spp.est {fossil} | R Documentation |
Estimate the diversity of a sample(s) using a number of species diversity estimators.
spp.est(x, rand = 10, abund = TRUE, counter = TRUE)
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 |
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 |
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.
Matthew Vavrek
The original idea for a program similar to this came from the extremely useful EstimateS program by Robert K. Colwell: newline Colwell, R. K. 2005. EstimateS: Statistical estimation of species richness and shared species from samples. Version 7.5. User's Guide and application published at: http://purl.oclc.org/estimates.
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