The CRS
of data(imdepi)
and data(measlesWeserEms)
have been updated via sp
’s rebuild_CRS()
to avoid warnings when rgdal is loaded with new PROJ and GDAL libraries.
simEpidataCS()
now internally resets the CRS (temporary), which avoids spurious warnings and also reduces its runtime by about 25%.
Fix encoding error in vignette("twinstim")
for CRAN’s non-UTF8 Linux test machine.
This version of surveillance (formally) requires the new spatstat umbrella package to avoid collisions of old spatstat and its new sub-packages (we only use spatstat.geom). The spatstat dependence will be dropped in the future.
The epoch<-
replacement method for "sts"
objects now accepts a "Date"
vector. The standard plots may give nicer x-axis annotation if indexed by dates. See the xaxis.*
arguments of stsplot_time()
.
tidy.sts()
(and thus autoplot.sts()
) failed for date-indexed "sts"
objects with non-standard frequencies. [spotted by Junyi Lu]
The nowcast()
function with method="bayes.trunc.ddcp"
now adds support for negative binomial response distribution instead of Poisson. Furthermore, additional components of the design matrix for the discrete time survival model can be provided, which allows the inclusion of, e.g., day of the week effects. Finally, the order of the polynomial created by the change-points in the discrete time survival model can now be specified. For further details see the work of Guenther et al. (2020) about nowcasting the Covid-19 outbreak in Bavaria, Germany.
animate.sts()
can position the timeplot
on other sides of the map.
The weighted sum in the ne
ighbourhood component of hhh4()
models is computed more efficiently.
simEpidataCS()
(and thus simulate.twinstim()
) uses a slightly more efficient location sampler for models with siaf = siaf.constant()
. Simulation results will differ from previous package versions even if the same random seed
is used.
The default main
title for stsplot_space()
now uses the ISO year-week format for weekly "sts"
data.
Bug fix in the farringtonFlexible()
-function, which for the argument thresholdMethod=="nbPlugin"
and thresholdMethod=="muan"
unfortunately computed the limit as an (1-alpha/2)
prediction interval instead of the documented (1-alpha)
prediction interval. This affects four threshold values in Table 2 of vignette("monitoringCounts")
. The default method "delta"
worked as expected.
In hhh4()
models without AR component, the matrix of fitted values could lack column names.
Experimental time-varying neighbourhood weights in hhh4()
were indexed differently in model fitting and in the simulate()
method (undocumented behaviour). Both now use the latter variant, where the mean at time t uses products of weights at time t and observed counts at time t-1. [reported by Johannes Bracher]
For weekly sts
indexed via start
and freq=52
, epoch(sts, as.Date=TRUE)
now interprets the start
week according to ISO 8601. For example, start = c(2020, 5)
corresponds to 2020-01-27, not 2020-02-03. This affects as.xts.sts()
and the time plot in animate.sts()
.
stsplot_space()
automatically extends manual color breaks (at
), if the intervals do not cover the data range.
simEndemicEvents()
and thus epitest(..., method="simulate")
are no longer slowed down by intermediate CRS()
computations.
Removed unused rmapshaper from “Suggests” and moved xts to “Enhances” (used only for as.xts.sts
).
Switched testing framework from (nowadays heavy) testthat to tinytest. Together with moving ggplot2 to “Enhances” (used only for autoplot.sts
) — and only then — this switch further reduces the total number of required packages for a complete check (i.e., installing with dependencies = TRUE
) in a factory-fresh R environment from 119 to 94.
spatstat was split into several sub-packages, of which we only need to import spatstat.geom. This new package requires R >= 3.5.0
, though.
surveillance now requires R >= 3.6.0
.
New spatial interaction function for twinstim()
: siaf.exponential()
implements the exponential kernel f(x) = exp(-x/σ), which is a useful alternative if the two-parameter power-law kernel is not identifiable.
The plot
-type "maps"
for "hhh4"
fits, plotHHH4_maps()
, now allows for map-specific color keys via zmax = NA
(useful for prop = TRUE
).
The nowcast()
-function now also works for method="bayes.trunc.ddcp"
method when the number of breakpoints is greater than 1.
The amplitudeShift
transformation for sine-cosine coefficient pairs in the summary
of multivariate "hhh4"
models was incorrect in the rare case that the model used unit-specific seasonal terms (addSeason2formula
with length(S) > 1
).
algo.hhh()
implementation of the HHH model has been removed from the package. The function hhh4()
provides an improved and much extended implementation since 2012.The head()
-method for "epidataCS"
objects did not work with a negative n
argument.
