Returns the penalty for the current arguments
penalties(locusAdjustment, power, dropout, degradation=NULL,
rcont=NULL, dropin, locusAdjPenalty=50, dropinPenalty=2,
degradationPenalty=50, bemn=-4.35, besd=0.38, ...)Locus adjustment for each locus
Tvedebrink exponent
Ignored
Degradation parameters
Ignored.
Dropin rate
Penalty parameter for the locus adjustments
Penalty parameter for the dropin rate
Penalty parameter for the degradation parameters
Mean of the normal distribution used to penalize degradation
Standard deviation of the normal distribution used to penalize degradation
Ignored
An array of penalties per locus
The penalties are applied if and only if the relevant arguments (locusAdjustment, dropin, degradation, power) are provided. The penalties are as follows:
dropin:\(e^{-d*p}\) where \(d\) is the dropin rate and \(p\) the associated penalty. The values is normalized to one locus.
degradation:\(e^{-p\sum d}\) where \(d\) are the degradation values and \(p\) is the associated penalty
power:dnorm(t, bemn, besd) where t is the
Tvedebrink exponent, dnorm is the density of the normal distribution
with mean bemn and standard deviation besd
locusAdjustment:dgamma(a, p, p) where a is the locus
adjustment, dgamma is the density of the Gamma distribution with
p its shape and rate
Some of these penalties are meant to be applied simultaneously across all loci. Since we want penalties per locus, a normalization \(p^{\frac{1}{n}}\) is applied, where \(p\) is the penalty and \(n\) the number of loci.
create.likelihood.vectors, create.likelihood.log, create.likelihood, Objective Functions