SpadeR：多样性指数计算的全家桶

1install.packages("SpadeR")
2library(SpadeR)

 1data(ChaoSpeciesData)
 2ChaoSpecies(ChaoSpeciesData$Abu,"abundance",k=10,conf=0.95)
 3#k为稀有物种的丰度阈值，用于计算ACE和ICE。conf为置信区间。
 4#结果包括三部分。（1）是基本信息，（2）为各种多样性指标，（3）为各种指标的说明。
 5(1) BASIC DATA INFORMATION:
 6
 7                                         Variable Value
 8    Sample size                                 n  1996
 9    Number of observed species                  D    25
10    Coverage estimate for entire dataset        C 0.998
11    CV for entire dataset                      CV 1.916
12    Cut-off point                               k    10
13
14                                                      Variable Value
15    Number of observed individuals for rare group       n_rare    53
16    Number of observed species for rare group           D_rare    11
17    Estimate of the sample coverage for rare group      C_rare 0.943
18    Estimate of CV for rare group in ACE               CV_rare 0.629
19    Estimate of CV1 for rare group in ACE-1           CV1_rare  0.74
20    Number of observed individuals for abundant group   n_abun  1943
21    Number of observed species for abundant group       D_abun    14
22
23NULL
24
25
26(2) SPECIES RICHNESS ESTIMATORS TABLE:
27
28                              Estimate  s.e. 95%Lower 95%Upper
29    Homogeneous Model           25.660 0.954   25.082   30.295
30    Homogeneous (MLE)           25.000 0.975   25.000   28.500
31    Chao1 (Chao, 1984)          27.249 3.394   25.266   44.030
32    Chao1-bc                    25.999 1.817   25.094   35.673
33    iChao1 (Chiu et al. 2014)   27.249 3.394   25.266   44.030
34    ACE (Chao & Lee, 1992)      26.920 2.367   25.292   37.639
35    ACE-1 (Chao & Lee, 1992)    27.399 3.163   25.336   42.153
36    1st order jackknife         27.998 2.449   25.739   37.171
37    2nd order jackknife         28.998 4.240   25.730   46.915
38
39
40(3) DESCRIPTION OF ESTIMATORS/MODELS:
41
42Homogeneous Model: This model assumes that all species have the same incidence or detection probabilities. See Eq. (3.2) of Lee and Chao (1994) or Eq. (12a) in Chao and Chiu (2016b).
43
44Chao2 (Chao, 1987): This approach uses the frequencies of uniques and duplicates to estimate the number of undetected species; see Chao (1987) or Eq. (11a) in Chao and Chiu (2016b).
45
46Chao2-bc: A bias-corrected form for the Chao2 estimator; see Chao (2005).
47
48iChao2: An improved Chao2 estimator; see Chiu et al. (2014).
49
50ICE (Incidence-based Coverage Estimator): A non-parametric estimator originally proposed by Lee and Chao (1994) in the context of capture-recapture data analysis. The observed species are separated as frequent and infrequent species groups;>in the infrequent group are used to estimate the number of undetected species. The estimated CV for species in the infrequent group characterizes the degree of heterogeneity among species incidence probabilities. See Eq. (12b) of Chao and Chiu (2016b), which is an improved version of Eq. (3.18) in Lee and Chao (1994). This model is also called Model(h) in capture-recapture literature where h denotes "heterogeneity".
51
52ICE-1: A modified ICE for highly-heterogeneous cases.
53
541st order jackknife: It uses the frequency of uniques to estimate the number of undetected species; see Burnham and Overton (1978).
55
562nd order jackknife: It uses the frequencies of uniques and duplicates to estimate the number of undetected species; see Burnham and Overton (1978).
57
5895% Confidence interval: A log-transformation is used for all estimators so that the lower bound of the resulting interval is at least the number of observed species. See Chao (1987).