Fix for "matrix"
changes in R-devel.
sts()
now checks for mismatches in column names of supplied matrices (observed
, population
, neighbourhood
, …). This is to catch input where the units (columns) are ordered differently in different slots, which would flaw subsequent analyses.simulate.twinSIR()
ignored the atRiskY
indicator of the underlying "epidata"
, so always assumed a completely susceptible population. Initially infectious individuals are now inherited. For the previous behaviour, adjust the supplied data
via data$atRiskY <- 1
.siaf.powerlaw1()
with fixed sigma = 1
. Useful if sigma
is difficult to estimate with siaf.powerlaw()
.pit()
’s default ylab
was wrong (default are densities not relative frequencies).
R0()
for "twinstim"
fits with specified newevents
now handles levels of epidemic factor variables automatically via the new xlevels
attribute stored in the fitted model.
Some S3 methods for the "sts"
class are now formally registered and identical to the established S4 methods.
Minor additions and fixes in the package documentation.
hcl.colors()
, exported since 1.14.0, has been renamed .hcl.colors()
and is now internal again, to avoid a name clash with R’s own such function introduced in R 3.6.0.W_powerlaw(..., from0 = TRUE)
enables more parsimonious hhh4
models in that the power-law weights are modified to include the autoregressive (0-distance) case (see vignette("hhh4_spacetime")
). The unstructured distance weights W_np()
gained from0
support as well.
sts()
creation can now handle epoch
arguments of class Date
directly.
The ranef()
-method for "hhh4"
fits gained a logical argument intercept
to extract the unit-specific intercepts of the log-linear predictors instead of the default zero-mean deviations around the fixed intercepts. The corresponding plot
method (type="ri"
) gained an argument exp
: if set to TRUE
random effects are exp
-transformed and thus show multiplicative effects. [based on feedback by Tim Pollington]
W_np()
’s argument to0
has been renamed to truncate
. The old name still works but is deprecated.
plotHHH4_ri()
now uses cm.colors(100)
as col.regions
, and 0-centered color breaks by default.
The help pages of twinSIR()
and related functions now give examples based on data("hagelloch")
instead of using the toy dataset data("fooepidata")
. The latter is now obsolete and will be removed in future versions of the package.
The elements of the control
list stored in the result of algo.farrington()
are now consistently ordered as in the default control
argument.
Using negative indices to exclude time points from an "sts"
object (e.g., x[-1,]
) is now supported and equivalent to the corresponding subset expression of retained indexes (x[2:nrow(x),]
) in resetting the start
and epoch
slots. [reported by Johannes Bracher]
For weekly "sts"
data with epochAsDate=TRUE
, the as.data.frame()
method computed freq
by "%Y"
-year instead of by "%G"
-year, which was inconsistent with the epochInPeriod
variable.
For non-weekly "sts"
data with epochAsDate=TRUE
, year()
as well as the year
column of the tidy.sts()
output corresponded to the ISO week-based year. It now gives the calendar year.
sts_creation()
hard-coded start = c(2006, 1)
.
aggregate()
ing an "sts"
object over time now recomputes fractions from the cumulated population values if and only if this is no multinomialTS
and already contains population fractions. The same rule holds when subsetting units of an "sts"
object. The aggregate
-method previously failed to recompute fractions in some cases.
For farringtonFlexible()
with multivariate time series, only the last unit had stored the additional control items (exceedence scores, p-values, …), all others were 0. [reported by Johannes Bracher]
The supplementary p-values returned by farringtonFlexible()
in control$pvalue
were wrong for the default approach, where thresholdMethod="delta"
(the original Farrington method) and a power transformation was applied to the data (powertrans != "none"
). Similarly, algo.farrington()
returned wrong predictive probabilities in control$pd[,1]
if a power transformation was used. [reported by Lore Merdrignac]
The control
argument list of algo.farrington()
as stated in the formal function definition was incomplete (plot
was missing) and partially out of sync with the default values that were actually set inside the function (b=5
and alpha=0.05
). This has been fixed. Results of algo.farrington()
would only be affected if the function was called without any control
options (which is hardly possible). So this can be regarded as a documentation error. The formal control
list of the farrington()
wrapper function has been adjusted accordingly.
