estimateAbundance {alakazam} R Documentation

## Estimates the complete clonal relative abundance distribution

### Description

`estimateAbundance` estimates the complete clonal relative abundance distribution and confidence intervals on clone sizes using bootstrapping.

### Usage

```estimateAbundance(
data,
clone = "clone_id",
copy = NULL,
group = NULL,
min_n = 30,
max_n = NULL,
uniform = TRUE,
ci = 0.95,
nboot = 200,
progress = FALSE
)
```

### Arguments

 `data` data.frame with Change-O style columns containing clonal assignments. `clone` name of the `data` column containing clone identifiers. `copy` name of the `data` column containing copy numbers for each sequence. If `copy=NULL` (the default), then clone abundance is determined by the number of sequences. If a `copy` column is specified, then clone abundances is determined by the sum of copy numbers within each clonal group. `group` name of the `data` column containing group identifiers. If `NULL` then no grouping is performed and the `group` column of the output will contain the value `NA` for each row. `min_n` minimum number of observations to sample. A group with less observations than the minimum is excluded. `max_n` maximum number of observations to sample. If `NULL` then no maximum is set. `uniform` if `TRUE` then uniformly resample each group to the same number of observations. If `FALSE` then allow each group to be resampled to its original size or, if specified, `max_size`. `ci` confidence interval to calculate; the value must be between 0 and 1. `nboot` number of bootstrap realizations to generate. `progress` if `TRUE` show a progress bar.

### Value

A AbundanceCurve object summarizing the abundances.

### References

1. Chao A. Nonparametric Estimation of the Number of Classes in a Population. Scand J Stat. 1984 11, 265270.

2. Chao A, et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol Monogr. 2014 84:45-67.

3. Chao A, et al. Unveiling the species-rank abundance distribution by generalizing the Good-Turing sample coverage theory. Ecology. 2015 96, 11891201.

```abund <- estimateAbundance(ExampleDb, group="sample_id", nboot=100)