Description Usage Arguments Value Author(s) References See Also Examples
There are six type
s of plots for fitted hhh4
models:
Plot the "fitted"
component means (of selected units)
along time along with the observed counts.
Plot the estimated "season"
ality of the three components.
Plot the time-course of the dominant eigenvalue "maxEV"
.
If the units of the corresponding multivariate
"sts"
object represent different regions,
maps of the fitted mean components averaged over time ("maps"
),
or a map of estimated region-specific intercepts ("ri"
) of a
selected model component can be produced.
Plot the (estimated) neighbourhood weights
("neweights"
) as a function of neighbourhood order
(shortest-path distance between regions), i.e., w_ji ~ o_ji
.
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 | ## S3 method for class 'hhh4'
plot(x, type=c("fitted", "season", "maxEV", "maps", "ri", "neweights"), ...)
plotHHH4_fitted(x, units = 1, names = NULL,
col = c("grey85", "blue", "orange"),
pch = 19, pt.cex = 0.6, pt.col = 1,
par.settings = list(),
legend = TRUE, legend.args = list(),
legend.observed = FALSE,
decompose = NULL, meanHHH = NULL, ...)
plotHHH4_fitted1(x, unit = 1, main = NULL,
col = c("grey85", "blue", "orange"),
pch = 19, pt.cex = 0.6, pt.col = 1, border = col,
start = x$stsObj@start, end = NULL, xaxis = NULL,
xlim = NULL, ylim = NULL, xlab = "", ylab = "No. infected",
hide0s = FALSE, decompose = NULL, meanHHH = NULL)
plotHHH4_season(..., components = NULL, intercept = FALSE,
xlim = NULL, ylim = NULL,
xlab = NULL, ylab = "", main = NULL,
par.settings = list(), matplot.args = list(),
legend = NULL, legend.args = list(),
refline.args = list(), unit = 1)
getMaxEV_season(x)
plotHHH4_maxEV(...,
matplot.args = list(), refline.args = list(),
legend.args = list())
getMaxEV(x)
plotHHH4_maps(x, which = c("mean", "endemic", "epi.own", "epi.neighbours"),
prop = FALSE, main = which, zmax = NULL,
col.regions = hcl.colors(10),
labels = FALSE, sp.layout = NULL, ...,
map = x$stsObj@map, meanHHH = NULL)
plotHHH4_ri(x, component, labels = FALSE, sp.layout = NULL,
gpar.missing = list(col = "darkgrey", lty = 2, lwd = 2),
...)
plotHHH4_neweights(x, plotter = boxplot, ...,
exclude = 0, maxlag = Inf)
|
x |
a fitted |
type |
type of plot: either |
... |
For |
units,unit |
integer or character vector specifying a single
|
names,main |
main title(s) for the selected
|
col,border |
length 3 vectors specifying the fill and border colors for the endemic, autoregressive, and spatio-temporal component polygons (in this order). |
pch,pt.cex,pt.col |
style specifications for the dots drawn to represent
the observed counts. |
par.settings |
list of graphical parameters for
|
legend |
Integer vector specifying in which of the
|
legend.args |
list of arguments for |
legend.observed |
logical indicating if the legend should contain a line for the dots corresponding to observed counts. |
decompose |
if |
start,end |
time range to plot specified by vectors of length two
in the form |
xaxis |
if this is a list (of arguments for
|
xlim |
numeric vector of length 2 specifying the x-axis range.
The default ( |
ylim |
y-axis range.
For |
xlab,ylab |
axis labels. For |
hide0s |
logical indicating if dots for zero observed counts should be omitted. Especially useful if there are too many. |
meanHHH |
(internal) use different component means than those
estimated and available from |
components |
character vector of component names, i.e., a subset
of |
intercept |
logical indicating whether to plot seasonality as a
multiplicative effect on the respective component (the default
|
matplot.args |
list of line style specifications passed to
|
refline.args |
list of line style specifications passed to
|
which |
a character vector specifying the components of the mean for which to produce maps. By default, the overall mean and all three components are shown. |
prop |
a logical indicating whether the component maps should display proportions of the total mean instead of absolute numbers. |
zmax |
a numeric vector of length |
col.regions |
a vector of colors used to encode the fitted
component means (see |
map |
an object inheriting from |
component |
component for which to plot the estimated
region-specific random intercepts. Must partially match one of
|
labels |
determines if and how regions are labeled, see
|
sp.layout |
optional list of additional layout items, see
|
gpar.missing |
list of graphical parameters for
|
plotter |
the (name of a) function used to produce the plot of
weights (a numeric vector) as a function of neighbourhood order (a
factor variable). It is called as
|
exclude |
vector of neighbourhood orders to be excluded from
plotting (passed to |
maxlag |
maximum order of neighbourhood to be assumed when
computing the |
plotHHH4_fitted1
invisibly returns a matrix of the fitted
component means for the selected unit
, and plotHHH4_fitted
returns these in a list for all units
.
