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Automatic Vignette update (#900)
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Co-authored-by: github-merge-queue[bot] <118344674+github-merge-queue[bot]@users.noreply.github.com>
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148 changes: 71 additions & 77 deletions vignettes/estimate_infections_options.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -198,10 +198,10 @@ summary(def)
# elapsed time (in seconds)
get_elapsed_time(def$fit)
#> warmup sample
#> chain:1 23.301 18.698
#> chain:2 29.287 28.181
#> chain:3 37.962 17.803
#> chain:4 37.761 14.090
#> chain:1 23.529 18.991
#> chain:2 29.477 28.414
#> chain:3 37.718 18.235
#> chain:4 38.292 14.125
# summary plot
plot(def)
```
Expand Down Expand Up @@ -232,10 +232,10 @@ summary(agp)
# elapsed time (in seconds)
get_elapsed_time(agp$fit)
#> warmup sample
#> chain:1 27.748 19.571
#> chain:2 23.701 18.866
#> chain:3 22.463 28.347
#> chain:4 26.436 19.798
#> chain:1 28.106 18.768
#> chain:2 24.271 19.359
#> chain:3 22.602 30.107
#> chain:4 26.954 20.210
# summary plot
plot(agp)
```
Expand Down Expand Up @@ -270,10 +270,10 @@ summary(dep)
# elapsed time (in seconds)
get_elapsed_time(dep$fit)
#> warmup sample
#> chain:1 42.083 23.377
#> chain:2 31.143 20.577
#> chain:3 39.918 19.875
#> chain:4 38.332 20.637
#> chain:1 42.625 23.562
#> chain:2 31.544 20.825
#> chain:3 40.299 20.096
#> chain:4 38.885 20.701
# summary plot
plot(dep)
```
Expand All @@ -288,30 +288,16 @@ Here, instead of doing so we assume that we know about truncation with mean of 1

``` r
trunc_dist <- LogNormal(
mean = Normal(0.5, 0.1),
sd = Normal(0.5, 0.1),
mean = 0.5,
sd = 0.5,
max = 3
)
#> Warning: ! Uncertain lognormal distribution specified in terms of parameters that are
#> not the "natural" parameters of the distribution meanlog and sdlog.
#> ℹ Converting using a crude and very approximate method that is likely to
#> produce biased results.
#> ℹ If possible it is preferable to specify the distribution directly in terms of
#> the natural parameters.
trunc_dist
#> - lognormal distribution (max: 3):
#> meanlog:
#> - normal distribution:
#> mean:
#> -1
#> sd:
#> 0.14
#> -1
#> sdlog:
#> - normal distribution:
#> mean:
#> 0.83
#> sd:
#> 0.13
#> 0.83
```

We can then use this in the `esimtate_infections()` function using the `truncation` option.
Expand All @@ -324,14 +310,22 @@ trunc <- estimate_infections(reported_cases,
truncation = trunc_opts(trunc_dist),
rt = rt_opts(prior = rt_prior)
)
#> Error in vapply(delays[parametric], attr, "weight_prior", FUN.VALUE = logical(1)): values must be length 1,
#> but FUN(X[[3]]) result is length 0
# summarise results
summary(trunc)
#> Error in object[[i]]: object of type 'builtin' is not subsettable
#> measure estimate
#> <char> <char>
#> 1: New infections per day 4273 (2401 -- 8111)
#> 2: Expected change in daily reports Likely increasing
#> 3: Effective reproduction no. 1.1 (0.87 -- 1.4)
#> 4: Rate of growth 0.032 (-0.043 -- 0.13)
#> 5: Doubling/halving time (days) 21 (5.5 -- -16)
# elapsed time (in seconds)
get_elapsed_time(trunc$fit)
#> Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'get_elapsed_time': object of type 'builtin' is not subsettable
#> warmup sample
#> chain:1 32.460 19.479
#> chain:2 36.538 18.114
#> chain:3 24.322 31.656
#> chain:4 30.328 20.364
# summary plot
plot(trunc)
```
Expand All @@ -356,18 +350,18 @@ project_rt <- estimate_infections(reported_cases,
