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Last updated on 2026-06-09 23:53:33 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.25.0 | 34.56 | 437.06 | 471.62 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.25.0 | 25.32 | 330.51 | 355.83 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.25.0 | 75.00 | 782.16 | 857.16 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.25.0 | 77.00 | 757.42 | 834.42 | OK | |
| r-devel-windows-x86_64 | 1.25.0 | 49.00 | 329.00 | 378.00 | ERROR | --no-vignettes |
| r-patched-linux-x86_64 | 1.25.0 | 44.58 | 441.84 | 486.42 | OK | |
| r-release-linux-x86_64 | 1.25.0 | 37.45 | 443.55 | 481.00 | OK | |
| r-release-macos-arm64 | 1.25.0 | 10.00 | 114.00 | 124.00 | OK | |
| r-release-macos-x86_64 | 1.25.0 | 29.00 | 477.00 | 506.00 | OK | |
| r-release-windows-x86_64 | 1.25.0 | 46.00 | 428.00 | 474.00 | OK | --no-vignettes |
| r-oldrel-macos-arm64 | 1.25.0 | OK | ||||
| r-oldrel-macos-x86_64 | 1.25.0 | 27.00 | 470.00 | 497.00 | OK | |
| r-oldrel-windows-x86_64 | 1.25.0 | 62.00 | 430.00 | 492.00 | OK | --no-vignettes |
Version: 1.25.0
Check: examples
Result: ERROR
Running examples in ‘surveillance-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: hhh4_simulate
> ### Title: Simulate '"hhh4"' Count Time Series
> ### Aliases: simulate.hhh4
> ### Keywords: datagen
>
> ### ** Examples
>
> data(influMen)
> # convert to sts class and extract meningococcal disease time series
> meningo <- disProg2sts(influMen)[,2]
>
> # fit model
> fit <- hhh4(meningo, control = list(
+ ar = list(f = ~ 1),
+ end = list(f = addSeason2formula(~1, period = 52)),
+ family = "NegBin1"))
> plot(fit)
>
> # simulate from model (generates an "sts" object)
> simData <- simulate(fit, seed=1234)
>
> # plot simulated data
> plot(simData, main = "simulated data", xaxis.labelFormat=NULL)
>
> # use simplify=TRUE to return an array of simulated counts
> simCounts <- simulate(fit, seed=1234, simplify=TRUE)
> dim(simCounts) # nTime x nUnit x nsim
[1] 312 1 1
> ## Don't show:
> stopifnot(observed(simData) == c(simCounts))
> ## End(Don't show)
> # plot the first year of simulated counts (+ initial + observed)
> plot(simCounts[1:52,,], type = "time", xaxis.labelFormat = NULL)
> # see help(plot.hhh4sims) for other plots, mainly useful for nsim > 1
>
> # simulate from a Poisson instead of a NegBin model
> # keeping all other parameters fixed at their original estimates
> coefs <- replace(coef(fit), "overdisp", 0)
> simData2 <- simulate(fit, seed=123, coefs = coefs)
> plot(simData2, main = "simulated data: Poisson model", xaxis.labelFormat = NULL)
>
> # simulate from a model with higher autoregressive parameter
> coefs <- replace(coef(fit), "ar.1", log(0.9))
> simData3 <- simulate(fit, seed=321, coefs = coefs)
> plot(simData3, main = "simulated data: lambda = 0.5", xaxis.labelFormat = NULL)
>
>
> ## more sophisticated: simulate beyond initially observed time range
>
> # extend data range by one year (non-observed domain), filling with NA values
> nextend <- 52
> timeslots <- c("observed", "state", "alarm", "upperbound", "populationFrac")
> addrows <- function (mat, n) mat[c(seq_len(nrow(mat)), rep(NA, n)),,drop=FALSE]
> extended <- Map(function (x) addrows(slot(meningo, x), n = nextend), x = timeslots)
> # create new sts object with extended matrices
> meningo2 <- do.call("sts", c(list(start = meningo@start, frequency = meningo@freq,
+ map = meningo@map), extended))
>
> # fit to the observed time range only, via the 'subset' argument
> fit2 <- hhh4(meningo2, control = list(
+ ar = list(f = ~ 1),
