beta.sample.abund: Resampling abundance-based multiple-site dissimilarity for n...
In betapart: Partitioning Beta Diversity into Turnover and Nestedness Components
Description
Usage
Arguments
Value
Author(s)
References
See Also
Examples
Description
Resamples the 3 abundance-based multiple-site dissimilarities (balanced variation fraction,abundance-gradient fraction, and overall
dissimilarity) for a subset of sites of the original data frame.
Usage
1
beta.sample.abund(x, index.family="bray", sites = nrow(x), samples = 1)
Arguments
x
data frame, where rows are sites and columns are species
index.family
family of dissimilarity indices, partial match of "bray" or "ruzicka".
sites
number of sites for which multiple-site dissimilarities will be computed. If not specified, default is all sites.
samples
number of repetitions. If not specified, default is 1.
Value
The function returns a list with a dataframe with the resampled 3 multiple-site dissimilarities
(balanced variation fraction, abundance-gradient fraction and overall dissimilarity; see beta.multi.abund),
a vector with the respective means and a vector with the respective standard deviation.
For index.family="bray":
sampled.values
dataframe containing beta.BRAY.BAL, beta.BRAY.GRA and beta.BRAY for all samples
mean.values
vector containing the mean values of beta.BRAY.BAL, beta.BRAY.GRA and beta.BRAY among samples
sd.values
vector containing the sd values of beta.BRAY.BAL, beta.BRAY.GRA and beta.BRAY among samples
For index.family="ruzicka":
sampled.values
dataframe containing beta.RUZ.BAL, beta.RUZ.GRA and beta.RUZ for all samples
mean.values
vector containing the mean values of beta.RUZ.BAL, beta.RUZ.GRA and beta.RUZ among samples
sd.values
vector containing the sd values of beta.RUZ.BAL, beta.RUZ.GRA and beta.RUZ among samples
Author(s)
Andrés Baselga
References
Baselga, A. 2017. Partitioning abundance-based multiple-site dissimilarity into components: balanced variation in abundance and abundance gradients. Methods in Ecology and Evolution 8: 799-808
See Also
Examples
1
2
3
require(vegan)
data(BCI)
beta.sample.abund(BCI, index.family="bray", sites=10, samples=100)
Example output
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-4
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$sampled.values
beta.BRAY.BAL beta.BRAY.GRA beta.BRAY
1 0.7100309 0.012860120 0.7228910
2 0.7009784 0.020416914 0.7213953
3 0.7250804 0.018483723 0.7435641
4 0.7227855 0.022657829 0.7454434
5 0.7038971 0.028187790 0.7320849
6 0.7147075 0.019953092 0.7346606
7 0.7077898 0.019170293 0.7269601
8 0.7293381 0.021413513 0.7507516
9 0.7248347 0.015966127 0.7408008
10 0.7013024 0.027459294 0.7287616
11 0.7180575 0.027560993 0.7456185
12 0.7254695 0.016433999 0.7419035
13 0.7208756 0.028004450 0.7488801
14 0.7213546 0.021782287 0.7431369
15 0.7091738 0.023904412 0.7330782
16 0.6992244 0.030988222 0.7302126
17 0.7170110 0.015812776 0.7328238
18 0.7329935 0.026391899 0.7593854
19 0.7397578 0.025585865 0.7653437
20 0.7162948 0.020391622 0.7366864
21 0.7177122 0.037971312 0.7556835
22 0.7360014 0.036717171 0.7727186
23 0.6839098 0.033967820 0.7178776
24 0.7367818 0.016643977 0.7534258
25 0.7116417 0.041417312 0.7530590
26 0.7051109 0.015300532 0.7204114
27 0.7498262 0.029400049 0.7792263
28 0.6988924 0.022599079 0.7214915
29 0.7162162 0.013489523 0.7297057
30 0.7422486 0.025897612 0.7681462
31 0.7483216 0.028679568 0.7770012
32 0.6809735 0.019481608 0.7004551
33 0.7254739 0.022253201 0.7477271
34 0.7327024 0.032298936 0.7650014
35 0.7072375 0.039800984 0.7470384
36 0.7355042 0.015436511 0.7509407
37 0.7150838 0.024293906 0.7393777
38 0.7478806 0.023868087 0.7717487
39 0.7238175 0.023407686 0.7472252
40 0.7219966 0.036216720 0.7582133
41 0.6879977 0.025664215 0.7136619
42 0.7266412 0.011831847 0.7384730
43 0.6982077 0.024476864 0.7226846
44 0.7058529 0.031447703 0.7373006
45 0.7010385 0.021354392 0.7223929
46 0.7374615 0.033434352 0.7708959
47 0.7242720 0.015151961 0.7394240
48 0.7093822 0.021116109 0.7304983
