SIR_LNA_ESlice_MCMC4: theta1 theta2 Trajectory matrix: in log-scale state Initial...
In MingweiWilliamTang/LNAphyloDyn: Bayesian inference on coalescent-based phylodynamics using linear noise approximation
Description
Infectious rate and recovery rate: theta1, theta2
Scale parameter: lambda
Usage
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SIR_LNA_ESlice_MCMC4(Init, times, t_correct, N, gridsize = 50, niter = 1000,
burn = 500, thin = 5, a1 = 10, a2 = 20, c1 = -2.3, d1 = 200,
d2 = 40)
Details
alpha1: log(alpha1) ~ gamma(60,a1)
alpha2: log(alpha2) ~ gamma(60,a2)
prior
s1: s1 = theta1*N/theta2 U[1,10]
propose function random walk Uniform[-0.5,0.5] on log space
s2: theta2 log(s2) ~ norm(c1,0.4)
lambda log(lambda) ~ gamma(d1,d2) propose random walk uniform [-0.5,0.5] on logspace
log(p1/p3) ~ gamma(60,10)
log(p2/p3) ~ gamma(60,20)
MingweiWilliamTang/LNAphyloDyn documentation built on Oct. 23, 2019, 11:56 a.m.
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Description
Infectious rate and recovery rate: theta1, theta2 Scale parameter: lambda
Usage
1 2 3 | SIR_LNA_ESlice_MCMC4(Init, times, t_correct, N, gridsize = 50, niter = 1000,
burn = 500, thin = 5, a1 = 10, a2 = 20, c1 = -2.3, d1 = 200,
d2 = 40)
|
Details
alpha1: log(alpha1) ~ gamma(60,a1) alpha2: log(alpha2) ~ gamma(60,a2) prior s1: s1 = theta1*N/theta2 U[1,10] propose function random walk Uniform[-0.5,0.5] on log space
s2: theta2 log(s2) ~ norm(c1,0.4)
lambda log(lambda) ~ gamma(d1,d2) propose random walk uniform [-0.5,0.5] on logspace
log(p1/p3) ~ gamma(60,10) log(p2/p3) ~ gamma(60,20)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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