 1data(DiversityData)
 2Diversity(DiversityData$Abu,"abundance",q=c(0,0.5,1,1.5,2))
 3#q为多样性阶数
 4#结果分5部分
 5(1) BASIC DATA INFORMATION:
 6                               Variable Value
 7    Sample size                       n   557
 8    Number of observed species        D    69
 9    Estimated sample coverage         C 0.957
10    Estimated CV                     CV 2.237
11
12(2) ESTIMATION OF SPECIES RICHNESS (DIVERSITY OF ORDER 0):
13
14                             Estimate s.e. 95%Lower 95%Upper
15    Chao1 (Chao, 1984)          104.9 20.3     81.8    169.9
16    Chao1-bc                     99.6 16.9     80.1    153.2
17    iChao1                      113.9 12.7     95.1    146.4
18    ACE (Chao & Lee, 1992)       92.1 10.2     79.1    121.8
19    ACE-1 (Chao & Lee, 1992)    100.4 15.7     81.4    148.1
20
21        Descriptions of richness estimators (See Species Part)
22
23(3a) SHANNON ENTROPY:
24
25                        Estimate  s.e. 95%Lower 95%Upper
26     MLE                   3.193 0.065    3.067    3.320
27     Jackknife             3.280 0.070    3.143    3.417
28     Chao & Shen           3.308 0.071    3.168    3.447
29     Chao et al. (2013)    3.293 0.072    3.152    3.433
30
31        MLE: empirical or observed entropy.
32        Jackknife: see Zahl (1977).
33        Chao & Shen: based>2003).
34        see Chao and Shen (2003).
35          Chao et al. (2013): A nearly optimal estimator of Shannon entropy; see Chao et al. (2013).
36          Estimated standard error is computed based>37
38(3b) SHANNON DIVERSITY (EXPONENTIAL OF SHANNON ENTROPY):
39
40                        Estimate  s.e. 95%Lower 95%Upper
41     MLE                  24.372 1.539   21.355   27.388
42     Jackknife            26.573 1.805   23.035   30.111
43     Chao & Shen          27.320 1.895   23.606   31.034
44     Chao et al. (2013)   26.917 1.870   23.251   30.583
45
46(4a) SIMPSON CONCENTRATION INDEX:
47
48          Estimate    s.e. 95%Lower 95%Upper
49     MVUE  0.08328 0.00714  0.06929  0.09728
50     MLE   0.08493 0.00713  0.07096  0.09890
51
52        MVUE: minimum variance unbiased estimator; see Eq. (2.27) of Magurran (1988).
53        MLE: maximum likelihood estimator or empirical index; see Eq. (2.26) of Magurran (1988).
54
55(4b) SIMPSON DIVERSITY (INVERSE OF SIMPSON CONCENTRATION):
56
57          Estimate    s.e. 95%Lower 95%Upper
58     MVUE 12.00729 0.96804 10.10992 13.90465
59     MLE  11.77460 0.92959  9.95262 13.59659
60
61(5) CHAO AND JOST (2015) ESTIMATES OF HILL NUMBERS 
62
63         q ChaoJost 95%Lower 95%Upper Empirical 95%Lower 95%Upper
64     1 0.0  104.935    7.476  202.394    69.000   61.625   76.375
65     2 0.5   53.093   38.499   67.687    41.565   37.267   45.863
66     3 1.0   26.917   23.475   30.359    24.372   21.420   27.324
67     4 1.5   16.411   13.936   18.886    15.806   13.481   18.131
68     5 2.0   12.007   10.006   14.008    11.775    9.854   13.696
69
70        ChaoJost: diversity profile estimator derived by Chao and Jost (2015).
71          Empirical: maximum likelihood estimator (observed index).

1data(ChaoSharedData)
2ChaoShared(ChaoSharedData$Abu,"abundance",se=TRUE,nboot=200,conf=0.95)
3#结果太多不放了

1data(SimilarityPairData)
2SimilarityPair(SimilarityPairData$Abu,"abundance",nboot=200)
3#结果也很丰富，包括了除Jaccard and Sorensen以外其他多种指标