed coefficients may have occurred by chance.

### Usage

    corr_coef(alpha = 0.05, mtc = "fdr", factor_names, method = "spearman", ...)

### Arguments

`alpha` | (numeric) The p-value cutoff for determining significance. The default is `0.05`.
`mtc` | (character) Multiple test correction method. Allowed values are limited to the following: `"bonferroni"`: Bonferroni correction in which the p-values are multiplied by the number of comparisons. `"fdr"`: Benjamini and Hochberg False Discovery Rate correction. `"none"`: No correction. The default is `"fdr"`.
`factor_names` | (character) The name of sample meta column(s) to use.
`method` | (character) Type of correlation. Allowed values are limited to the following: `"kendall"`: Kendall's tau is computed. `"pearson"`: Pearson product moment correlation is computed. `"spearman"`: Spearman's rho statistic is computed. The default is `"spearman"`.
`...` | Additional slots and values passed to `struct_class`.

### Details

This object makes use of functionality from the following packages:

- `stats`

### Value

A `corr_coef` object with the following `output` slots:

`coeff` | (data.frame) The value of the calculate statistics which is converted to a p-value when compared to a t-distribution.
`p_value` | (data.frame) The probability of observing the calculated statistic if the null hypothesis is true.
`significant` | (data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.

### References

R Core Team (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

### Examples

    D = MTBLS79_DatasetExperiment(filtered=TRUE)

    # subset for this example
    D = D[,1:10]

    # convert to numeric for this example
    D$sample_meta$sample_order=as.numeric(D$sample_meta$run_order)
    D$sample_meta$sample_rep=as.numeric(D$sample_meta$Sample_Rep)

    M = corr_coef(factor_names=c('sample_order','sample_rep'))
    M = model_apply(M,D)

[Package structToolbox version 1.6.1 Index]