NEWS | R Documentation |
This version consists only of a minor update of the DESCRIPTION
file to allow for the new INLA additional repository.
hhh4
In the coefficient vector resulting from a hhh4
fit,
random intercepts are now named.
Parameter start
values in hhh4()
are now
matched by name but need not be complete in that case (default
initial values are used for unspecified parameters).
The update.hhh4()
-method now by default does
use.estimates
from the previous fit. This reduces the
number of iterations during model fitting but may lead to slightly
different parameter estimates (within a tolerance of 1e-5
).
Setting use.estimates = FALSE
means to re-use the previous
start specification.
"sts"
-class For univariate "sts"
objects, the (meaningless)
“head of neighbourhood” is no longer show
n.
The "sts"
class now has a dimnames
-method
instead of a colnames
-method. Furthermore, the redundant
nrow
and ncol
methods have been removed
(the dim
-method is sufficient).
If a map
is provided when initialize()
ing an
"sts"
object, it is now verified that all observed
regions are part of the map
(matched by row.names
).
In stsplot_space()
, extra (unobserved) regions of the
map
are no longer dropped but shown with a dashed border by
default.
The R0
-method for "twinstim"
gained an argument
newcoef
to simplify computation of reproduction numbers with
a different parameter vector (also used for Monte Carlo CI's).
New plot type="neweights"
for "hhh4"
fits.
The scores()
function allows the selection of multiple
units
(by index or name) for which to compute (averaged) proper
scores. Furthermore, one can now select which
scores to compute.
Added a formula
-method for "hhh4"
fits to
extract the f
specifications of the three components from the
control list.
The update()
-method for fitted "hhh4"
models
gained an argument S
for convenient modification of component
seasonality using addSeason2formula()
.
The new auxiliary function layout.labels()
generates an
sp.layout
item for spplot()
in order to draw labels.
When generating the pit()
histogram with a single
predictive CDF pdistr
, the ...
arguments can now be
x
-specific and are recycled if necessary using mapply()
.
If pdistr
is a list of CDFs, pit()
no longer requires
the functions to be vectorized.
New method as.epidata.data.frame()
, which constructs the
start/stop SIR event history format from a simple individual-based
data frame (e.g., hagelloch.df
).
New argument w
in as.epidata.default()
to
generate covariate-based weights for the force of infection in
twinSIR
. The f
argument is for distance-based weights.
The result of profile.twinSIR()
gained a class and an
associated plot
-method.
For multivariate oneStepAhead()
predictions,
scores(..., individual=TRUE)
now returns a 3d array instead
of a collapsed matrix. Furthermore, the scores computed by default
are c("logs","rps","dss","ses")
, excluding the normalized
squared error score "nses"
which is improper.
The plot-type="season"
for "hhh4"
fits now by
default plots the multiplicative effect of seasonality on the
respective component (new argument intercept=FALSE
).
The default set of components to plot has also changed.
When as.epidata()
and simEpidata()
calculate
distance-based epidemic weights from the f
functions, they no
longer set the distance of an infectious individual to itself
artificially to Inf
.
This changes the corresponding columns in the "epidata"
in
rows of currently infectious individuals, but the twinSIR
model itself is invariant, since only rows with atRiskY=1
contribute to the likelihood.
Several modifications and corrections in data("hagelloch")
.
Better plotting of stsNC
objects by writing an own plot
method for them. Prediction intervals are now shown jointly with the
point estimate.
Reduced package size by applying tools::resaveRdaFiles
to some large datasets and by building the package with
--compact-vignettes=both
, i.e., using additional GhostScript
compression with ebook quality, see ?tools::compactPDF
.
Added units
argument to stsplot_time
to select
only a subset of the multivariate time series for plotting.
The untie
-method for class "epidataCS"
gained an
argument verbose
which is now FALSE
by default.
"epidataCS"
objects store the clipper
used
during generation as attribute of $events$.influenceRegion
.
In plotHHH4_fitted()
, the argument legend.observed
now defaults to FALSE
.
The default weights for the spatio-temporal component in
hhh4
models now are neighbourhood(stsObj) == 1
.
The previous default neighbourhood(stsObj)
does not make
sense for the newly supported nbOrder
neighbourhood matrices
(shortest-path distances). The new default makes no difference for
(old) models with binary adjacency matrices in the neighbourhood
slot of the stsObj
.
The default for nonparametric weights W_np()
in
hhh4()
is now to assume zero weight for neighbourhood orders
above maxlag
, i.e., W_np()
's argument to0
now
defaults to TRUE
.
Added a verbose
argument to permutationTest()
,
which defaults to FALSE
. The previous behaviour corresponds
to verbose=TRUE
.
simulate.twinstim()
now by default uses the original
data$W
as observation region.
The data("measlesWeserEms")
contain two additional
variables in the @map@data
slot: "vaccdoc.2004"
and
"vacc1.2004"
.
The plot-method for "epidata"
objects now uses colored
lines by default.
The surveillance package now depends on R >= 3.0.2, which, effectively, is the minimum version required since surveillance 1.7-0 (see the corresponding NEWS below).
The two diagnostic plots of checkResidualProcess()
are
now by default plotted side by side (mfrow=c(1,2)
) instead of
one below the other.
In farringtonFlexible
alarms are now for
observed>upperbound
and not for observed>=upperbound
which was not correct.
Fixed duplicate "functions"
element resulting from
update.twinstim(*,model=TRUE)
and ensured that
"twinstim"
objects always have the same components (some may
be NULL
).
animate.epidata
works again with the
animation package (ani.options("outdir")
was
removed in version 2.3)
For hhh4
models with random effects, confint()
only worked if argument parm
was specified.
Computing one-sided AIC weights by simulation for twinSIR
models with more than 2 epidemic covariates now is more robust (by
rescaling the objective function for the quadratic programming
solver) and twice as fast (due to code optimization).
simulate.twinstim(..., rmarks=NULL)
can now handle the
case where data
has no events within the simulation period
(by sampling marks from all of data$events
).
The lambda.h
values of simulated events in
"simEpidataCS"
objects were wrong if the model contained an
endemic intercept (which is usually the case).
Automatic choice of color breaks in the animate
-method
for class "sts"
now also works for incidence maps (i.e., with
a population
argument).
hhh4()
did not allow the use of nonparametric
neighbourhood weights W_np()
with maxlag=2
.
scores()
did not work for multivariate oneStepAhead()
predictions if both individual=TRUE
and sign=TRUE
, and
it could not handle a oneStepAhead()
prediction of only one
time point. Furthermore, the "sign"
column of
scores(..., sign=TRUE)
was wrong (reversed).
For "epidataCS"
with only one event,
epidataCSplot_space()
did not draw the point.
The trivial (identity) call
aggregate(stsObj, nfreq=stsObj@freq)
did not work.
Package surveillance now depends on newer versions of
packages sp (>= 1.0-15), polyCub (>= 0.4-2),
and spatstat (>= 1.36-0).
The R packages INLA and runjags are now suggested
to support a new outbreak detection algorithm (boda()
) and
the new nowcast()
ing procedure, respectively.
The R packages for lattice, grid,
gridExtra, and scales are suggested for
added visualization facilities.
More tests have been implemented to ensure package integrity. We now use testthat instead of the outdated package RUnit.
hhh4()
fits now have class "hhh4"
instead of
"ah4"
, for consistency with twinstim()
,
twinSIR()
, and to follow the common convention (cp. lm()
).
Standard S3-methods for the old "ah4"
name are still
available for backwards compatibility but may be removed in the
future.
Plot variants for "sts"
objects have been cleaned up:
The functions implementing the various plot types
(stsplot_*
, previously named plot.sts.*
)
are now exported and documented separately.
The nowcast
procedure has been completely re-written to
handle the inherit right-truncation of reporting data (best
visualized as a reporting triangle). The new code implements the
generalized-Dirichlet and the hierarchical Bayesian approach described in
Höhle and an der Heiden (2014). No backwards
compatibility to the old nowcasting procedure is given.
The package contains a new monitoring function
boda
. This is a first experimental surveillance
implementation of the Bayesian Outbreak Detection Algorithm (BODA)
proposed in Manitz and Höhle (2012). The function
relies on the non-CRAN package INLA, which has to be installed first
in order to use this function. Expect initial problems.
New toLatex
-method for "sts"
objects.
The new function stsplot_space()
provides an improved
map plot of disease incidence for "sts"
objects aggregated
over time. It corresponds to the new type = observed ~ unit
of the stsplot
-method, and supersedes
type = observed ~ 1|unit
(except for alarm shading).
An animate()
-method for the "sts"
class provides
a new implementation for animated maps (superseding the plot
type=observed ~ 1 | unit * time
) with an optional evolving
time series plot below the map.
The plot()
method for "sts"
objects with epochs as
dates is now made more flexible by introducing the arguments
xaxis.tickFreq
, xaxis.labelFreq
and
xaxis.labelFormat
. These allow the specification of
tick-marks and labelling based on strftime
compatible
conversion codes – independently if data are daily, weekly, monthly,
etc. As a consequence, the old argument xaxis.years
is
removed. See stsplot_time()
for more information.
Inference for neighbourhood weights in hhh4()
models:
W_powerlaw()
and W_np()
both implement weights
depending on the order of neighbourhood between regions, a power-law
decay and nonparametric weights, i.e., unconstrained estimation of
individual weights for each neighbourhood order.
hhh4()
now allows the inclusion of multiplicative
offsets also in the epidemic components "ar"
and "ne"
.
hhh4()
now has support for lag != 1
in the
autoregressive and neighbor-driven components. The applied lags are
stored as component "lags"
of the return value (previously
there was an unused component "lag"
which was always 1 and
has been removed now).
oneStepAhead()
:
Added support for parallel computation of predictions using
mclapply()
from package parallel.
New argument type
with a new type
"first"
to base all subsequent one-step-ahead predictions
on a single initial fit.
Nicer interpretation of verbose
levels, and
txtProgressBar()
.
The plot()
-method for fitted hhh4()
objects now
offers three additional types of plots: component seasonality,
seasonal or time course of the dominant eigenvalue, and maps
of estimated random intercepts. It is documented and more customizable.
Note that argument order and some names have changed:
i
-> units
, title
-> names
.
(Deviance) residuals()
-method for fitted hhh4()
models.
Added methods of vcov()
and nobs()
for the "hhh4"
class. For AIC()
and BIC()
, the
default methods work smoothly now (due to changes to
logLik.hhh4()
documented below).
