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Correlation coefficient

Source: R/corr_coef_class.R
corr_coef.Rd

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.

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.

Details

This object makes use of functionality from the following packages:

  • stats

Inheritance

A corr_coef object inherits the following struct classes:

[corr_coef] >> [model] >> [struct_class]

References

R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Examples

M = corr_coef(
      alpha = 0.05,
      mtc = "fdr",
      factor_names = "V1",
      method = "spearman")

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)
#> Warning: Cannot compute exact p-value with ties
#> Warning: Cannot compute exact p-value with ties
#> Warning: Cannot compute exact p-value with ties
#> Warning: Cannot compute exact p-value with ties
#> Warning: Cannot compute exact p-value with ties
#> Warning: Cannot compute exact p-value with ties
#> Warning: Cannot compute exact p-value with ties
#> Warning: Cannot compute exact p-value with ties
#> Warning: Cannot compute exact p-value with ties
#> Warning: Cannot compute exact p-value with ties
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion