View source: R/corr_coef_class.R

corr_coef | R Documentation |

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.

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

`alpha` |
(numeric) The p-value cutoff for determining significance. The default is |

`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 |

`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 |

`...` |
Additional slots and values passed to |

This object makes use of functionality from the following packages:

`stats`

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. |

R Core Team (2022).
*R: A Language and Environment for Statistical Computing*.
R Foundation for Statistical Computing, Vienna, Austria.
https://www.R-project.org/.

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)

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