New predefined interaction functions for twinstim()
:
siaf.student()
implements a t-kernel for the distance
decay, and siaf.step()
and tiaf.step()
provide step
function kernels (which may also be invoked by specifying the
vector of knots as the siaf
or tiaf
argument in
twinstim
).
Numerical integration over polygonal domains in the F
and Deriv
components of siaf.powerlaw()
and
siaf.powerlawL()
is much faster and more accurate now since
we use the new polyCub.iso()
instead of polyCub.SV()
from package polyCub.
New as.stepfun()
-method for "epidataCS"
objects.
plot.epidataCS()
:
The spatial plot has new arguments to automatically add
legends to the plot: legend.types
and legend.counts
.
It also gained an add
argument.
The temporal plot now supports type-specific sub-histograms, additional lines for the cumulative number of events, and an automatic legend.
The new function glm_epidataCS()
can be used to fit
an endemic-only twinstim()
via glm()
.
This is mainly provided for testing purposes since wrapping into
glm
usually takes longer.
Fitted hhh4()
objects no longer contain the associated
"sts"
data twice: it is now only stored as $stsObj
component, the hidden duplicate in $control$data$.sts
was
dropped, which makes fitted objects substantially smaller.
logLik.hhh4()
always returns an object of class
"logLik"
now; for random effects models, its "df"
attribute is NA_real_
. Furthermore, for non-convergent fits,
logLik.hhh4()
gives a warning and returns NA_real_
;
previously, an error was thrown in this case.
oneStepAhead()
:
Default of tp[2]
is now the penultimate time point of
the fitted subset (not of the whole stsObj
).
+1
on rownames of $pred
(now the same as for
$observed
).
The optional "twinstim"
result components
fisherinfo
, tau
, and functions
are always
included. They are set to NULL
if they are not applicable
instead of missing completely (as before), such that all
"twinstim"
objects have the same list structure.
iafplot()
...
invisibly returns a matrix containing the plotted
values of the (scaled) interaction function (and the confidence
interval as an attribute).
Previously, nothing (NULL
) was returned.
detects a type-specific interaction function and by default
uses types=1
if it is not type-specific.
has better default axis ranges.
adapts to the new step function kernels (with new arguments
verticals
and do.points
).
supports logarithmic axes (via new log
argument
passed on to plot.default
).
optionally respects eps.s
and eps.t
,
respectively (by the new argument truncated
).
now uses scaled=TRUE
by default.
The argument colTypes
of
plot.epidataCS(,aggregate="space")
is deprecated (use
points.args$col
instead).
The events in an "epidataCS"
object no longer have
a reserved "ID"
column.
hhh4()
now stores the runtime just like twinstim()
.
Take verbose=FALSE
in hhh4()
more seriously.
hhh4()
issues a warning()
if non-convergent.
The following components of a hhh4()
fit now have names:
"se"
, "cov"
, "Sigma"
.
The new default for pit()
is to produce the plot.
The twinstim()
argument cumCIF
now defaults to
FALSE
.
update.twinstim()
no longer uses recursive
modifyList()
for the control.siaf
argument. Instead,
the supplied new list elements ("F"
, "Deriv"
)
completely replace the respective elements from the original
control.siaf
specification.
siaf.lomax()
is now defunct (it has been deprecated
since version 1.5-2); use siaf.powerlaw()
instead.
Allow the default adapt
ive bandwidth to be specified via the
F.adaptive
argument in siaf.gaussian()
.
Unsupported options (logpars=FALSE
,
effRangeProb
) have been dropped from siaf.powerlaw()
and siaf.powerlawL()
.
More rigorous checking of tiles
in
simulate.twinstim()
and intensityplot.twinstim
.
as.epidataCS()
gained a verbose
argument.
animate.epidataCS()
now by default does not draw
influence regions (col.influence=NULL
), is verbose
if
interactive()
, and ignores sleep
on non-interactive
devices.
The multiplicity
-generic and its default method have
been integrated into spatstat and are imported from there.
The polygon representation of Germany's districts (
system.file("shapes", "districtsD.RData", package="surveillance")
) has been simplified further. The union of districtsD
is
used as observation window W
in data("imdepi")
. The
exemplary twinstim()
fit data("imdepifit")
has been
updated as well. Furthermore, row.names(imdepi$events)
have
been reset (chronological index), and numerical differences
in imdepi$events$.influenceRegion
are due to changes in
polyclip 1.3-0.
The Campylobacteriosis data set campyDE
, where absolute
humidity is used as concurrent covariate to adjust the outbreak
detection is added to the package to exemplify boda()
.
New data("measlesWeserEms")
(of class "sts"
),
a corrected version of data("measles.weser")
(of the old
"disProg"
class).
Fixed a bug in LRCUSUM.runlength
where computations
were erroneously always done under the in-control parameter
mu0
instead of mu
.
Fixed a bug during alarm plots (stsplot_alarm()
),
where the use of alarm.symbol
was ignored.
Fixed a bug in algo.glrnb
where the overdispersion
parameter alpha
from the automatically fitted glm.nb
model (fitted by estimateGLRNbHook
) was incorrectly taken as
mod[[1]]$theta
instead of 1/mod[[1]]$theta
. The error is
due to a different parametrization of the negative binomial
distribution compared to the parametrization in Höhle
and Paul (2008).