49 0.7166532 0.014567029 0.7312203
50 0.7196842 0.016057562 0.7357418
51 0.7200293 0.015352645 0.7353820
52 0.7046547 0.033057177 0.7377119
53 0.7033134 0.035933131 0.7392465
54 0.7266439 0.024592551 0.7512365
55 0.7164537 0.011654572 0.7281082
56 0.7514973 0.020775510 0.7722728
57 0.7143798 0.015616683 0.7299964
58 0.7197729 0.010779804 0.7305527
59 0.7488523 0.020978600 0.7698309
60 0.7413837 0.025912866 0.7672966
61 0.6997322 0.016529252 0.7162614
62 0.7118371 0.037245542 0.7490827
63 0.7489347 0.030464714 0.7793994
64 0.7042614 0.025237213 0.7294986
65 0.7266451 0.009009415 0.7356545
66 0.6773419 0.035352508 0.7126944
67 0.7551003 0.030587098 0.7856874
68 0.7293253 0.012963585 0.7422888
69 0.7331272 0.023880634 0.7570078
70 0.7310474 0.022874154 0.7539216
71 0.7313459 0.013742128 0.7450881
72 0.6934840 0.023234563 0.7167186
73 0.7063818 0.029796273 0.7361781
74 0.7283268 0.027136303 0.7554631
75 0.7265105 0.023212643 0.7497231
76 0.7222900 0.020807755 0.7430978
77 0.7342908 0.024900261 0.7591911
78 0.7210031 0.040042390 0.7610455
79 0.7179771 0.021160744 0.7391379
80 0.7316718 0.015510765 0.7471826
81 0.6967779 0.033668367 0.7304463
82 0.7325095 0.029187076 0.7616966
83 0.7078965 0.027325810 0.7352223
84 0.7227051 0.021388665 0.7440938
85 0.7279347 0.013508556 0.7414433
86 0.7295308 0.032960598 0.7624914
87 0.7203869 0.012119943 0.7325069
88 0.7380805 0.018113051 0.7561936
89 0.7458683 0.024947146 0.7708155
90 0.6921346 0.029382135 0.7215167
91 0.7039594 0.023121895 0.7270813
92 0.7265955 0.010942778 0.7375382
93 0.7257014 0.031118283 0.7568196
94 0.7354516 0.032387335 0.7678390
95 0.7101378 0.027979674 0.7381175
96 0.7249035 0.012412709 0.7373162
97 0.7151136 0.016989273 0.7321028
98 0.7004286 0.019421497 0.7198501
99 0.7102355 0.020976090 0.7312116
100 0.7076531 0.021909110 0.7295622
$mean.values
beta.BRAY.BAL beta.BRAY.GRA beta.BRAY
0.7195710 0.0235587 0.7431297
$sd.values
beta.BRAY.BAL beta.BRAY.GRA beta.BRAY
0.016310987 0.007624009 0.017326379
betapart documentation built on May 2, 2019, 6:10 p.m.
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Description Usage Arguments Value Author(s) References See Also Examples
Description
Resamples the 3 abundance-based multiple-site dissimilarities (balanced variation fraction,abundance-gradient fraction, and overall dissimilarity) for a subset of sites of the original data frame.
Usage
1 | beta.sample.abund(x, index.family="bray", sites = nrow(x), samples = 1)
|
Arguments
x |
data frame, where rows are sites and columns are species |
index.family |
family of dissimilarity indices, partial match of |
sites |
number of sites for which multiple-site dissimilarities will be computed. If not specified, default is all sites. |
samples |
number of repetitions. If not specified, default is 1. |
Value
The function returns a list with a dataframe with the resampled 3 multiple-site dissimilarities
(balanced variation fraction, abundance-gradient fraction and overall dissimilarity; see beta.multi.abund),
a vector with the respective means and a vector with the respective standard deviation.
For index.family="bray":
sampled.values |
dataframe containing beta.BRAY.BAL, beta.BRAY.GRA and beta.BRAY for all samples |
mean.values |
vector containing the mean values of beta.BRAY.BAL, beta.BRAY.GRA and beta.BRAY among samples |
sd.values |
vector containing the sd values of beta.BRAY.BAL, beta.BRAY.GRA and beta.BRAY among samples |
For index.family="ruzicka":
sampled.values |
dataframe containing beta.RUZ.BAL, beta.RUZ.GRA and beta.RUZ for all samples |
mean.values |
vector containing the mean values of beta.RUZ.BAL, beta.RUZ.GRA and beta.RUZ among samples |
sd.values |
vector containing the sd values of beta.RUZ.BAL, beta.RUZ.GRA and beta.RUZ among samples |
Author(s)
Andrés Baselga
References
Baselga, A. 2017. Partitioning abundance-based multiple-site dissimilarity into components: balanced variation in abundance and abundance gradients. Methods in Ecology and Evolution 8: 799-808
See Also
