Tests on Abundance Data

test_diversity(object, ...)

test_fit(object, ...)

# S4 method for CountMatrix
test_fit(object, simplify = FALSE, ...)

# S4 method for CountMatrix
test_diversity(object, adjust = "holm", ...)

## Arguments

object A $$m \times p$$ matrix of count data. Further arguments to be passed to internal methods. A logical scalar: should the result be simplified to a matrix? A character string specifying the method for adjusting $$p$$ values (see p.adjust).

## Value

If simplify is FALSE, returns a list (default), else returns a matrix.

## Details

The following methods are available:

test_diversity

Compare Shannon diversity between samples. This test produces two sided pairwise comparisons: it returns a matrix of adjusted $$p$$ values.

test_fit

The Frequency Increment Test (Feder et al. 2014). This test rejects neutrality if the distribution of normalized variant frequency increments exhibits a mean that deviates significantly from zero.

## References

Feder, A. F., Kryazhimskiy, S. & Plotkin, J. B. (2014). Identifying Signatures of Selection in Genetic Time Series. Genetics, 196(2), 509-522. DOI: 10.1534/genetics.113.158220.

Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. DOI: 10.1007/978-94-015-7358-0.

## Examples

## Shannon diversity test
merzbach_count <- as_count(merzbach)
div <- test_diversity(merzbach_count)

## Frequency Increment Test
## Coerce the merzbach dataset to a count matrix
## Keep only decoration types that have a maximum frequency of at least 50
keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50)
merzbach_count <- as_count(merzbach[, keep])

## The data are grouped by phase
## We use the row names as time coordinates (roman numerals)
set_dates(merzbach_count) <- rownames(merzbach)

fit <- test_fit(merzbach_count, simplify = TRUE)