The score function of hhh4()
was wrong when fitting
endemic-only models to a subset
including the first time
point. This led to “false convergence”.
twinstim()
did not work without an endemic offset if
is.null(optim.args$par)
.
Package gpclib is no longer necessary for the
construction of "epidataCS"
-objects. Instead, we make use of
the new dedicated package polyclip (licensed under the
BSL) for polygon clipping operations (via
spatstat::intersect.owin()
). This results in a slightly
different $events$.influenceRegion
component of
"epidataCS"
objects, one reason being that
polyclip uses integer arithmetic.
Change of twinstim()
estimates for a newly created
"epidataCS"
compared with the same data prepared in earlier
versions should be very small (e.g., for data("imdepifit")
the mean relative difference of coefficients is 3.7e-08, while the
logLik()
is all.equal()
).
As an alternative, rgeos can still be chosen to do the polygon
operations.
The surveillance-internal code now depends on
R >= 2.15.2 (for nlminb()
NA
fix of PR#15052,
consistent rownames(model.matrix)
of PR#14992,
paste0()
, parallel::mcmapply()
). However, the
required recent version of spatstat (1.34-0, for
polyclip) actually needs R >= 3.0.2, which therefore also
applies to surveillance.
Some minor new features and changes are documented below.
Functions unionSpatialPolygons()
and
intersectPolyCircle()
are now exported. Both are wrappers
around functionality from different packages supporting polygon
operations: for determining the union of all subpolygons of a
"SpatialPolygons"
object, and the intersection of a polygonal
and a circular domain, respectively.
discpoly()
moved back from polyCub
to surveillance.
surveillance now Depends on polyCub (>= 0.4-0)
and not only Imports it (which avoids ::
-references in
.GlobalEnv-made functions).
Nicer default axis labels for iafplot()
.
For twinstim()
, the default is now to trace
every iteration instead of every fifth only.
Slightly changed default arguments for plot.epidata()
:
lwd
(1->2), rug.opts
(col
is set according to
which.rug
)
twinstim()
saves the vector of fixed
coefficients as part of the returned optim.args
component,
such that these will again be held fixed upon update()
.
The plot
-method for hhh4()
-fits allows for
region selection by name.
The polyCub
-methods for cubature over polygonal domains
have been moved to the new dedicated package polyCub,
since they are of a rather general use. The discpoly()
function
has also been moved to that package.
As a replacement for the license-restricted gpclib package,
the rgeos package is now used by default
(surveillance.options(gpclib=FALSE)
) in generating
"epidataCS"
(polygon intersections, slightly slower).
Therefore, when installing surveillance version 1.6-0, the
system requirements for rgeos have to be met, i.e., GEOS
must be available on the system. On Linux variants this means
installing ‘libgeos’ (‘libgeos-dev’).
The improved Farrington method described in Noufaily et al.
(2012) is now available as function farringtonFlexible()
.
New handling of reference dates in algo.farrington()
for
"sts"
objects with epochAsDate=TRUE
. Instead of always
going back in time to the next Date in the "epoch"
slot, the
function now determines the closest Date. Note that this
might lead to slightly different results for the upperbound
compared to previously. Furthermore, the functionality is only
tested for weekly data (monthly data are experimental). The same
functionality applies to farringtonFlexible()
.
To make the different retrospective modelling frameworks of
the surveillance package jointly applicable, it is now possible
to convert (aggregate) "epidataCS"
(continuous-time continuous-space data) into an "sts"
object
(multivariate time series of counts) by the new function
epidataCS2sts
.
Simulation from hhh4
models has
been re-implemented, which fixes a bug and makes it more flexible
and compatible with a wider class of models.
The map
-slot of the "sts"
class now requires
"SpatialPolygons"
(only) instead of
"SpatialPolygonsDataFrame"
.
Re-implementation of oneStepAhead()
for
hhh4
-models with a bug fix, some speed-up and more options.
Slight speed-up for hhh4()
fits,
e.g., by more use of .rowSums()
and .colSums()
.
Crucial speed-up for twinstim()
fits by more efficient
code: mapply
, dropped clumsy for
-loop in
fisherinfo
, new argument cores
for parallel
computing via forking (not available on Windows).
Some further new features, minor changes, and bug fixes are described in the following subsections.
Using tiaf.exponential()
in a twinstim()
now works
with nTypes=1
for multi-type data.
A legend can be added automatically in iafplot()
.
The untie
methods are now able to produce jittered points
with a required minimum separation (minsep
).
simulate.ah4
gained a simplify
argument.
New update
-method for fitted hhh4
-models
(class "ah4"
).
oneStepAhead()
has more options: specify time range
(not only start), choose type of start values, verbose
argument.
pit()
allows for a list of predictive distributions
(pdistr
), one for each observation x
.
New spatial auxiliary function polyAtBorder()
indicating polygons at the border (for a "SpatialPolygons"
object).
animate.epidataCS()
allows for a main
title and
can show a progress bar.
Changed parametrization of zetaweights()
and completed
its documentation (now no longer marked as experimental).
twinstim(...)$converged
is TRUE
if
the optimization routine converged (as before) but contains
the failure message otherwise.