The control
argument lists of farringtonFlexible()
and bodaDelay()
as stated in the formal function definitions were partially out of sync with respect to the following default values that were actually set inside these functions: b=5
(not 3), alpha=0.05
(not 0.01), pastWeeksNotIncluded=w
(not 26), and, for bodaDelay()
only, delay=FALSE
(not TRUE
). This has been fixed. Results would only be affected if the functions were called without any control
options (which is hardly possible). So this can be regarded as a documentation error.
pairedbinCUSUM()
did not properly subset the sts
object if a range
was specified, and forgot to store the control
arguments in the result.
wrap.algo()
now aborts if the monitored range is not supplied as a numeric vector.
In vignette("monitoringCounts")
: several inconsistencies between code and output have been fixed.
epidataCS2sts()
no longer transfers the stgrid$BLOCK
indices to the epoch
slot of the resulting "sts"
object (to avoid epoch[1] != 1
scenarios).
The ranef()
matrix extracted from fitted "hhh4"
models could have wrong column names.
compMatrix.writeTable()
, makePlot()
, test()
, testSim()
, readData()
(the raw txt files have been removed as well), correct53to52()
, enlargeData()
, toFileDisProg()
.autoplot.sts()
gained a width
argument to adjust the bar width, which now defaults to 7 for weekly time series (previously was 90% of that so there were gaps between the bars).
"epidataCS"
generation now (again) employs spatstat’s bdist.points()
, which has been accelerated in version 1.56-0. If you use the twinstim()
-related modelling part of surveillance, you are thus advised to update your spatstat installation.
The boda()
examples in vignette("monitoringCounts")
have been updated to also work with recent versions of INLA.
Offsets in hhh4
’s epidemic components were ignored by simulate.hhh4()
[spotted by Johannes Bracher] as well as in dominant eigenvalues (“maxEV”).
The color key in fanplot()
is no longer distorted by log="y"
.
autoplot.sts()
now sets the calling environment as the plot_env
of the result.
Several twinstim
-related functions finally allow for prehistory events (long supported by twinstim()
itself): as.epidataCS()
, glm_epidataCS()
, as.epidata.epidataCS()
.
The summary()
for SI[R]S-type "epidata"
failed if there were initially infectious individuals.
qlomax()
, readData()
, toFileDisProg()
, correct53to52()
, enlargeData()
, compMatrix.writeTable()
, test()
, testSim()
, makePlot()
.The as.data.frame()
method for "sts"
objects gained a tidy
argument, which enables conversion to the long data format and is also available as function tidy.sts()
.
A ggplot2 variant of stsplot_time()
is now available via autoplot.sts()
.
as.epidata.data.frame()
gained an argument max.time
to specify the end of the observation period (which by default coincides with the last observed event).
The now exported function fanplot()
wraps fanplot::fan()
. It is used by plot.oneStepAhead()
and plot.hhh4sims()
, which now have an option to add the point forecasts to the fan as well.
plotHHH4_fitted()
(and plotHHH4_fitted1()
) gained an option total
to sum the fitted components over all units.
Package polyCub is no longer automatically attached (only imported).
scores.oneStepAhead()
no longer reverses the ordering of the time points by default, as announced in 1.15.0.
aggregate(stsObj, by = "unit")
no longer results in empty colnames (set to "overall"
). The obsolete map is dropped.
The subset
argument of twinSIR()
was partially ignored:
If nIntervals = 1
, the model summary()
reported the total number of events.
Automatic knots
, model residuals()
, as well as the rug in intensityplot()
were computed from the whole set of event times.
The as.epidata.data.frame()
converter did not actually allow for latent periods (via tE.col
). This is now possible but considered experimental (methods for "epidata"
currently ignore latent periods).
The all.equal()
methods for "hhh4"
and "twinstim"
objects now first check for the correct classes.
siaf.gaussian()
now also employs a polyCub.iso()
integration routine by default (similar to the powerlaw-type kernels), instead of adaptive midpoint cubature. This increases precision and considerably accelerates estimation of twinstim()
models with a Gaussian spatial interaction function. Models fitted with the new default (F.adaptive=FALSE, F.method="iso"
) will likely differ from previous fits (F.adaptive=TRUE
), and the numerical difference depends on the adaptive bandwidth used before (the default adapt=0.1
yielded a rather rough approximation of the integral).
Added quantile()
, confint()
, and plot()
methods for "oneStepAhead"
predictions.
Exported the function simEndemicEvents()
to simulate a spatio-temporal point pattern from an endemic-only "twinstim"
; faster than via the general simulate.twinstim()
method.
twinstim(..., siaf = siaf.gaussian())
uses a larger default initial value for the kernel’s standard deviation (based on the size of the observation region).
Non-default parametrizations of siaf.gaussian()
are deprecated, i.e., always use logsd=TRUE
and density=FALSE
.
twinstim()
uses a smaller default initial value for the epidemic intercept, which usually allows for faster convergence.
update.hhh4()
now allows subset.upper
values beyond the originally fitted time range (but still within the time range of the underlying "sts"
object).
scores.oneStepAhead()
by default reverses the ordering of the time points. This awkward behaviour will change in the next version, so the method now warns if the default reverse=TRUE
is used without explicit specification.
Minor improvements in the documentation and some vignettes: corrected typos, simplified example code, documented some methods.
The C-routines introduced in version 1.14.0 used ==
comparisons on parameter values to choose among case-specific formulae (e.g., for d==2 in siaf.powerlaw()
). We now employ an absolute tolerance of 1e-7 (which should fix the failing tests on Solaris).
Interaction functions for twinstim()
, such as siaf.powerlaw()
or tiaf.exponential()
, no longer live in the global environment as this risks using masked base functions.
demo("v77i11")
. It exemplifies the spatio-temporal endemic-epidemic modelling frameworks twinstim
, twinSIR
, and hhh4
(see also the corresponding vignettes).Pure C-implementations of integration routines for spatial interaction functions considerably accelerate the estimation of twinstim()
models containing siaf.powerlaw()
, siaf.powerlawL()
, or siaf.student()
.
The color palette generating function used by sts
plots, hcl.colors
, is now exported.
The utility function clapply
(conditional lapply
) is now exported.
Some utility functions for hhh4
fits are now exported (update.hhh4
, getNEweights
, coefW
), as well as several internal functions for use by hhh4
add-on packages (meanHHH
, sizeHHH
, decompose.hhh4
).
The "fan"
-type plot function for "hhh4sims"
gained a key.args
argument for an automatic color key.
New auxiliary function makeControl()
, which may be used to specify a hhh4()
model.
twinstim()
now throws an informative error message when trying to fit a purely epidemic model to data containing endemic events (i.e., events without ancestors). The help("twinstim")
exemplifies such a model.siaf.powerlaw()$deriv
returned NaN
for the partial derivative wrt the decay parameter d, if d was large enough for f to be numerically equal to 0. It will now return 0 in this case.
twinstim()
could fail (with an error from duplicated.default
) if the fitted time range was substantially reduced via the T
argument.
The "simEpidataCSlist"
generated by simulate.twinstim(..., simplify = TRUE)
was missing the elements bbox
and control.siaf
.
vignette("twinstim")
, vignette("twinSIR")
, and vignette("hhh4_spacetime")
, respectively.The calibrationTest()
and pit()
methods for "oneStepAhead"
forecasts gained an argument units
to allow for unit-specific assessments.
A default scores
-method is now available to compute a set of proper scoring rules for Poisson or NegBin predictions.
New plot type = "fan"
for simulations from "hhh4"
models to produce a fan chart using the fanplot package.
scores.hhh4()
sets rownames for consistency with scores.oneStepAhead()
."Lambda.const"
matrix returned by getMaxEV_season()
was wrong for models with asymmetric neighbourhood weights. [spotted by Johannes Bracher]"maxEV"
) were not affected by this bug.earsC
now has two new arguments thanks to Howard Burkom: the number of past time units to be used in calculation is now not always 7, it can be chosen in the baseline
parameter. Furthermore, the minSigma
parameter allows to get a threshold in the case of sparse data. When one doesn’t give any value for those two parameters, the algorithm works like it used to.
animate.sts()
gained support for date labels in the bottom timeplot
.
stsplot_space()
and animate.sts()
can now generate incidence maps based on the population information stored in the supplied "sts"
object. Furthermore, animate.sts()
now supports time-varying population numbers.
hhh4()
guards against the misuse of family = factor("Poisson")
for univariate time series. Previously, this resulted in a negative binomial model by definition, but is now interpreted as family = "Poisson"
(with a warning).animate.sts()
now supports objects with missing values (with a warning). Furthermore, the automatic color breaks have been improved for incidence maps, also in stsplot_space()
.
