`R/richness_breakaway_nof1.R`

`breakaway_nof1.Rd`

This function permits estimation of total diversity based on a sample
frequency count table. Unlike `breakaway`

, it does not require
an input for the number of species observed once, making it an excellent
exploratory tool for microbial ecologists who believe that their sample may
contain spurious singletons. The underlying estimation procedure is similar
to that of `breakaway`

and is outlined in Willis & Bunge (2014).
The diversity estimate, standard error, estimated model coefficients and
plot of the fitted model are returned.

breakaway_nof1( input_data, output = NULL, plot = NULL, answers = NULL, print = NULL )

input_data | An input type that can be processed by |
---|---|

output | Deprecated; only for backwards compatibility |

plot | Deprecated; only for backwards compatibility |

answers | Deprecated; only for backwards compatibility |

Deprecated; only for backwards compatibility |

An object of class `alpha_estimate`

A category representing algorithm behaviour.
`code=1` indicates no nonlinear models converged and the transformed
WLRM diversity estimate of Rocchetti et. al. (2011) is returned.
`code=2` indicates that the iteratively reweighted model converged and
was returned. `code=3` indicates that iterative reweighting did not
converge but a model based on a simplified variance structure was returned
(in this case, the variance of the frequency ratios is assumed to be
proportional to the denominator frequency index). Please peruse your fitted
model before using your diversity estimate.

The ``name'' of the selected model. The first integer represents the numerator polynomial degree and the second integer represents the denominator polynomial degree. See Willis & Bunge (2014) for details.

Estimated model parameters and standard errors.

The estimate of total (observed plus unobserved) diversity.

The standard error in the diversity estimate.

The chosen nonlinear model for frequency ratios.

It is common for microbial ecologists to believe that their dataset
contains false diversity. This often arises because sequencing errors result
in classifying already observed organisms as new organisms.
`breakaway_nof1` was developed as an exploratory tool in this case.
Practitioners can run `breakaway` on their dataset including the
singletons, and `breakaway_nof1` on their dataset excluding the
singletons, and assess if the estimated levels of diversity are very
different. Great disparity may provide evidence of an inflated singleton
count, or at the very least, that `breakaway` is especially sensitive
to the number of rare species observed. Note that `breakaway_nof1` may
be less stable than `breakaway` due to predicting based on a reduced
dataset, and have greater standard errors.

Willis, A. (2015). Species richness estimation with high
diversity but spurious singletons. *arXiv.*

Willis, A. and Bunge, J. (2015). Estimating diversity via frequency ratios.
*Biometrics.*

Amy Willis

breakaway_nof1(apples[-1, ]) #> Estimate of richness from method breakaway_nof1: #> Estimate is 1500 #> with standard error 1341.35 #> Confidence interval: (725, 139033) #> breakaway_nof1(apples[-1, ], plot = FALSE, output = FALSE, answers = TRUE) #> Estimate of richness from method breakaway_nof1: #> Estimate is 1500 #> with standard error 1341.35 #> Confidence interval: (725, 139033) #>