+ end = list(f = addSeason2formula(~1, period = 52)),
+ family = "NegBin1",
+ subset = 2:(nrow(meningo2) - nextend)))
> # the result is the same as before
> stopifnot(all.equal(fit, fit2, ignore = c("stsObj", "control")))
> ## Don't show:
> # one-week-ahead prediction only "works" for the first non-observed time point
> # because the autoregressive component relies on non-missing past counts
> oneStepAhead(fit2, tp = rep(nrow(meningo2)-nextend, 2), type = "final", verbose = FALSE)
$pred
meningococcus
313 11.45203
$observed
meningococcus
313 NA
$psi
-log(overdisp)
312 3.012411
$allConverged
[1] TRUE
attr(,"class")
[1] "oneStepAhead"
> # however, methods won't work as observed is NA
> ## End(Don't show)
> # long-term probabilistic forecast via simulation for non-observed time points
> meningoSim <- simulate(fit2, nsim = 100, seed = 1,
+ subset = seq(nrow(meningo)+1, nrow(meningo2)),
+ y.start = tail(observed(meningo), 1))
> apply(meningoSim, 1:2, function (ysim) quantile(ysim, c(0.1, 0.5, 0.9)))
Error in x@observed[i, j, drop = FALSE] : subscript out of bounds
Calls: apply ... subset_hhh4sims_attributes -> suppressMessages -> withCallingHandlers -> [ -> [
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
epidataCS_plot 4.157 0.056 5.702
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.25.0
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘glrnb.Rnw’ using Sweave
Loading required package: sp
Loading required package: xtable
This is surveillance 1.25.0; see ‘package?surveillance’ or
https://surveillance.R-Forge.R-project.org/ for an overview.
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting glm.nb model with alpha=0.225966923076071
glrnb: Fitting glm.nb model with alpha=0.225966923076071
glrnb: Fitting glm.nb model with alpha=0.225966923076071
glrnb: Fitting glm.nb model with alpha=0.225966923076071
glrnb: Fitting glm.nb model with alpha=0.225966923076071
glrnb: Fitting glm.nb model with alpha=0.225966923076071
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
glrnb: Fitting Poisson model because alpha == 0
--- finished re-building ‘glrnb.Rnw’
--- re-building ‘hhh4.Rnw’ using Sweave
Loading required package: sp
Loading required package: xtable
This is surveillance 1.25.0; see ‘package?surveillance’ or
https://surveillance.R-Forge.R-project.org/ for an overview.
Doing computations: FALSE
--- finished re-building ‘hhh4.Rnw’
--- re-building ‘surveillance.Rnw’ using Sweave
Loading required package: sp
Loading required package: xtable
This is surveillance 1.25.0; see ‘package?surveillance’ or
https://surveillance.R-Forge.R-project.org/ for an overview.
Doing computations: FALSE
--- finished re-building ‘surveillance.Rnw’
--- re-building ‘hhh4_spacetime.Rnw’ using knitr
Quitting from hhh4_spacetime.Rnw:1081-1083 [measlesSim_plot_time]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `x@observed[i, j, drop = FALSE]`:
! subscript out of bounds
---
Backtrace:
x
1. +-base::plot(measlesSim, "fan", means.args = list(), key.args = list())
2. +-base::plot(measlesSim, "fan", means.args = list(), key.args = list())
3. \-surveillance:::plot.hhh4sims(...)
4. \-surveillance:::plot.hhh4simslist(x, ...)
5. +-base::do.call(FUN, list(quote(x), ...))
6. \-surveillance::plotHHH4sims_fan(x, means.args = `<list>`, key.args = `<list>`)
7. \-surveillance:::aggregate.hhh4sims(...)
8. \-base::apply(X = x, MARGIN = c(1L, 3L), FUN = sum)
9. +-newX[, i]
10. \-surveillance:::`[.hhh4sims`(newX, , i)
11. \-surveillance:::subset_hhh4sims_attributes(xx, i, j)
12. +-base::suppressMessages(attr(x, "stsObserved")[, j])
13. | \-base::withCallingHandlers(...)