The as.data.frame
-method for the "sts"
class, applied to classical time-index-based "sts"
objects (epochAsDate=FALSE
), ignored a start
epoch different from 1 when computing the epochInPeriod
indexes. Furthermore, the returned epochInPeriod
now is a fraction of freq
, for consistency with the result for objects with epochAsDate=TRUE
.
simulate.hhh4()
did not handle shared overdispersion parameters correctly. The different parameters were simply recycled to the number of units, ignoring the factor specification from the model’s family
. [spotted by Johannes Bracher]
Simulations from endemic-only "hhh4"
models with unit-specific overdispersion parameters used wrong variances. [spotted by Johannes Bracher]
oneStepAhead()
predictions of type
"rolling"
(or "first"
) were incorrect for time points tp
(tp[1]
) beyond the originally fitted time range (in that they were based on the original time range only). This usage of oneStepAhead()
was never really supported and is now catched when checking the tp
argument.
plot.hhh4simslist()
ignored its par.settings
argument if groups=NULL
(default).
The internal auxiliary function, which determines the sets of potential source events in "epidataCS"
has been implemented in C++, which accelerates as.epidataCS()
, permute.epidataCS()
, and therefore epitest()
. This is only really relevant for "epidataCS"
with a large number of events (>1000, say).
Negative-binomial hhh4()
models may not converge for non-overdispersed data (try, e.g., set.seed(1); hhh4(sts(rpois(104, 10)), list(family="NegBin1"))
). The resulting non-convergence warning message now mentions low overdispersion if this is detected. [suggested by Johannes Bracher]
An additional type="delay"
option was added to the plot
method of stsNC
objects. Furthermore, an animate_nowcasts
function allows one to animate a sequence of nowcasts.
animate
-method for "sts"
objects, the default top padding of lattice plots is now disabled for the bottom timeplot
to reduce the space between the panels. Furthermore, the new option fill
can be used to make the panel of the timeplot
as large as possible.bodaDelay()
: fixed spurious warnings from rnbinom()
.
vignette("monitoringCounts")
: fixed boda
-related code and cache to obtain same results as in corresponding JSS paper.
vignette("monitoringCounts")
illustrates the monitoring of count time series in R with a particular focus on aberration detection in public health surveillance. This vignette corresponds to a recently accepted manuscript for the Journal of Statistical Software (Salmon, Schumacher, and Höhle, 2016).The code of boda()
(with samplingMethod="joint"
) and bodaDelay()
(with inferenceMethod="INLA"
) has been adjusted to a change of arguments of INLA’s inla.posterior.sample
function. Accordingly, the minimum INLA version required to run boda()
and bodaDelay()
is 0.0-1458166556.
The functions returned by W_powerlaw()
now have the package namespace as their environment to support situations where the package is not attached.
Attaching package nlme after surveillance no longer masks "hhh4"
’s ranef
-method. (We now import the fixef
and ranef
generics from nlme.)
Several new vignettes illustrate endemic-epidemic modeling frameworks for spatio-temporal surveillance data:
vignette("twinstim")
describes a spatio-temporal point process regression model.
vignette("twinSIR")
describes a multivariate temporal point process regression model.
vignette("hhh4_spacetime")
describes an areal time-series model for infectious disease counts.
These vignettes are based on a recently accepted manuscript for the Journal of Statistical Software (Meyer, Held, and Höhle, 2016).
Improved the documentation on various help pages.
The hhh4()
-based analysis of data("fluBYBW")
has been moved to a separate demo script ‘fluBYBW.R’. Due to the abundance of models and the relatively long runtime, we recommend to open the script in an editor rather than running all the code at once using demo("fluBYBW")
.
Overhaul of the "sts"
implementation. This mostly affects package-internal code, which is simpler, cleaner and better tested now, but requires R >= 3.2.0 (due to callNextMethod()
bugs in older versions of R). Beyond that, the user-level constructor function sts()
now has explicit arguments for clarity and convenience. For instance, its first argument sets the observed
slot and no longer needs to be named, i.e., sts(mycounts, start=c(2016,3), frequency=12)
works just like for the classical ts()
function.
stsplot_time(..., as.one=TRUE)
is now implemented (yielding a simple matplot
of multiple time series).
plotHHH4_season()
now by default draws a horizontal reference line at unity if the multiplicative effect of component seasonality is shown (i.e., if intercept=FALSE
).
