associate {microbiome} | R Documentation

## Cross Correlation Wrapper

### Description

Cross-correlate columns of the input matrices.

### Usage

    associate(x, y = NULL, method = "spearman", p.adj.threshold = Inf,
      cth = NULL, order = FALSE, n.signif = 0, mode = "table",
      p.adj.method = "fdr", verbose = FALSE,
      filter.self.correlations = FALSE)

### Arguments

`x` | matrix (samples x features if annotation matrix)
`y` | matrix (samples x features if cross-correlated with annotations)
`method` | association method ('pearson', or 'spearman' for continuous; categorical for discrete)
`p.adj.threshold` | q-value threshold to include features
`cth` | correlation threshold to include features
`order` | order the results
`n.signif` | mininum number of significant correlations for each element
`mode` | Specify output format ('table' or 'matrix')
`p.adj.method` | p-value multiple testing correction method. One of the methods in p.adjust function ('BH' and others; see help(p.adjust)). Default: 'fdr'
`verbose` | verbose
`filter.self.correlations` | Filter out correlations between identical items.

### Details

As the method=categorical (discrete) association measure for nominal (no order for levels) variables we use Goodman and Kruskal tau based on r-bloggers.com/measuring-associations-between-non-numeric-variables/

### Value

List with cor, pval, pval.adjusted

### Author(s)

Contact: Leo Lahti microbiome-admin@googlegroups.com

### References

See citation('microbiome')

### Examples

    data(peerj32)
    d1 <- peerj32$microbes[1:20, 1:10]
    d2 <- peerj32$lipids[1:20,1:10]
    cc <- associate(d1, d2, method='pearson')

[Package microbiome version 1.6.0 Index]