Increased default maxit
for the Nelder-Mead optimizer
in hhh4
from 50 to 300, and removed default artificial lower
bound (-20) on intercepts of epidemic components.
Renamed returned list from oneStepAhead
(mean->pred, x->observed, params->coefficients,
variances->Sigma.orig) for consistency, and
oneStepAhead()$psi
is only non-NULL
if we have a
NegBin model.
Argument order of pit()
has changed, which is also
faster now and got additional arguments relative
and
plot
.
twinstim(...)$runtime
now contains the complete
information from proc.time()
.
Fixed a bug in function
refvalIdxByDate()
which produced empty reference values
(i.e. NA
s) in case the Date entries of epoch
were not
mondays. Note: The function works by subtracting 1:b
years from the
date of the range value and then takes the span -w:w
around this
value. For each value in this set it is determined whether the
closest date in the epoch slot is obtained by going forward or
backward. Note that this behaviour is now slightly changed compared
to previously, where we always went back in time.
algo.farrington()
: Reference values too far back in time
and hence not being in the "epoch"
slot of the "sts"
object are now ignored (previously the resulting NA
s caused the
function to halt). A warning is displayed in this case.
hhh4
: The entry (5,6) of the marginal Fisher
information matrix in models with random intercepts in all three
components was incorrect.
If nlminb
was used as optimizer for the variance parameters
(using the negative marginal Fisher information as Hessian), this
could have caused false convergence (with warning) or minimally
biased convergence (without warning).
As a consequence, the "Sigma.cov"
component of the
hhh4()
result, which is the inverse of the marginal Fisher
information matrix at the MLE, was also wrong.
untie.matrix()
could have produced jittering greater than
the specified amount
.
hhh4
: if there are no random intercepts, the
redundant updateVariance
steps are no longer evaluated.
update.twinstim()
did not work with
optim.args=..1
(e.g., if updating a list of models with lapply).
Furthermore, if adding the model
component only, the
control.siaf
and optim.args
components were lost.
earsC
should now also work with multivariate
sts
time-series objects.
The last week in data(fluBYBW)
(row index 417) has been
removed. It corresponded to week 1 in year 2009 and was wrong
(an artifact, filled with zero counts only).
Furthermore, the regions in @map
are now ordered the same as
in @observed
.
Fixed start value of the overdispersion parameter in
oneStepAhead
(must be on internal log-scale, not
reparametrized as returned by coef()
by default).
When subsetting "sts"
objects in time, @start
was
updated but not @epoch
.
pit
gave NA
results if any x[-1]==0
.
The returned optim.args$par
vector in twinstim()
was missing any fixed parameters.
hhh4()
did not work with time-varying neighbourhood
weights due to an error in the internal checkWeightsArray()
function.
Fixed obsolete .path.package()
calls.
Small corrections in the documentation.
update.twinstim()
performs better in preserving
the original initial values of the parameters.
New pre-defined spatial interaction function
siaf.powerlawL()
, which implements a _L_agged power-law
kernel, i.e. accounts for uniform short-range dispersal.
New method for outbreak detection: earsC
(CUSUM-method described in the CDC Early Aberration Reporting
System, see Hutwagner et al, 2003).
New features and minor bug fixes for the "twinstim
"
part of the package (see below).
Yet another p-value formatting function formatPval()
is now also part of the surveillance package.
polyCub.SV()
now also accepts objects of classes
"Polygon"
and "Polygons"
for convenience.
siaf.lomax
is deprecated and replaced by
siaf.powerlaw
(re-parametrization).
twinstim()
-related) The temporal plot
-method for class "epidataCS"
now understands the add
parameter to add the histogram to an
existing plot window, and auto-transforms the t0.Date
argument using as.Date()
if necessary.
nobs()
methods for classes "epidataCS"
and
"twinstim"
.
New argument verbose
for twinstim()
which, if
set to FALSE
, disables the printing of information messages
during execution.
New argument start
for twinstim()
, where (some)
initial parameter values may be provided, which overwrite those in
optim.args$par
, which is no longer required (as a naive
default, a crude estimate for the endemic intercept and zeroes for
the other parameters are used).
Implemented a wrapper stepComponent()
for step()
to perform algorithmic component-specific model selection in
"twinstim"
models. This also required the implementation of
suitable terms()
and extractAIC()
methods. The single-step
methods add1()
and drop1()
are also available.
The update.twinstim()
method now by default uses the
parameter estimates from the previous model as initial values for
the new fit (new argument use.estimates = TRUE
).
as.epidataCS()
checks for consistency of the area of
W
and the (now really obligatory) area column in
stgrid
.
simulate.twinstim()
now by default uses the previous
nCircle2Poly
from the data
argument.
direction
argument for untie.epidataCS()
.
The toLatex
-method for "summary.twinstim"
got
different defaults and a new argument eps.Pvalue
.
New xtable
-method for "summary.twinstim"
for
printing the covariate effects as risk ratios (with CI's and p-values).
hhh4()
-related) New argument hide0s
in the plot
-method for class
"ah4"
.
New argument timevar
for addSeason2formula()
,
which now also works for long formulae.
The surveillance package is again backward-compatible with R version 2.14.0, which is now declared as the minimum required version.
This new version mainly improves upon the twinstim()
and
hhh4()
implementations (see below).
