loglikelihood: The (incomplete data) loglikelihood function for the negative...

In johannabertl/SelectionMix: Negative binomial mixture model

Description

The (incomplete data) loglikelihood function for the negative binomial mixture model

Usage

loglikelihood(theta, x.syn, x.non, cvec)

Arguments

theta	c(alpha, beta, p1, p2, ..., pk-1)
x.syn	vector of synonymous mutation count
x.non	vector of non-synonymous mutation count
cvec	vector of k positive values c(c1, ..., ck)

Author(s)

Johanna Bertl

Examples

c1 = 0.5; c2 = 10
p1 = 0.2; p2 = 0.8
alpha = 10
beta = 5

mutations = rnegbinmix_syn_nonsyn(n=3000, alpha=alpha, beta=beta, c=c(c1, c2), p = c(p1, p2))
# log likelihood at the true value
loglikelihood(theta=c(alpha,beta,p1), x.syn = mutations$Syn, x.non = mutations$Non, cvec=c(c1, c2))

# likelihood surface

alphavec = 1:20
betavec = seq(0.5, 10, by=0.5)
p1vec = seq(0.11, 0.3, by=0.01)

llalpha = numeric(20)
llbeta = numeric(20)
llp1 = numeric(20)

for(i in 1:20){
 llalpha[i] = loglikelihood(theta=c(alphavec[i],beta,p1), x.syn = mutations$Syn, x.non = mutations$Non, cvec=c(c1, c2))
 llbeta[i] = loglikelihood(theta=c(alpha,betavec[i],p1), x.syn = mutations$Syn, x.non = mutations$Non, cvec=c(c1, c2))
 llp1[i] = loglikelihood(theta=c(alpha,beta,p1vec[i]), x.syn = mutations$Syn, x.non = mutations$Non, cvec=c(c1, c2))
}

plot(alphavec, llalpha, t="b")
abline(v=alpha, col="red")
plot(betavec, llbeta, t="b")
abline(v=beta, col="red")
plot(p1vec, llp1, t="b")
abline(v=p1, col="red")

johannabertl/SelectionMix documentation built on May 3, 2019, 4:03 p.m.