plotHHH4_season
invisibly returns the plotted y-values, i.e. the
multiplicative seasonality effect within each of components
.
Note that this will include the intercept, i.e. the point estimate of
exp(intercept + seasonality) is plotted and returned.
getMaxEV_season
returns a list with elements
"maxEV.season"
(as plotted by
plotHHH4_season(..., components="maxEV")
,
"maxEV.const"
and "Lambda.const"
(the Lambda matrix and
its dominant eigenvalue if time effects are ignored).
plotHHH4_maxEV
(invisibly) and getMaxEV
return the
dominant eigenvalue of the Λ_t matrix for all time points
t of x$stsObj
.
plotHHH4_maps
returns a trellis.object
if
length(which) == 1
(a single spplot
), and
otherwise uses grid.arrange
from the
gridExtra package to arrange all length(which)
spplot
s on a single page.
plotHHH4_ri
returns the generated spplot
, i.e.,
a trellis.object
.
plotHHH4_neweights
eventually calls plotter
and
thus returns whatever is returned by that function.
Sebastian Meyer
Held, L. and Paul, M. (2012): Modeling seasonality in space-time
infectious disease surveillance data.
Biometrical Journal, 54, 824-843.
DOI-Link: http://dx.doi.org/10.1002/bimj.201200037
other methods for hhh4
fits, e.g., summary.hhh4
.
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 | data("measlesWeserEms")
## fit a simple hhh4 model
measlesModel <- list(
ar = list(f = ~ 1),
end = list(f = addSeason2formula(~0 + ri(type="iid"), S=1, period=52),
offset = population(measlesWeserEms)),
family = "NegBin1"
)
measlesFit <- hhh4(measlesWeserEms, measlesModel)
## fitted values for a single unit
plot(measlesFit, units=2)
## 'xaxis' option for a nicely formatted time axis
## default tick locations and labels:
plot(measlesFit, units=2, xaxis=list(epochsAsDate=TRUE, line=1))
## an alternative with monthly ticks:
oopts <- surveillance.options(stsTickFactors = c("%m"=0.75, "%Y" = 1.5))
plot(measlesFit, units=2, xaxis=list(epochsAsDate=TRUE,
xaxis.tickFreq=list("%m"=atChange, "%Y"=atChange),
xaxis.labelFreq=list("%Y"=atMedian), xaxis.labelFormat="%Y"))
surveillance.options(oopts)
## plot the multiplicative effect of seasonality
plot(measlesFit, type="season")
## dominant eigenvalue of the Lambda matrix (cf. Held and Paul, 2012)
getMaxEV(measlesFit) # here simply constant and equal to exp(ar.1)
plot(measlesFit, type="maxEV") # not very exciting
## fitted mean components by district averaged over time
if (requireNamespace("gridExtra"))
plot(measlesFit, type="maps", labels=list(cex=0.6),
main=c("Total","Endemic","Within district","From other districts"))
## random intercepts of the endemic component
plot(measlesFit, type="ri", component="end", labels=list(font=3, labels="GEN"))
## neighbourhood weights as a function of neighbourhood order
plot(measlesFit, type="neweights") # boring, model has no "ne" component
## fitted values for the 6 regions with most cases and some customization
bigunits <- tail(names(sort(colSums(observed(measlesWeserEms)))), 6)
plot(measlesFit, units=bigunits,
names=measlesWeserEms@map@data[bigunits,"GEN"],
legend=5, legend.args=list(x="top"), xlab="Time (weekly)",
hide0s=TRUE, ylim=c(0,max(observed(measlesWeserEms)[,bigunits])),
start=c(2002,1), end=c(2002,26), par.settings=list(xaxs="i"))
## plot completely decomposed mean structure (useless without 'ne' component)
plot(measlesFit, units=bigunits, col=rainbow(measlesFit$nUnit), decompose=TRUE)
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