Description Usage Arguments Value See Also Examples
Summary method for PMCMC
objects.
1 2 |
object |
A |
transfunc |
Is a |
... |
Not used here. |
A summary.mcmc
object.
PMCMC
, print.PMCMC
, predict.PMCMC
, plot.PMCMC
window.PMCMC
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | ## set up data to pass to PMCMC
flu_dat <- data.frame(
t = 1:14,
Robs = c(3, 8, 26, 76, 225, 298, 258, 233, 189, 128, 68, 29, 14, 4)
)
## set up observation process
obs <- data.frame(
dataNames = "Robs",
dist = "pois",
p1 = "R + 1e-5",
p2 = NA,
stringsAsFactors = FALSE
)
## set up model (no need to specify tspan
## argument as it is set in PMCMC())
transitions <- c(
"S -> beta * S * I / (S + I + R + R1) -> I",
"I -> gamma * I -> R",
"R -> gamma1 * R -> R1"
)
compartments <- c("S", "I", "R", "R1")
pars <- c("beta", "gamma", "gamma1")
model <- mparseRcpp(
transitions = transitions,
compartments = compartments,
pars = pars,
obsProcess = obs
)
## set priors
priors <- data.frame(
parnames = c("beta", "gamma", "gamma1"),
dist = rep("unif", 3),
stringsAsFactors = FALSE)
priors$p1 <- c(0, 0, 0)
priors$p2 <- c(5, 5, 5)
## define initial states
iniStates <- c(S = 762, I = 1, R = 0, R1 = 0)
set.seed(50)
## run PMCMC algorithm
post <- PMCMC(
x = flu_dat,
priors = priors,
func = model,
u = iniStates,
npart = 25,
niter = 5000,
nprintsum = 1000
)
## plot MCMC traces
plot(post, "trace")
## continue for some more iterations
post <- PMCMC(post, niter = 5000, nprintsum = 1000)
## plot traces and posteriors
plot(post, "trace")
plot(post)
## remove burn-in
post <- window(post, start = 5000)
## summarise posteriors
summary(post)
|
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