Since surveillance 1.8-0, hhh4()
results are of class "hhh4"
instead of "ah4"
(renamed). Legacy methods for the old class name "ah4"
have been removed.
The internal model preparation in twinstim()
is more efficient (the distance matrix of the events is only computed if event sources actually need to be updated).
stsplot_spacetime()
now recognizes its opts.col
argument.
Conversion from "ts"
to "sts"
using as(ts, "sts")
could set a wrong start time. For instance, as(ts(1:10, start=c(1959,2), frequency=4), "sts")@start
was c(1959,1)
.
algo.twins()
now also accepts "sts"
input and the automatic legend in the first plot of plot.atwins()
works again.
The experimental profile
-method for "twinstim"
objects did not work if embedded twinstim()
fits issued warnings.
update.epidata()
can now handle a distance matrix D
in the form of a classed "Matrix"
. [suggested by George Wood]
glrnb()
can now handle ret="cases"
for the generalized likelihood ratio detector based on the negative binomial distribution. It’s based on a brute-force search and hence might be slow in some situations.
boda()
and bodaDelay()
now support an alternative method (quantileMethod="MM"
) to compute quantiles based on the posterior distribution. The new method samples parameters from the posterior distribution and then computes the quantile of the mixture distribution using bisectionning, which is faster and yields similar results compared to the original method (quantileMethod="MC"
, still the default).
vignette("hhh4")
, updated the package description as well as some references in the documentation. Also updated (the cache of) the slightly outdated vignette("surveillance")
to account for the corrected version of algo.bayes()
implemented since surveillance 1.10-0.Fixed bug in categoricalCUSUM()
, which ignored alarms generated for the last time point in range
. Furthermore, the exact computation in case of returns of the type "value"
for the binomial are now checked through an attribute.
Fixed bug in the estimateGLRNbHook
function of algo.glrnb
, which ignored potential fixed alpha
values. If alpha
is fixed this is now taken into consideration while fitting the negative binomial function. See revised help files for the details.
Made a hot-fix such that the algo.quality
function now also works for sts
objects and if the state
or alarm
slots consists of TRUE/FALSE instead of 0/1.
intensity.twinstim()
did not work for non-endemic models.
A parallelized epitest()
could fail with a strange error message if some replications were left unassigned. This seems to happen if forking is used (mclapply
) with insufficient memory. Incomplete replications are now ignored with a warning.
Calibration tests for count data (Wei and Held, 2014, Test) are now implemented and available as calibrationTest()
. In addition to a default method taking pure counts and predictive means and dispersion parameters, there are convenient methods for "hhh4"
and "oneStepAhead"
objects.
Shared overdispersion across units in negative binomial hhh4()
time series models (by specifying a factor variable as the family
argument).
scores()
and pit()
are now generic and have convenient methods for "oneStepAhead"
predictions and "hhh4"
fits.
The initial values used for model updates during the oneStepAhead()
procedure can now be specified directly through the which.start
argument (as an alternative to the previous options "current"
and "final"
).
plotHHH4_fitted()
(and plotHHH4_fitted1()
) gained an option decompose
to plot the contributions from each single unit (and the endemic part) instead of the default endemic + AR + neighbours decomposition. Furthermore, a formatted time axis similar to stsplot_time1()
can now be enabled via the new argument xaxis
.
The new plot
type
"maps"
for "hhh4"
fits shows maps of the fitted mean components averaged over time.
New plot
-method for simulations from "hhh4"
models (using simulate.hhh4(..., simplify = TRUE)
, which now has a dedicated class: "hhh4sims"
) to show the final size distribution or the simulated time series (possibly stratified by groups of units). There is also a new scores
-method to compute proper scoring rules based on such simulations.
The argument idx2Exp
of coef.hhh4()
may now be conveniently set to TRUE
to exp-transform all coefficients.
Added a coeflist()
-method for "hhh4"
fits.
The generator function sts()
can now be used to initialize objects of class "sts"
(instead of writing new("sts", ...)
).
Additional arguments of layout.scalebar()
now allow to change the style of the labels.
A pre-computed distance matrix D
can now be used as input for the as.epidata()
converter – offering an alternative to the default Euclidean distance based on the individuals coordinates. (Request of George Wood to support twinSIR
models on networks.)
