spp.est: Estimating Species Diversity
In fossil: Palaeoecological and Palaeogeographical Analysis Tools

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Estimate the diversity of a sample(s) using a number of species diversity estimators.

1	spp.est(x, rand = 10, abund = TRUE, counter = FALSE, max.est = 'all')

`x`	A vector, matrix or data frame with species as rows and locations/samples as columns
`rand`	The number of times to run the internal randomizations; default is set to 10
`abund`	If the data is abundance or presence/absence; default is set to TRUE for abundance
`counter`	Whether or not to provide a running total of progress of randomizations
`max.est`	The value to go up to for the analysis; default is set to the same as the total number of samples

This function will accept a vector, matrix or data frame of species by samples and return a large matrix with various species estimation values.

Returns a table with the following column names if abund=TRUE:

`N.obs`	Total sample size
`S.obs`	Number of observed species
`S.obs(+95%)`	95% upper confidence interval
`S.obs(-95%)`	95% lower confidence interval
`Chao1`	Chao Species Estimation
`Chao1(upper)`	95% upper confidence interval
`Chao1(lower)`	95% lower confidence interval
`ACE`	Abundance-based Coverage Estimator
`ACE(upper)`	95% upper confidence interval
`ACE(lower)`	95% lower confidence interval
`Jack1`	First Order Jacknife Estimator
`Jack1(upper)`	95% upper confidence interval
`Jack1(lower)`	95% lower confidence interval

Returns a table with the following column names if abund=FALSE:

`N.obs`	Total sample size
`S.obs`	Number of observed species
`S.obs(+95%)`	95% upper confidence interval
`S.obs(-95%)`	95% lower confidence interval
`Chao2`	Chao Species Estimation
`Chao2(upper)`	95% upper confidence interval
`Chao2(lower)`	95% lower confidence interval
`ICE`	Incidence-based Coverage Estimator
`ICE(upper)`	95% upper confidence interval
`ICE(lower)`	95% lower confidence interval
`Jack1`	First Order Jacknife Estimator
`Jack1(upper)`	95% upper confidence interval
`Jack1(lower)`	95% lower confidence interval

This function can be very long to run due to its iterative nature. The randomizations are initially set to 10 so the process will run relatively quickly, but a low value for randomizations will not give nicely smoothed curves.

Also, in some cases due to the nature of some of the functions, they provide no answer, such as is common with the Chao standard deviation. In this case, the Chao upper and lower bounds are simply 95% confidence intervals based on the actual Chao estimator.

Matthew Vavrek

The original idea for a program similar to this came from the extremely useful EstimateS program by Robert K. Colwell

Colwell, R.K. 2010. EstimateS: Statistical estimation of species richness and shared species from samples. Version 8.2. User's Guide and application published at: http://purl.oclc.org/estimates.

chao1, jack1, bootstrap

#abundance example with sample data set
data(fdata.mat)
spp.est(fdata.mat, abund = TRUE, counter = FALSE)

#occurrence example with sample data set
data(fdata.mat)
spp.est(fdata.mat, abund = FALSE, counter = FALSE)