optpart (version 3.0-3)

murdoch: Indicator Species Analysis by Murdoch Preference Function

Description

Calculates the indicator value of species in a single cluster or environment type using the Murdoch Preference Function

Usage

murdoch(comm,type,minval=0,minplt=10)
# S3 method for murdoch
summary(object,pval=0.05,digits=3,…)
# S3 method for murdoch
plot(x,axtype=1,pval=0.05,…)
# S3 method for murdoch
print(x,digits = 5, ...)

Arguments

comm

a matrix or data.frame of samples with species as columns and samples as rows

type

a logical vector with values of TRUE for samples in a specific cluster or type

minval

a threshold minimum abundance value to count as a presence

minplt

the minimum number of presences to include a species in the calculation

object

and object of class ‘murdoch’

pval

the maximum probability to include a species in the summary table

digits

the number of digits to report

…

ancillary arguments to maintain compatibility with the generic summary function

x

an object of class ‘murdoch’

axtype

a switch to control scaling of the x axis in the plot. 1=number of plots in the data set, other = number of presences in the type

Value

a list object of class ‘murdoch’ with components:

minplt

the minimum number of presences to be included

nplots

the number of plots a species occurs in

type

the plot membership vector for the type

pres

the number of presences for species in the type

abs

the number of absences of species in the type

murdoch

the Murdoch value for species in the type

pval

the probability of getting such a high murdoch value

Details

Calculates the indicator value of species for a specific type using the modified Murdoch statistic: $$log((p/a) * (n-p_i)/n_i)$$ where: \(p\) = number of samples where species is present, \(a\) = number of samples where species is absent, \(n\) = total number of samples (p+a), \(p_i\) = number of samples in type i where species is present, \(n_i\) = number of samples in type i.

Probabilities are based on the hypergeometric distribution calculation of having as many or more presences in a type as observed.

See Also

indval, tabdev

Examples

# NOT RUN {
data(shoshveg)                           # returns a vegetation dataframe
dis.bc <- dsvdis(shoshveg,'bray/curtis') # returns a dissimilarity 
                                         #   matrix
opt.5 <- optpart(5,dis.bc)
plot(murdoch(shoshveg,opt.5$clustering==1))
# }