The first argument of scores()
is now called x
instead of object
(for consistency with calibrationTest()
).
The result of oneStepAhead()
now has the dedicated class attribute "oneStepAhead"
(previously was just a list).
Changed interpretation of the col
argument of plotHHH4_fitted()
and plotHHH4_fitted1()
(moved color of “observed” to separate argument pt.col
and reversed remaining colors). The old col
specification as a vector of length 4 still works (catched internally) but is undocumented.
The epoch
slot of class "sts"
is now initialized to 1:nrow(observed)
by default and thus no longer needs to be explicitly set when creating a new("sts", ...)
for this standard case.
Initialization of new("sts", ...)
now supports the argument frequency
(for consistency with ts()
). Note that freq
still works (via partial argument matching) and that the corresponding "sts"
slot is still called freq
.
If missing(legend.opts)
in stsplot_time1()
, the default legend will only be produced if the "sts"
object contains information on outbreaks, alarms, or upperbounds.
The default summary()
of a "twinstim"
fit is more concise since it no longer includes the number of log-likelihood and score function evaluations and the elapsed time during model fitting. Set the new runtime
argument of summary.twinstim()
to TRUE
to add this information to the summary as before.
The animate
-method for "sts"
objects gained an argument draw
(to disable the default instantaneous plotting) and now invisibly returns the sequential plot objects (of class "gtable"
or "trellis"
) in a list for post-processing.
The flexible time axis configurations for "sts"
plots introduced in version 1.8-0 now also work for classical "sts"
objects with integer epochs and standard frequencies (try plot(..., epochsAsDate = TRUE)
).
stsplot_time()
initiates par
settings only if the par.list
argument is a list.
The new all.equal()
method for class "hhh4"
compares two fits ignoring their "runtime"
and "call"
elements (at least).
Fixed a bug in algo.bayes
, where an alarm was already sounded if the current observation was equal to the quantile of the predictive posterior. This was changed in order to get alarm_t = I(obs_t > quantile_t) which is consistent with the use in boda
and bodaDelay
.
Fixed bug in algo.outbreakP
causing a halt in the computations of value="cases"
when calc.outbreakP.statistic
returned NaN
. Now, a NaN
is returned.
wrap.algo
argument control.hook
used control
argument defined outside it’s scope (and not the one provided to the function). It is now added as additional 2nd argument to the control.hook
function.
stsplot_time()
did not account for the optional units
argument for multivariate "sts"
objects when choosing a suitable value for par("mfrow")
.
hhh4()
could have used a function dpois()
or dnbinom()
from the global environment instead of the respective function from package stats.
The default time variable t
created as part of the data
argument in hhh4()
was incompatible with "sts"
objects having epochAsDate=TRUE
.
A consistency check in as.epidata.default()
failed for SI-type data (and, more generally, for all data which ended with an I-event in the last time block). [spotted by George Wood]
This is a quick patch release to make the test suite run smoothly on CRAN’s Windows and Solaris Sparc systems.
The new hhh4()
option to scale neighbourhood weights did not work for parametric weights with more than one parameter if normalize=FALSE
.
New functions and data for Bayesian outbreak detection in the presence of reporting delays (Salmon et al., 2015): bodaDelay()
, sts_observation()
, and sts_creation()
.
New functions implementing tests for space-time interaction:
knox()
supports both the Poisson approximation and a Monte Carlo permutation approach to determine the p-value,
stKtest()
wraps space-time K-function methods from package splancs for use with "epidataCS"
,
and epitest()
for twinstim
models (makes use of the new auxiliary function simpleR0()
).
New function plapply()
: a parallel and verbose version of lapply()
wrapping around both mclapply()
and parLapply()
of package parallel.
New converter as.xts.sts()
to transform "sts"
objects to the quasi standard "xts"
class, e.g., to make use of package dygraphs for interactive time series plots.
New options for scaling and normalization of neighbourhood weights in hhh4()
models.
New auxiliary function layout.scalebar()
for use as part of sp.layout
in spplot()
or in the traditional graphics system.
"epidataCS"
New argument by
for plot.epidataCS()
, which defines a stratifying variable for the events (default is the event type as before). It can also be set to NULL
to make the plot not distinguish between event types.
