View source: R/ExponentialFitnessLinkFun.R
Model.additivelink.exponential.fitness | R Documentation |
Assumes a diagonal consisting of 0s.
Model.additivelink.exponential.fitness(
n,
alpha,
beta,
gamma = 1,
lambdaprior,
sdpropfitness = 1/sqrt(n)
)
n |
Number of nodes in the model. |
alpha |
Exponent of the power law of the degree distribution. Must be <0. |
beta |
Lower endpoint of the relative expected out degree (expected out degree divided by n-1). Must be >=0. |
gamma |
Upper endpoint of the relative expected out degree (expected out degree divided by n-1). Must be at least beta and at most 1. |
lambdaprior |
Prior on zeta and eta. For the type of object
required see |
sdpropfitness |
Standard deviation for the log-normally
distributed multiplicative proposals for Metropoli-Hastings updates
of the fitness. Defaults to |
A model to be used by sample_HierarchicalModel. This is a list of functions. It includes a function accrates() that repors acceptance rates for the Metropolis-Hasting steps involved.
mod <- Model.additivelink.exponential.fitness(10,alpha=-2.5,beta=0.1,
lambdaprior=Model.fitness.genlambdaparprior(ratescale=1e3))
theta <- mod$rtheta()
L <- genL(mod)
l <- rowSums(L$L)
a <- colSums(L$L)
## increase number of samples and thinning in real examples
res <- sample_HierarchicalModel(l=l,a=a,model=mod,nsamples=4,thin=50)
mod$accrates()
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