Examples
1 2 3 | require(vegan)
data(BCI)
beta.sample.abund(BCI, index.family="bray", sites=10, samples=100)
|
Example output
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-4
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$sampled.values
beta.BRAY.BAL beta.BRAY.GRA beta.BRAY
1 0.7100309 0.012860120 0.7228910
2 0.7009784 0.020416914 0.7213953
3 0.7250804 0.018483723 0.7435641
4 0.7227855 0.022657829 0.7454434
5 0.7038971 0.028187790 0.7320849
6 0.7147075 0.019953092 0.7346606
7 0.7077898 0.019170293 0.7269601
8 0.7293381 0.021413513 0.7507516
9 0.7248347 0.015966127 0.7408008
10 0.7013024 0.027459294 0.7287616
11 0.7180575 0.027560993 0.7456185
12 0.7254695 0.016433999 0.7419035
13 0.7208756 0.028004450 0.7488801
14 0.7213546 0.021782287 0.7431369
15 0.7091738 0.023904412 0.7330782
16 0.6992244 0.030988222 0.7302126
17 0.7170110 0.015812776 0.7328238
18 0.7329935 0.026391899 0.7593854
19 0.7397578 0.025585865 0.7653437
20 0.7162948 0.020391622 0.7366864
21 0.7177122 0.037971312 0.7556835
22 0.7360014 0.036717171 0.7727186
23 0.6839098 0.033967820 0.7178776
24 0.7367818 0.016643977 0.7534258
25 0.7116417 0.041417312 0.7530590
26 0.7051109 0.015300532 0.7204114
27 0.7498262 0.029400049 0.7792263
28 0.6988924 0.022599079 0.7214915
29 0.7162162 0.013489523 0.7297057
30 0.7422486 0.025897612 0.7681462
31 0.7483216 0.028679568 0.7770012
32 0.6809735 0.019481608 0.7004551
33 0.7254739 0.022253201 0.7477271
34 0.7327024 0.032298936 0.7650014
35 0.7072375 0.039800984 0.7470384
36 0.7355042 0.015436511 0.7509407
37 0.7150838 0.024293906 0.7393777
38 0.7478806 0.023868087 0.7717487
39 0.7238175 0.023407686 0.7472252
40 0.7219966 0.036216720 0.7582133
41 0.6879977 0.025664215 0.7136619
42 0.7266412 0.011831847 0.7384730
43 0.6982077 0.024476864 0.7226846
44 0.7058529 0.031447703 0.7373006
45 0.7010385 0.021354392 0.7223929
46 0.7374615 0.033434352 0.7708959
47 0.7242720 0.015151961 0.7394240
48 0.7093822 0.021116109 0.7304983
49 0.7166532 0.014567029 0.7312203
50 0.7196842 0.016057562 0.7357418
51 0.7200293 0.015352645 0.7353820
52 0.7046547 0.033057177 0.7377119
53 0.7033134 0.035933131 0.7392465
54 0.7266439 0.024592551 0.7512365
55 0.7164537 0.011654572 0.7281082
56 0.7514973 0.020775510 0.7722728
57 0.7143798 0.015616683 0.7299964
58 0.7197729 0.010779804 0.7305527
59 0.7488523 0.020978600 0.7698309
60 0.7413837 0.025912866 0.7672966
61 0.6997322 0.016529252 0.7162614
62 0.7118371 0.037245542 0.7490827
63 0.7489347 0.030464714 0.7793994
64 0.7042614 0.025237213 0.7294986
65 0.7266451 0.009009415 0.7356545
66 0.6773419 0.035352508 0.7126944
67 0.7551003 0.030587098 0.7856874
68 0.7293253 0.012963585 0.7422888
69 0.7331272 0.023880634 0.7570078
70 0.7310474 0.022874154 0.7539216
71 0.7313459 0.013742128 0.7450881
72 0.6934840 0.023234563 0.7167186
73 0.7063818 0.029796273 0.7361781
74 0.7283268 0.027136303 0.7554631
75 0.7265105 0.023212643 0.7497231
76 0.7222900 0.020807755 0.7430978
77 0.7342908 0.024900261 0.7591911
78 0.7210031 0.040042390 0.7610455
79 0.7179771 0.021160744 0.7391379
80 0.7316718 0.015510765 0.7471826
81 0.6967779 0.033668367 0.7304463
82 0.7325095 0.029187076 0.7616966
83 0.7078965 0.027325810 0.7352223
84 0.7227051 0.021388665 0.7440938
85 0.7279347 0.013508556 0.7414433
86 0.7295308 0.032960598 0.7624914
87 0.7203869 0.012119943 0.7325069
88 0.7380805 0.018113051 0.7561936
89 0.7458683 0.024947146 0.7708155
90 0.6921346 0.029382135 0.7215167
91 0.7039594 0.023121895 0.7270813
92 0.7265955 0.010942778 0.7375382
93 0.7257014 0.031118283 0.7568196
94 0.7354516 0.032387335 0.7678390
95 0.7101378 0.027979674 0.7381175
96 0.7249035 0.012412709 0.7373162
97 0.7151136 0.016989273 0.7321028
98 0.7004286 0.019421497 0.7198501
99 0.7102355 0.020976090 0.7312116
100 0.7076531 0.021909110 0.7295622
$mean.values
beta.BRAY.BAL beta.BRAY.GRA beta.BRAY
0.7195710 0.0235587 0.7431297
$sd.values
beta.BRAY.BAL beta.BRAY.GRA beta.BRAY
0.016310987 0.007624009 0.017326379
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