individual.based.random.values.a {CNull} | R Documentation |

Given a matrix M, an alpha diversity measure f and a number of repetitions k, the function produces k random values of f based on the individual-based model. This is equivalent to shuffling M according to this model as many as k times , each time outputing the value of f only for a certain row (e.g. the top one) of the shuffled matrix. The output values can be used to determine the null distribution of f for a row of M. This distribution is the same for every row of M. This is because the examined null model produces the same distribution for all rows of M; after shuffling M, each row has the same probability to store a specific community C as any other in the resulting matrix.

individual.based.random.values.a(matrix,f,args,reps=1000)

`matrix` |
A matrix with integer values. The matrix should not contain any NA values. |

`f` |
An alpha diversity function f. The interface of f should be such that f(matrix,args) returns a numeric vector V where the i-th element of V is equal to the value of f when applied at the i-th row of the given matrix. To fit to this interface, the user might have to develop f as a wrapper around an existing R function (see |

`args` |
A list with extra arguments needed by f. |

`reps` |
The number of randomizations. This argument is optional and its default value is set to one thousand. |

A vector of as many as reps elements. Stores the randomized values of f calculated based on the individual-based null model.

Constantinos Tsirogiannis (tsirogiannis.c@gmail.com)

Stegen, J. C., Freestone, A. L., Crist, T. O., Anderson, M. J., Chase, J. M., Comita, L. S., Cornell, H. V., Davies, K. F., Harrison, S. P., Hurlbert, A. H., Inouye, B. D., Kraft, N. J. B., Myers, J. A., Sanders, N. J., Swenson, N. G., Vellend, M. (2013), Stochastic and Deterministic Drivers of Spatial and Temporal Turnover in Breeding Bird Communities. Global Ecology and Biogeography, 22: 202-212.

Tsirogiannis, C., A. Kalvisa, B. Sandel and T. Conradi. Column-Shuffling Null Models Are Simpler Than You Thought. To appear.

#In the next example null-model calculations are #performed using a function of phylogenetic diversity. #Hence, we first load the required packages. require(CNull) require(ape) require(PhyloMeasures) #Load phylogenetic tree of bird families from package "ape" data(bird.families, package = "ape") #Create 100 random communities with 50 families each comm = matrix(0,nrow = 100,ncol = length(bird.families$tip.label)) for(i in 1:nrow(comm)) {comm[i,sample(1:ncol(comm),50)] = 1} colnames(comm) = bird.families$tip.label #Set function f to be the Phylogenetic Diversity measure (PD) #as defined in the R package PhyloMeasures. my.f = function(mt,args){ return (pd.query(args[[1]],mt))} # This function takes one extra argument, which is a phylogenetic tree. # Hence, create a list whose only element is the desired tree. arguments = list() arguments[[1]] = bird.families # Calculate 2000 randomized values of f on comm # based on the individual-based null model. individual.based.random.values.a(comm,f=my.f,args=arguments,reps=2000)

[Package *CNull* version 1.0 Index]