As requested by the CRAN team, examples now run faster. Some
are conditioned on the value of the new package option
"allExamples"
, which usually defaults to TRUE
(but is
set to FALSE
for CRAN checking, if timings are active).
Moved some rarely used package dependencies to “Suggests:”, and also removed some unused packages from there.
Dropped strict dependence on gpclib, which has a
restricted license, for the surveillance package to be clearly
GPL-2. Generation of "epidataCS"
objects, which makes use of
gpclib's polygon intersection capabilities, now requires prior
explicit acceptance of the gpclib license via setting
surveillance.options(gpclib = TRUE)
. Otherwise,
as.epidataCS()
and simEpidataCS()
may not be used.
twinstim()
-related) Speed-up by memoisation of the siaf
cubature (using
the memoise package).
Allow for nlm
-optimizer (really not recommended).
Allow for nlminb
-specific control arguments.
Use of the expected Fisher information matrix can be disabled
for nlminb
optimization.
Use of the effRange
-trick can be disabled in
siaf.gaussian()
and siaf.lomax()
. The default
effRangeProb
argument for the latter has been changed from
0.99 to 0.999.
The twinstim()
argument nCub
has been replaced
by the new control.siaf
argument list. The old
nCub.adaptive
indicator became a feature of the
siaf.gaussian()
generator (named F.adaptive
there) and
does no longer depend on the effRange
specification, but uses
the bandwidth adapt*sd
, where the adapt
parameter may be
specified in the control.siaf
list in the twinstim()
call. Accordingly, the components "nCub"
and
"nCub.adaptive"
have been removed from the result
of twinstim()
, and are replaced by "control.siaf"
.
The "method"
component of the twinstim()
result
has been replaced by the whole "optim.args"
.
The new "Deriv"
component of siaf
specifications
integrates the “siaf$deriv” function over a polygonal domain.
siaf.gaussian()
and siaf.lomax()
use polyCub.SV()
(with intelligent alpha
parameters) for this task
(previously: midpoint-rule with naive bandwidth)
scaled
iafplot()
(default FALSE
). The
ngrid
parameter has been renamed to xgrid
and is more
general.
The "simulate"
component of siaf
's takes an
argument ub
(upperbound for distance from the source).
Numerical integration of spatial interaction functions with an
Fcircle
trick is more precise now; this slightly changes
previous results.
New S3-generic untie()
with a method for the
"epidataCS"
class (to randomly break tied event times and/or
locations).
Renamed N
argument of polyCub.SV()
to
nGQ
, and a
to alpha
, which both have new
default values.
The optional polygon rotation proposed by Sommariva &
Vianello is now also implemented (based on the corresponding MATLAB
code) and available as the new rotation
argument.
The scale.poly()
method for "gpc.poly"
is now
available as scale.gpc.poly()
. The default return class of
discpoly()
was changed from "gpc.poly"
to
"Polygon"
.
An intensityplot()
-method is now also implemented for
"simEpidataCS"
.
hhh4()
-related)Significant speed-up (runs about 6 times faster now, amongst others by many code optimizations and by using sparse Matrix operations).
hhh4()
optimization routines can now be customized for
the updates of regression and variance parameters seperately, which
for instance enables the use of Nelder-Mead for the variance
updates, which seems to be more stable/robust as it does
not depend on the inverse Fisher info and is usually faster.
The ranef()
extraction function for "ah4"
objects
gained a useful tomatrix
argument, which re-arranges random
effects in a unit x effect matrix (also transforming CAR effects
appropriately).
Generalized hhh4()
to also capture parametric
neighbourhood weights (like a power-law decay). The new function
nbOrder()
determines the neighbourhood order matrix
from a binary adjacency matrix (depends on package spdep).
New argument check.analyticals
(default FALSE
)
mainly for development purposes.
Fixed sign of observed Fisher information matrix in
twinstim
.
Simulation from the Lomax kernel is now correct (via polar coordinates).
Fixed modifyListcall()
to also work with updated NULL
arguments.
Fixed wrong Fisher information entry for the overdispersion
parameter in hhh4
-models.
Fixed wrong entries in penalized Fisher information wrt the combination fixed effects x CAR intercept.
Fixed indexing bug in penalized Fisher calculation in the case of multiple overdispersion parameters and random intercepts.
Fixed bug in Fisher matrix calculation concerning the relation of unit-specific and random effects (did not work previously).
Improved handling of non-convergent / degenerate solutions during
hhh4
optimization. This involves using ginv()
from
package MASS, if the penalized Fisher info is singular.
Correct labeling of overdispersion parameter in
"ah4"
-objects.
Some control arguments of hhh4()
have more clear
defaults.
The result of algo.farrington.fitGLM.fast()
now
additionally inherits from the "lm"
class to avoid warnings
from predict.lm()
about fake object.
Improved ‘NAMESPACE’ imports.
Some additional tiny bug fixes, see the subversion log on R-Forge for details.
This is mainly a patch release for the twinstim
-related
functionality of the package.
Apart from that, the package is now again compatible with older
releases of R (< 2.15.0) as intended (by defining paste0()
in
the package namespace if it is not found in R base at
installation of the surveillance package).
Important new twinstim()
-feature: fix parameters
during optimization.
Useful update
-method for "twinstim"
-objects.
New [[
- and plot
-methods for
"simEpidataCSlist"
-objects.
simEpidataCS()
received tiny bug fixes and is now
able to simulate from epidemic-only models.
