Calculates the indicator value (fidelity and relative abundance) of species in clusters or types.
indval(x, …)
# S3 method for default
indval(x,clustering,numitr=1000,…)
# S3 method for stride
indval(x,taxa,numitr=1,…)
# S3 method for indval
summary(object, p=0.05, type='short', digits=2, show=p,
sort=FALSE, too.many=100, …)a matrix or data.frame of samples with species as columns and
samples as rows, or an object of class ‘stride’ from function
stride
the number of randomizations to iterate to calculate probabilities
a dataframe with samples as rows and species as columns
an object of class ‘indval’
the maximum probability for a species to be listed in the summary
a switch to choose between ‘short’ and ‘long’ style summary
the number of significant digits to show
the threshold to show values as opposed to a dot column place-holder
a switch to control user-managed interactive table sorting
a threshold reduce the listing for large data sets
additional arguments to the summary or generic function
The default function returns a list of class ‘indval’ with components:
relative frequency of species in classes
relative abundance of species in classes
the indicator value for each species
the class each species has maximum indicator value for
the indicator value for each species to its maximum class
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 val;ue 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.
Calculates the indicator value ‘d’ of species as the product of the relative frequency and relative average abundance in clusters. Specifically,
where: \(p_{i,j} = \) presence/absence (1/0) of species \(i\) in sample \(j\); \(x_{i,j}\) = abundance of species \(i\) in sample \(j\); \(n_c = \) number of samples in cluster \(c\); for cluster \(c\) in set \(K\);
$$f_{i,c} = {\sum_{j \in c} p_{i,j} \over n_c}$$ $$a_{i,c} = {(\sum_{j \in c} x_{i,j}) / n_c \over \sum_{k=1}^K ((\sum_{j \in k} x_{i,j}) / n_k)}$$ $$d_{i,c} = f_{i,c} \times a_{i,c}$$
Calculated on a ‘stride’ the function calculates the indicator values of species for each of the separate partitions in the stride.
Dufrene, M. and Legendre, P. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67(3):345-366.
# NOT RUN {
data(bryceveg) # returns a vegetation data.frame
dis.bc <- dsvdis(bryceveg,'bray/curtis') # returns a dissimilarity matrix
clust <- sample(1:5,nrow(bryceveg),replace=TRUE)
indval(bryceveg,clust)
# }
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