R/richness_breakaway_nof1.R
breakaway_nof1.RdThis 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) #>