Description Usage Arguments Author(s) Examples
Arrays of simulated counts from simulate.hhh4
can be
visualized in various levels of aggregation: final size, time series.
Furthermore, proper scoring rules can be calculated based on the
simulated predictive distributions. Be aware, though, that the current
implementation can only compute univariate scores, i.e., it treats the
predictions at the various time points as independent.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## S3 method for class 'hhh4sims'
plot(x, ...)
as.hhh4simslist(x, ...)
## S3 method for class 'hhh4simslist'
plot(x, type = c("size", "time"), ...,
groups = NULL, par.settings = list())
plotHHH4sims_size(x, horizontal = TRUE, trafo = NULL, observed = TRUE, ...)
plotHHH4sims_time(x, average = mean, individual = length(x) == 1,
conf.level = if (individual) 0.95 else NULL,
matplot.args = list(), initial.args = list(), legend = length(x) > 1,
xlim = NULL, ylim = NULL, add = FALSE, ...)
## S3 method for class 'hhh4sims'
scores(x, which = "rps", units = NULL, ..., drop = TRUE)
## S3 method for class 'hhh4simslist'
scores(x, ...)
|
x |
an object of class |
type |
a character string indicating the summary plot to produce. |
... |
further arguments passed to methods. |
groups |
an optional factor to produce stratified plots by groups of units.
The special setting |
par.settings |
a list of graphical parameters for |
horizontal |
a logical indicating if the boxplots of the final size distributions should be horizontal (the default). |
trafo |
an optional transformation function from the scales package, e.g.,
|
observed |
a logical indicating if a line and axis value for the observed size of the epidemic should be added to the plot. Alternatively, a list with graphical parameters can be specified to modify the default values. |
average |
scalar-valued function to apply to the simulated counts at each time point. |
individual |
a logical indicating if the individual simulations should be shown as well. |
conf.level |
a scalar in (0,1), which determines the level of the pointwise
quantiles obtained from the simulated counts at each time point.
A value of |
matplot.args |
a list of graphical parameters for |
initial.args |
if a list (of graphical parameters for |
legend |
a logical or a list of parameters for |
xlim,ylim |
vectors of length 2 determining the axis limits. |
add |
a logical indicating if the (mean) simulated time series should be added to an existing plot. |
which |
a character vector indicating which proper scoring rules to compute.
By default, only the ranked probability score ( |
units |
if non- |
drop |
a logical indicating if univariate dimensions should be dropped (the default). |
Sebastian Meyer
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 | ### univariate example
data("salmAllOnset")
## fit a hhh4 model to the first 13 years
salmModel <- list(end = list(f = addSeason2formula(~1 + t)),
ar = list(f = ~1), family = "NegBin1", subset = 2:678)
salmFit <- hhh4(salmAllOnset, salmModel)
## simulate the next 20 weeks ahead
salmSims <- simulate(salmFit, nsim = 300, seed = 3, subset = 678 + seq_len(20),
y.start = observed(salmAllOnset)[678,])
## compare final size distribution to observed value
plot(salmSims)
## simulated time series
plot(salmSims, type = "time", main = "2-weeks-ahead simulation")
### multivariate example
data("measlesWeserEms")
## fit a hhh4 model to the first year
measlesModel <- list(
end = list(f = addSeason2formula(~1), offset = population(measlesWeserEms)),
ar = list(f = ~1),
ne = list(f = ~1 + log(pop),
weights = W_powerlaw(maxlag = 5, normalize = TRUE)),
family = "NegBin1", subset = 2:52,
data = list(pop = population(measlesWeserEms)))
measlesFit1 <- hhh4(measlesWeserEms, control = measlesModel)
measlesFit2 <- update(measlesFit1, family = "Poisson")
## simulate realizations from this model during the second year
measlesSims <- lapply(X = list(NegBin = measlesFit1, Poisson = measlesFit2),
FUN = simulate, nsim = 50, seed = 1, subset = 53:104,
y.start = observed(measlesWeserEms)[52,])
## final size of the first model
plot(measlesSims[[1]])
## stratified by groups of districts
plot(measlesSims[[1]], groups = factor(substr(colnames(measlesWeserEms), 4, 4)))
## a class and plot-method for a list of simulations from different models
measlesSims <- as.hhh4simslist(measlesSims)
plot(measlesSims)
## simulated time series
plot(measlesSims, type = "time", individual = TRUE, ylim = c(0, 80))
## compare proper scoring rules for a specific subset of the regions
## (CAVE: these are univariate scores for each time point and region,
## which do not account for dependence over time)
measlesScores5 <- scores(measlesSims, which = "rps",
units = substr(colnames(measlesWeserEms), 4, 4) == "5")
sapply(measlesScores5, mean)
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