The spatial plot of "epidataCS"
gained the arguments tiles
, pop
and sp.layout
, and can now produce an spplot()
with the tile-specific population levels behind the point pattern.
New function permute.epidataCS()
to randomly permute time points or locations of the events (holding other marks fixed).
twinstim()
New S3-generic coeflist()
to list model coefficients by component. It currently has a default method and one for "twinstim"
and "simEpidataCS"
.
New argument newcoef
for simulate.twinstim()
to customize the model parameters used for the simulation.
New argument epilink
for twinstim()
, offering experimental support for an identity link for the epidemic predictor. The default remains epilink = "log"
.
Simulation from "twinstim"
models and generation of "epidataCS"
is slightly faster now (faster spatstat functions are used to determine the distance of events to the border).
New option scaled = "standardized"
in iafplot()
to plot f(x) / f(0) or g(t) / g(0), respectively.
Initial data processing in twinstim()
is faster since event sources are only re-determined if there is effective need for an update (due to subsetting or a change of qmatrix
).
formatPval()
disables scientific
notation by default.
The "time"
plot for "epidataCS"
uses the temporal grid points as the default histogram breaks
.
The special fe()
function which sets up fixed effects in hhh4()
models gained an argument unitSpecific
as a convenient shortcut for which = rep(TRUE, nUnits)
.
The convenient plot
option of permutationTest()
uses MASS::truehist()
instead of hist()
and accepts graphical parameters to customize the histogram.
The bodaFit
function did not draw samples from the joint posterior. Instead draws were from the respective posterior marginals. A new argument samplingMethod
is now introduced defaulting to the proper ‘joint’. For backwards compatibility use the value ‘marginal’.
The functions as.epidataCS()
and simEpidataCS()
could throw inappropriate warnings when checking polygon areas (only if W
or tiles
, respectively, contained holes).
Non-convergent endemic-only twinstim
models produced an error. [spotted by Bing Zhang]
The "owin"
-method of intersectPolyCircle
could have returned a rectangle-type "owin"
instead of a polygon.
An error occurred in twinstim()
if finetune=TRUE
or choosing optim()
instead of the default nlminb()
optimizer without supplying a control
list in optim.args
.
The "time"
plot for "epidataCS"
did not necessarily use the same histogram breaks
for all strata.
Specifying a step function of interaction via a numeric vector of knots did not work in twinstim()
.
plot.hhh4()
did not support an unnamed type
argument such as plot(x, "season")
.
simEpidataCS()
did not work if t0
was in the last block of stgrid
(thus it did not work for single-cell grids), and mislabeled the start
column copied to events
if there were no covariates in stgrid
.
Evaluating intensity.twinstim()$hFUN()
at time points before t0
was an error. The function now returns NA_real_
as for time points beyond T
.
Truncated, normalized power-law weights for hhh4()
models, i.e., W_powerlaw(maxlag = M, normalize = TRUE)
with M < max(neighbourhood(stsObj))
, had wrong derivatives and thus failed to converge.
update.hhh4(..., use.estimates = TRUE)
did not use the estimated weight function parameters as initial values for the new fit. It does so now iff the weight function ne$weights
is left unchanged.
Accommodate a new note given by R-devel checks, and set the new INLA additional repository in the ‘DESCRIPTION’ file.
Made linelist2sts()
work for quarters by adding extra "%q"
formatting in formatDate()
.
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).
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.
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.
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 $observed
).
The optional "twinstim"
result components fisherinfo
, tau
, and functions
are always included. They are set to NULL
if they are not applicable instead of missing completely (as before), such that all "twinstim"
objects have the same list structure.
invisibly returns a matrix containing the plotted values of the (scaled) interaction function (and the confidence interval as an attribute). Previously, nothing (NULL
) was returned.
detects a type-specific interaction function and by default uses types=1
if it is not type-specific.
has better default axis ranges.
adapts to the new step function kernels (with new arguments verticals
and do.points
).
supports logarithmic axes (via new log
argument passed on to plot.default
).
optionally respects eps.s
and eps.t
, respectively (by the new argument truncated
).
now uses scaled=TRUE
by default.
The argument colTypes
of plot.epidataCS(,aggregate="space")
is deprecated (use points.args$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.
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).
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.
Fixed sign of observed Fisher information matrix in twinstim
.
Simulation from the Lomax kernel is now correct (via polar coordinates).
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.
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()
.
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"
"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, μ̂^{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.
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