14. +-attr(x, "stsObserved")[, j]
15. \-attr(x, "stsObserved")[, j]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'hhh4_spacetime.Rnw' failed with diagnostics:
subscript out of bounds
--- failed re-building ‘hhh4_spacetime.Rnw’
--- re-building ‘monitoringCounts.Rnw’ using knitr
--- finished re-building ‘monitoringCounts.Rnw’
--- re-building ‘twinSIR.Rnw’ using knitr
--- finished re-building ‘twinSIR.Rnw’
--- re-building ‘twinstim.Rnw’ using knitr
--- finished re-building ‘twinstim.Rnw’
SUMMARY: processing the following file failed:
‘hhh4_spacetime.Rnw’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.25.0
Flags: --no-vignettes
Check: examples
Result: ERROR
Running examples in 'surveillance-Ex.R' failed
The error most likely occurred in:
> ### Name: hhh4_simulate
> ### Title: Simulate '"hhh4"' Count Time Series
> ### Aliases: simulate.hhh4
> ### Keywords: datagen
>
> ### ** Examples
>
> data(influMen)
> # convert to sts class and extract meningococcal disease time series
> meningo <- disProg2sts(influMen)[,2]
>
> # fit model
> fit <- hhh4(meningo, control = list(
+ ar = list(f = ~ 1),
+ end = list(f = addSeason2formula(~1, period = 52)),
+ family = "NegBin1"))
> plot(fit)
>
> # simulate from model (generates an "sts" object)
> simData <- simulate(fit, seed=1234)
>
> # plot simulated data
> plot(simData, main = "simulated data", xaxis.labelFormat=NULL)
>
> # use simplify=TRUE to return an array of simulated counts
> simCounts <- simulate(fit, seed=1234, simplify=TRUE)
> dim(simCounts) # nTime x nUnit x nsim
[1] 312 1 1
> ## Don't show:
> stopifnot(observed(simData) == c(simCounts))
> ## End(Don't show)
> # plot the first year of simulated counts (+ initial + observed)
> plot(simCounts[1:52,,], type = "time", xaxis.labelFormat = NULL)
> # see help(plot.hhh4sims) for other plots, mainly useful for nsim > 1
>
> # simulate from a Poisson instead of a NegBin model
> # keeping all other parameters fixed at their original estimates
> coefs <- replace(coef(fit), "overdisp", 0)
> simData2 <- simulate(fit, seed=123, coefs = coefs)
> plot(simData2, main = "simulated data: Poisson model", xaxis.labelFormat = NULL)
>
> # simulate from a model with higher autoregressive parameter
> coefs <- replace(coef(fit), "ar.1", log(0.9))
> simData3 <- simulate(fit, seed=321, coefs = coefs)
> plot(simData3, main = "simulated data: lambda = 0.5", xaxis.labelFormat = NULL)
>
>
> ## more sophisticated: simulate beyond initially observed time range
>
> # extend data range by one year (non-observed domain), filling with NA values
> nextend <- 52
> timeslots <- c("observed", "state", "alarm", "upperbound", "populationFrac")
> addrows <- function (mat, n) mat[c(seq_len(nrow(mat)), rep(NA, n)),,drop=FALSE]
> extended <- Map(function (x) addrows(slot(meningo, x), n = nextend), x = timeslots)
> # create new sts object with extended matrices
> meningo2 <- do.call("sts", c(list(start = meningo@start, frequency = meningo@freq,
+ map = meningo@map), extended))
>
> # fit to the observed time range only, via the 'subset' argument
> fit2 <- hhh4(meningo2, control = list(
+ ar = list(f = ~ 1),
+ end = list(f = addSeason2formula(~1, period = 52)),
+ family = "NegBin1",
+ subset = 2:(nrow(meningo2) - nextend)))
> # the result is the same as before
> stopifnot(all.equal(fit, fit2, ignore = c("stsObj", "control")))
> ## Don't show:
> # one-week-ahead prediction only "works" for the first non-observed time point
> # because the autoregressive component relies on non-missing past counts
> oneStepAhead(fit2, tp = rep(nrow(meningo2)-nextend, 2), type = "final", verbose = FALSE)
$pred
meningococcus
313 11.45203
$observed
meningococcus
313 NA
$psi
-log(overdisp)
312 3.012411
$allConverged
[1] TRUE
attr(,"class")
[1] "oneStepAhead"
> # however, methods won't work as observed is NA
> ## End(Don't show)
> # long-term probabilistic forecast via simulation for non-observed time points
> meningoSim <- simulate(fit2, nsim = 100, seed = 1,
+ subset = seq(nrow(meningo)+1, nrow(meningo2)),
+ y.start = tail(observed(meningo), 1))
> apply(meningoSim, 1:2, function (ysim) quantile(ysim, c(0.1, 0.5, 0.9)))
Error in x@observed[i, j, drop = FALSE] : subscript out of bounds
Calls: apply ... subset_hhh4sims_attributes -> suppressMessages -> withCallingHandlers -> [ -> [
Execution halted
Flavor: r-devel-windows-x86_64
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