R0
-method for "simEpidataCS"
-objects (actually
a wrapper for R0.twinstim()
).
Removed dimyx
and eps
arguments from
R0.twinstim()
; now uses nCub
and
nCub.adaptive
from the fitted model and applies the same
(numerical) integration method.
animate.epidata
is now compatible with the
animation package.
More thorough documentation of "twinstim"
-related
functions including many examples.
"twinstim"
-related)nlminb
(instead of optim
's "BFGS"
) is
now the default optimizer (as already documented).
The twinstim
-argument nCub
can now be omitted when
using siaf.constant()
(as documented) and is internally set to
NA_real_
in this case. Furthermore, nCub
and
nCub.adaptive
are set to NULL
if there is
no epidemic component in the model.
toLatex.summary.twinstim
now again works for
summary(*, test.iaf=FALSE)
.
print
- and summary
-methods for
"epidataCS"
no longer assume that the BLOCK
index
starts at 1, which may not be the case when using a subset in
simulate.twinstim()
.
The "counter"
step function returned by
summary.epidataCS()
does no longer produce false
numbers of infectives (they were lagged by one timepoint).
plot.epidataCS()
now resolves ... correctly and
the argument colTypes
takes care of a possible
subset
.
simEpidataCS()
now also works for endemic-only models
and is synchronised with twinstim()
regarding the
way how siaf
is numerically integrated (including the
argument nCub.adaptive
).
Fixed problem with simEpidataCS()
related to missing
‘NAMESPACE’ imports (and re-exports) of marks.ppp
and
markformat.default
from spatstat, which are required
for spatstat::runifpoint()
to work, probably because
spatstat currently does not register its S3-methods.
Improved error handling in simEpidataCS()
. Removed a
browser()
-call and avoid potentially infinite loop.
"twinSIR"
-related) The .allocate
argument of simEpidata()
has
now a fail-save default.
Simulation without endemic cox()
-terms now works.
Simplified imdepi
data to monthly instead of weekly
intervals in stgrid
for faster examples and reduced package
size.
The environment of all predefined interaction functions for
twinstim()
is now set to the .GlobalEnv
. The previous
behaviour of defining them in the parent.frame()
could have
led to huge save()
's of "twinstim"
objects even with
model=FALSE
.
simulate.twinSIR
only returns a list of epidemics if
nsim > 1
.
simulate.twinstim
uses nCub
and
nCub.adaptive
from fitted object as defaults.
Removed the ...-argument from simEpidataCS()
.
The coefficients returned by simEpidataCS()
are now stored
in a vector rather than a list for compatibility with
"twinstim"
-methods.
Argument cex.fun
of intensityplot.twinstim()
now
defaults to the sqrt
function (as in plot.epidataCS()
.
Besides minor bug fixes, additional functionality has entered the package and a new attempt is made to finally release a new version on CRAN (version 1.3 has not appeared on CRAN), including a proper ‘NAMESPACE’.
Support for non-parametric back-projection using the function
backprojNP()
which returns an object of the new
"stsBP"
class which inherits from "sts"
.
Bayesian nowcasting for discrete time count data is implemented in
the function nowcast()
.
Methods for cubature over polygonal domains can now also visualize what
they do. There is also a new quasi-exact method for cubature of the
bivariate normal density over polygonal domains. The
function polyCub()
is a wrapper for the different
methods.
residuals.twinstim()
and residuals.twinSIR()
:
extract the “residual process”, see Ogata
(1988). The residuals of "twinSIR"
and
"twinstim"
models may be checked graphically by the new
function checkResidualProcess()
.
Many new features for the "twinstim"
class of
self-exciting spatio-temporal point process models (see
below).
"twinstim"
Modified arguments of twinstim()
: new ordering, new
argument nCub.adaptive
, removed argument
typeSpecificEndemicIntercept
(which is now specified as part of
the endemic
formula as (1|type)
).
Completely rewrote the R0
-method (calculate “trimmed” and
“untrimmed” R_0 values)
The “trimmed” R0
values are now part of the
result of the model fit, as well as bbox(W)
. The
model evaluation environment is now set as attribute of the
result if model=TRUE
.
New predefined spatial kernel: the Lomax power law kernel
siaf.lomax()
plot
-methods for "twinstim"
(intensityplot()
and iafplot()
)
as.epidataCS()
now auto-generates the stop-column if this is missing
print
-method for class "summary.epidataCS"
[
- and subset-method for "epidataCS"
(subsetting ...$events
)
plot
-method for "epidataCS"
Improved documentation for the new functionalities.
Updated references.
twinSIR
's intensityPlot
is now a method of the
new S3-generic function intensityplot
.
This is a major realease integrating plenty of new code (unfortunately
not all documented as good as it could be). This includes code
for the "twinstim"
and the "hhh4"
model.
The "twinSIR"
class of models has been
migrated from package RLadyBug to surveillance.
It may take a while before this version will become available from CRAN.
For further details see below.
Renamed the "week"
slot of the "sts"
S4 class to "epoch"
.
All saved data objects have accordingly be renamed, but some hazzle
is to be expected if one you have old "sts"
objects stored in binary
form. The function convertSTS()
can be used to
convert such “old school” "sts"
objects.
