parfossil (version 0.2.0)

par.spp.est: A parallelized function for estimating species diversity

Description

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

Usage

par.spp.est(x, rand = 10, abund = TRUE, counter = FALSE)

Arguments

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

Value

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

Details

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

References

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.

See Also

chao1, jack1, bootstrap

Examples

## Not run: 
# #comparison of run times between the serial and parallel versions on the estimator
# #please note that this example is designed for a multicore OS X or Linux computer
# library(doMC)
# registerDoMC()
# data(fdata.mat)
# system.time({spp.est(fdata.mat, rand = 100, abund = TRUE, counter = FALSE)})
# system.time({par.spp.est(fdata.mat, rand = 100, abund = TRUE, counter = FALSE)})
# 
# #this example is for a multicore Windows computer, but HAS NOT BEEN TESTED
# library(doSNOW)
# library(snow)
# cl <- makeCluster(c("localhost","localhost"), type = "SOCK")
# registerDoSNOW(c1)
# data(fdata.mat)
# system.time({spp.est(fdata.mat, rand = 100, abund = TRUE, counter = FALSE)})
# system.time({par.spp.est(fdata.mat, rand = 100, abund = TRUE, counter = FALSE)})
# stopCluster(cl)
# 
# ## End(Not run)