indval: Dufrene-Legendre Indicator Species Analysis

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/indval.R

Description

Calculates the indicator value (fidelity and relative abundance) of species in clusters or types.

Usage

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indval(x, ...)
## Default S3 method:
indval(x,clustering,numitr=1000,...)
## S3 method for class 'stride'
indval(x,comm,numitr=1,...)
## S3 method for class 'indval'
summary(object, p=0.05, type='short', digits=2, show=p,
       sort=FALSE, too.many=100, ...)

Arguments

x

a matrix or data.frame of samples with species as columns and samples as rows, or an object of class ‘stride’ from function stride

clustering

a vector of numeric cluster memberships for samples, or a classification object returned from pam, or optpart, slice, or archi

numitr

the number of randomizations to iterate to calculate probabilities

comm

a data.frame with samples as rows and species as columns

object

an object of class ‘indval’

p

the maximum probability for a species to be listed in the summary

type

a switch to choose between ‘short’ and ‘long’ style summary

digits

the number of significant digits to show

show

the threshold to show values as opposed to a dot column place-holder

sort

a switch to control user-managed interactive table sorting

too.many

a threshold reduce the listing for large data sets

...

additional arguments to the summary or generic function

Details

Calculates the indicator value ‘d’ of species as the product of the relative frequency and relative average abundance in clusters. Specifically,

where:
p_(ij) = presence/absence (1/0) of species i in sample j;
x_(ij) = abundance of species i in sample j;
n_c = number of samples in cluster c;
for cluster c \in K;

f_{ic} = {∑_{j \in c} p_{ij} \over n_c}

a_{ic} = {∑_{j \in c} x_{ij} / n_c \over ∑_{k=1}^K (∑_{j \in k} x_{ij} / n_k)}

d_{ic} = f_{ic} \times a_{ic}

Calculated on a ‘stride’ the function calculates the indicator values of species for each of the separate partitions in the stride.

Value

The default function returns a list of class ‘indval’ with components:

relfrq

relative frequency of species in classes

relabu

relative abundance of species in classes

indval

the indicator value for each species

maxcls

the class each species has maximum indicator value for

indcls

the indicator value for each species to its maximum class

pval

the probability of obtaining as high an indicator values as observed over the specified iterations

The stride-based function returns a data.frame with the number of clusters in the first column and the mean indicator value in the second.

The ‘summary’ function has two options. In ‘short’ mode it presents a table of indicator species whose probability is less then ‘p’, giving their indicator value and the identity of the cluster they indicate, along with the sum of probabilities for the entire data set. In ‘long’ mode, the indicator value of each species in each class is shown, with values less than ‘show’ replaced by a place-holder dot to emphasize larger values.

If ‘sort==TRUE’, a prompt is given to re-order the rows of the matrix interactively.

Note

Indicator value analysis was proposed by Dufrene and Legendre (1997) as a possible stopping rule for clustering, but has been used by ecologists for a variety of analyses. Dufrene and Legendre's nomenclature in the paper is somewhat ambiguous, but the equations above are taken from the worked example in the paper, not the equations on page 350 which appear to be in error. Dufrene and Legendre, however, multiply d by 100; this function does not.

Author(s)

David W. Roberts droberts@montana.edu http://ecology.msu.montana.edu/droberts/droberts.html

References

Dufrene, M. and Legendre, P. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67(3):345-366.

See Also

isamic

Examples

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data(bryceveg) # returns a vegetation data.frame
data(brycesite)
clust <- cut(brycesite$elev,5,labels=FALSE)
summary(indval(bryceveg,clust))

Example output

Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-28. For overview type 'help("mgcv-package")'.
Loading required package: MASS
Loading required package: cluster

Attaching package: 'labdsv'

The following object is masked from 'package:stats':

    density

       cluster indicator_value probability
arttri       1          0.4773       0.001
atrcan       1          0.4483       0.001
oryhym       1          0.2912       0.001
sithys       1          0.2696       0.009
sphcoc       1          0.2496       0.003
eurlan       1          0.2069       0.001
ericor       1          0.2069       0.003
cirneo       1          0.1724       0.001
crycon       1          0.1590       0.003
berfre       1          0.1506       0.003
molpar       1          0.1379       0.013
oenbra       1          0.1065       0.033
agrcri       1          0.1034       0.022
elysal       1          0.1012       0.035
eripan       1          0.0875       0.046
cermon       2          0.6305       0.001
quegam       2          0.3299       0.001
sherot       2          0.1734       0.002
macgri       2          0.1663       0.009
rhutri       2          0.1250       0.011
oencae       2          0.1230       0.013
artarb       3          0.6708       0.001
sticom       3          0.5923       0.001
chrvis       3          0.5168       0.001
koenit       3          0.4541       0.001
leppun       3          0.4151       0.001
oenlav       3          0.3845       0.001
agogla       3          0.3019       0.001
poafen       3          0.2984       0.001
tetcan       3          0.2877       0.001
erirac       3          0.2708       0.001
artfri       3          0.2642       0.001
linlew       3          0.2501       0.003
caschr       3          0.2336       0.001
pencom       3          0.2336       0.002
broano       3          0.2075       0.001
stipin       3          0.2075       0.001
calnut       3          0.1985       0.007
asthum       3          0.1887       0.001
erifla       3          0.1887       0.005
eriumb       3          0.1887       0.002
corkin       3          0.1622       0.004
bougra       3          0.1595       0.020
muhmon       3          0.1509       0.003
pencae       3          0.1398       0.014
junbal       3          0.1321       0.012
poanev       3          0.1321       0.011
lesint       3          0.1184       0.029
poapra       3          0.1132       0.023
lupser       3          0.1132       0.018
agrdas       3          0.1036       0.045
chrpar       3          0.1008       0.030
astken       3          0.0943       0.030
compal       3          0.0943       0.030
lyggra       3          0.0943       0.039
oencor       3          0.0943       0.018
phllon       3          0.0943       0.026
taroff       3          0.0943       0.026
ceamar       4          0.3643       0.001
carrss       4          0.2853       0.001
arcpat       4          0.2797       0.003
purtri       4          0.2161       0.023
euplur       4          0.2072       0.002
astcon       4          0.1530       0.014
swerad       4          0.1290       0.039
steten       4          0.1213       0.006
gerfre       4          0.0831       0.049
juncom       5          0.4923       0.001
symore       5          0.4199       0.001
pacmyr       5          0.4139       0.001
berrep       5          0.4115       0.001
astmis       5          0.4008       0.001
ribcer       5          0.3863       0.001
lotuta       5          0.2191       0.003
senmul       5          0.2082       0.025
achmil       5          0.1462       0.029
gilcon       5          0.1160       0.038

Sum of probabilities                 =  38.011 

Sum of Indicator Values              =  22.57 

Sum of Significant Indicator Values  =  18.11 

Number of Significant Indicators     =  77 

Significant Indicator Distribution

 1  2  3  4  5 
15  6 37  9 10 

labdsv documentation built on Aug. 4, 2019, 5:03 p.m.

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