Removed the functions algo.cdc()
and algo.rki()
.
Support for "twinSIR"
models (with associated
"epidata"
objects) as described
in Höhle (2009) has been moved from package
RLadyBug to surveillance.
That means continuous-time discrete-space SIR models.
Support for "twinstim"
models as described in
Meyer et al (2012). That means continuous-time
continuous-space infectious disease models.
Added functionality for non-parametric back projection
(backprojNP()
) and
now-casting (nowcast()
) based on "sts"
objects.
Replaced the deprecated getSpPPolygonsLabptSlots method with calls to the coordinates method when plotting the map slot.
Minor proof-reading of the documentation.
Added an argument "extraMSMargs"
to the algo.hmm function.
Fixed bug in outbreakP()
when having observations equal to zero
in the beginning. Here, \hat{μ}^{C1} in (5) of Frisen et al (2008)
is zero and hence the log-based summation in the code failed.
Changed to product as in the original code, which however might be
less numerically stable.
Fixed bug in stcd which added one to the calculated index of idxFA and idxCC. Thanks to Thais Rotsen Correa for pointing this out.
Added algo.outbreakP()
(Frisen & Andersson, 2009) providing a
semiparametric approach for outbreak detection for Poisson
distributed variables.
Added a pure R function for extracting ISO week and year from Date
objects. This function (isoWeekYear) is only called if "%G" and "%V"
format strings are used on Windows (sessionInfo()[[1]]$os == "mingw32"
)
as this is not implemented for "format.Date"
on Windows.
Thanks to Ashley Ford, University of Warwick, UK for identifying
this Windows specific bug.
For algo.farrington()
a faster fit routine "algo.farrington.fitGLM.fast"
has been provided by Mikko Virtanen, National Institute for Health
and Welfare, Finland. The new function calls glm.fit()
directly, which gives a doubling of speed for long series. However, if one
wants to process the fitted model output some of the GLM routines might
not work on this output. For backwards compability the argument
control$fitFun = "algo.farrington.fitGLM"
provides the old (and slow)
behaviour.
A few minor bug fixes
Small improvements in the C-implementation of the twins()
function by Daniel Sabanés Bové fixing the segmentation fault
issue on 64-bit architectures.
Added the functions categoricalCUSUM and LRCUSUM.runlength for the CUSUM monitoring of general categorical time series (binomial, beta-binomial, multinomial, ordered response, Bradley-Terry models).
Added the functions pairedbinCUSUM and pairedbinCUSUM.runlength implementing the CUSUM monitoring and run-length computations for a paired binary outcome as described in Steiner et al. (1999).
Experimental implementation of the prospective space-time cluster detection described in Assuncao and Correa (2009).
Added a demo("biosurvbook")
containing the code of an upcoming
book chapter on how to use the surveillance package. This
contains the description of ISO date use, negative binomial CUSUM,
run-length computation, etc. From an applicational point of view
the methods are illustrated by Danish mortality monitoring.
Fixed a small bug in algo.cdc found by Marian Talbert Allen which resulted in the control$m argument being ignored.
The constructor of the sts class now uses the argument
"epoch"
instead of weeks to make clearer that also
daily, monthly or other data can be handled.
Added additional epochAsDate slot to sts class. Modified plot functions so they can handle ISO weeks.
algo.farrington now also computes quantile and median of the predictive distribution. Furthermore has the computation of reference values been modified so its a) a little bit faster and b) it is also able to handle ISO weeks now. The reference values for date t0 are calculated as follows: For i, i=1,..., b look at date t0 - i*year. From this date on move w months/weeks/days to the left and right. In case of weeks: For each of these determined time points go back in time to the closest Monday
Renamed the functions obsinyear to epochInYear, which now also handles objects of class Date.
Negative Binomial CUSUM or the more general NegBin likelihood ratio detector is now implemented as part of algo.glrnb. This includes the back calculation of the required number of cases before an alarm.
Time varying proportion binomial CUSUM.
Current status: Development version available from http://surveillance.r-forge.r-project.org/
Rewriting of the plot.sts.time.one function to use polygons
instead of lines for the number of observed cases. Due cause
a number of problems were fixed in the plotting of the legend.
Plotting routine now also handles binomial data, where the
number of observed cases y are stored in "observed"
and the
denominator data n are stored in "populationFrac"
.
Problems with the aggregate function not operating correctly for the populationFrac were fixed.
The "rogerson"
wrapper function for algo.rogerson was modified so it
now works better for distribution "binomial"
. Thus a time varying
binomial cusum can be run by calling
rogerson( x, control(..., distribution="binomial"))
An experimental implementation of the twins model documented in Held, L., Hofmann, M., Höhle, M. and Schmid V. (2006) A two-component model for counts of infectious diseases, Biostatistics, 7, pp. 422–437 is now available as algo.twins.
Fixed a few small problems which gave warnings in the CRAN distribution
The algo_glrpois function now has an additional "ret"
arguments,
where one specifies the return type. The arguments of the underlying
c functions have been changed to include an additional direction and
return type value arguments.
added restart argument to the algo.glrpois control object, which allows the user to control what happens after the first alarm has been generated
experimental algo.glrnb function is added to the package. All calls to algo.glrpois are now just alpha=0 calls to this function. However, the underlying C functions differentiate between poisson and negative case