R/print.ChaoSpecies.R

In JohnsonHsieh/SpadeR: Species Prediction and Diversity Estimation with R

print.ChaoSpecies <- function(x, ...){
	cat('\n(1) BASIC DATA INFORMATION:\n\n')
	print(x$Basic.Data.Information)
	cat('\n')
	if(names(x)[2]=="Rare.Species.Group"){
		print(x$Rare.Species.Group)
		cat('\n')
		cat('\n(2) SPECIES RICHNESS ESTIMATORS TABLE:\n\n')
		print(x$Species.Table)
		cat('\n')
		cat('\n(3)DESCRIPTION OF ESTIMATORS/MODELS:

Homogenous Model: This model assumes that all species have the same discovery probabilities. See Eq.(2.3) of Chao and Lee (1992) or Eq. (2.1) of Chao et al. (2000).

Homogenous (MLE): An approximate maximum likelihood estimate under homogenous model. See Eq.(1.1) and Eq.(1.2) of Chao and Lee (1992).

Chao1 (Chao, 1984): This approach uses the numbers of singletons and doubletons to estimate the number of missing species because missing species information is mostly concentrated on those low frequency counts; see Chao (1984), Shen, Chao and Lin (2003) and Chao, Shen and Hwang (2006).

Chao1-bc: a bias-corrected form for the Chao1; see Chao (2005).

iChao1: an improved Chao1 estimtor; see Chiu et al. (2014)

ACE (Abundance-based Coverage Estimator): A non-parametric estimator proposed by Chao and Lee (1992) and Chao, Ma and Yang (1993).  The observed species are separated as rare and abundant groups; only the rare group is used to estimate the number of missing species. The estimated CV is used to characterize the degree of heterogeneity among species discovery probabilities.  See Eq.(2.14) in Chao and Lee (1992) or Eq.(2.2) of Chao et al. (2000).

ACE-1: A modified ACE for highly-heterogeneous communities. See Eq.(2.15) in Chao and Lee (1992).

1st order jackknife: It uses the number of singletons to estimate the number of missing species; see Burnham and Overton (1978).

2nd order jackknife: It uses the numbers of singletons and doubletons to estimate the number of missing species; see Burnham and Overton (1978).

95% Confidence interval: A log-transformation is used so that the lower bound of the resulting interval is at least the number of observed species. See Chao (1987).
')}
	if(names(x)[2]!="Rare.Species.Group"){
		print(x$Infreq.Species.Group)
		cat('\n')
		cat('\n(2) SPECIES RICHNESS ESTIMATORS TABLE:\n\n')
		print(x$Species.Table)
		cat('\n')
		cat('\n(3)  DESCRIPTION OF ESTIMATORS/MODELS:

Homogeneous Model: This model assumes that all species have the same detection probabilities. See Eq.(3.2) of Lee and Chao (1994).

Chao2 (Chao, 1987): This approach uses the frequencies of uniques and duplicates to estimate the number of missing species; see Chao (1987).
     
Chao2-bc: a bias-corrected form for the Chao2; see Chao (2005).
  
iChao2: an improved Chao2 estimator; see Chiu et al. (2014)

Model(h) (ICE: Incidence-based Coverage Estimator): Model(h) assumes that the detection probabilities are heterogeneous among species. The estimator given here is an improved version of Eq.(3.18) in Lee and Chao (1994) by using an improved estimated sample coverage given in Shen (2003) and the SPADE User Guide; see Eq.(3.23) in Lee and Chao (1994) for the estimated squared CV.

Model(h)-1 (or ICE-1):  A modified ICE for highly-heterogeneous cases.

1st order jackknife: It uses the frequency of uniques to estimate the number of missing species; see Burnham and Overton (1978).

2nd order jackknife: It uses the frequencies of uniques and duplicates to estimate the number of missing species; see Burnham and Overton (1978).

95% Confidence interval: A log-transformation is used so that the lower bound of the resulting interval is at least the number of observed species. See Chao (1987).
')
	}
}