Description Usage Arguments Value Author(s) References See Also Examples
View source: R/transdistfuncs.r
Runs est.trandsdist
on multiple bootstraps of the data and calculates confidence intervals for the mean transmission distance.
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epi.data |
a three-column matrix giving the coordinates ( |
gen.t.mean |
mean generation time of the infecting pathogen |
gen.t.sd |
standard deviation of generation time of the infecting pathogen |
t1 |
time step to begin estimation of transmission distance |
max.sep |
maximum number of time steps allowed between two cases (passed to the |
max.dist |
maximum spatial distance between two cases considered in calculation |
n.transtree.reps |
number of time to simulate transmission trees when estimating the weights of theta (passed to the |
mean.equals.sd |
logical term indicating if the mean and standard deviation of the transmission kernel are expected to be equal (default = FALSE) |
theta.weights |
use external matrix of theta weights. If NULL (default) the matrix of theta weights is automatically estimated by calling the |
boot.iter |
the number of bootstrapped iterations to perform |
ci.low |
low end of the confidence interval (default = 0.025) |
ci.high |
high end of the confidence interval (default = 0.975) |
parallel |
run bootstraps in parallel (default = FALSE) |
n.cores |
number of cores to use when |
a list object containing the point estimate for mean transmission distance and low and high bootstrapped confidence intervals
John Giles, Justin Lessler, and Henrik Salje
Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.
Other transdist:
est.transdist.temporal.bootstrap.ci()
,
est.transdist.temporal()
,
est.transdist.theta.weights()
,
est.transdist()
,
get.transdist.theta()
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# Exponentially distributed transmission kernel with mean and standard deviation = 100
dist.func <- alist(n=1, a=1/100, rexp(n, a))
# Simulate epidemic
a <- sim.epidemic(R=2.5,
gen.t.mean=7,
gen.t.sd=2,
min.cases=20,
tot.generations=5,
trans.kern.func=dist.func)
# Estimate mean transmission kernel and its bootstrapped confidence intervals
b <- est.transdist.bootstrap.ci(epi.data=a,
gen.t.mean=7,
gen.t.sd=2,
t1=0,
max.sep=1e10,
max.dist=1e10,
n.transtree.reps=10,
mean.equals.sd=TRUE,
boot.iter=10,
ci.low=0.025,
ci.high=0.975,
n.cores=2)
b
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