Niche overlap null model engine

Usage

niche_null_model(speciesData, algo = "ra3", metric = "pianka", nReps = 1000, rowNames = TRUE, saveSeed = FALSE, algoOpts = list(), metricOpts = list())

Arguments

speciesData

a data frame in which each row is a species, each column is a resource utilization category, and the entries represent the quantity of the resource used by each species. Examples might be the amount of time a species spends foraging in different microhabitats, the biomass of different prey types, or counts of the number of times an adult female oviposits eggs on different species of a host plant.

algo

the algorithm to use, must be "ra1", "ra2", "ra3", "ra4"

metric

the metric used to caluclate the null model: choices are "pianka", "czekanowski", "pianka_var", "czekanowski_var", "pianka_skew", "czekanowski_skew"; default is pianka

nReps

the number of replicates to run the null model.

rowNames

Does your dataframe have row names? If yes, they are stripped, otherwise FALSE for data that has no row names

saveSeed

TRUE or FALSE. If TRUE the current seed is saved so the simulation can be repeated

algoOpts

a list containing all the options for the specific algorithm you want to use. Must match the algorithm given in the `algo` argument

metricOpts

a list containing all the options for the specific metric you want to use. Must match the metric given in the `metric` argument

Examples

# Load MacAruthur warbler data
data(dataMacWarb)

Warning message:
data set ‘dataMacWarb’ not found

# Run the null model
warbMod <- niche_null_model(dataMacWarb,nReps=100000)

     
  |=========================================================================================| 100%

# Summary and plot info
summary(warbMod)

Time Stamp:  Thu Mar  5 23:27:24 2015 
Reproducible:  FALSE 
Number of Replications:  1e+05 
Elapsed Time:  37 secs 
Metric:  pianka 
Algorithm:  ra3 
Observed Index:  0.55514 
Mean Of Simulated Index:  0.39032 
Variance Of Simulated Index:  0.0023272 
Lower 95% (1-tail):  0.3219 
Upper 95% (1-tail):  0.4783 
Lower 95% (2-tail):  0.31224 
Upper 95% (2-tail):  0.49999 
Lower-tail P =  0.99593 
Upper-tail P =  0.00407 
Observed metric > 99593 simulated metrics 
Observed metric < 407 simulated metrics 
Observed metric = 0 simulated metrics 
Standardized Effect Size (SES):  3.4165 

plot(warbMod)


plot(warbMod,type="niche")