summary(project_rt)
#> measure estimate
#> <char> <char>
#> 1: New infections per day 2265 (1291 -- 4223)
#> 1: New infections per day 2277 (1300 -- 4159)
#> 2: Expected change in daily reports Likely decreasing
#> 3: Effective reproduction no. 0.9 (0.71 -- 1.2)
#> 4: Rate of growth -0.026 (-0.099 -- 0.057)
#> 5: Doubling/halving time (days) -27 (12 -- -7)
#> 3: Effective reproduction no. 0.9 (0.71 -- 1.1)
#> 4: Rate of growth -0.028 (-0.098 -- 0.052)
#> 5: Doubling/halving time (days) -25 (13 -- -7)
# elapsed time (in seconds)
get_elapsed_time(project_rt$fit)
#> warmup sample
#> chain:1 32.436 20.433
#> chain:2 30.657 30.856
#> chain:3 38.811 29.771
#> chain:4 35.218 21.061
#> chain:1 41.792 18.973
#> chain:2 34.302 20.610
#> chain:3 38.729 18.627
#> chain:4 33.687 20.569
# summary plot
plot(project_rt)
```
Expand All @@ -389,18 +383,18 @@ fixed <- estimate_infections(reported_cases,
summary(fixed)
#> measure estimate
#> <char> <char>
#> 1: New infections per day 15985 (9499 -- 27668)
#> 1: New infections per day 15719 (9135 -- 27752)
#> 2: Expected change in daily reports Increasing
#> 3: Effective reproduction no. 1.2 (1.1 -- 1.3)
#> 4: Rate of growth 0.048 (0.034 -- 0.063)
#> 5: Doubling/halving time (days) 14 (11 -- 20)
#> 4: Rate of growth 0.048 (0.033 -- 0.062)
#> 5: Doubling/halving time (days) 14 (11 -- 21)
# elapsed time (in seconds)
get_elapsed_time(fixed$fit)
#> warmup sample
#> chain:1 2.584 1.781
#> chain:2 2.518 1.999
#> chain:3 2.782 1.998
#> chain:4 3.113 1.698
#> chain:1 3.135 2.078
#> chain:2 3.071 1.926
#> chain:3 2.708 1.865
#> chain:4 3.069 1.897
# summary plot
plot(fixed)
```
Expand Down Expand Up @@ -435,18 +429,18 @@ bkp <- estimate_infections(bp_cases,
summary(bkp)
#> measure estimate
#> <char> <char>
#> 1: New infections per day 2343 (1927 -- 2880)
#> 1: New infections per day 2363 (1947 -- 2891)
#> 2: Expected change in daily reports Decreasing
#> 3: Effective reproduction no. 0.89 (0.86 -- 0.92)
#> 4: Rate of growth -0.027 (-0.035 -- -0.02)
#> 5: Doubling/halving time (days) -25 (-35 -- -20)
#> 4: Rate of growth -0.027 (-0.034 -- -0.02)
#> 5: Doubling/halving time (days) -26 (-35 -- -20)
# elapsed time (in seconds)
get_elapsed_time(bkp$fit)
#> warmup sample
#> chain:1 4.169 4.144
#> chain:2 4.123 4.556
#> chain:3 4.067 4.346
#> chain:4 3.840 4.380
#> chain:1 4.777 4.458
#> chain:2 4.579 4.862
#> chain:3 4.953 5.284
#> chain:4 4.385 5.380
# summary plot
plot(bkp)
```
Expand All @@ -470,18 +464,18 @@ rw <- estimate_infections(reported_cases,
summary(rw)
#> measure estimate
#> <char> <char>
#> 1: New infections per day 2139 (1069 -- 4280)
#> 1: New infections per day 2079 (1032 -- 4439)
#> 2: Expected change in daily reports Likely decreasing
#> 3: Effective reproduction no. 0.87 (0.63 -- 1.2)
#> 4: Rate of growth -0.036 (-0.11 -- 0.044)
#> 5: Doubling/halving time (days) -19 (16 -- -6.4)
#> 3: Effective reproduction no. 0.86 (0.62 -- 1.2)
#> 4: Rate of growth -0.038 (-0.12 -- 0.047)
#> 5: Doubling/halving time (days) -18 (15 -- -6)
# elapsed time (in seconds)
get_elapsed_time(rw$fit)
#> warmup sample
#> chain:1 9.343 13.485
#> chain:2 8.843 15.550
#> chain:3 8.556 10.431
#> chain:4 9.442 13.621
#> chain:1 9.655 15.971
#> chain:2 9.614 16.314
#> chain:3 9.230 12.979
#> chain:4 10.482 16.692
# summary plot
plot(rw)
```
Expand All @@ -502,18 +496,18 @@ no_delay <- estimate_infections(
summary(no_delay)
#> measure estimate
#> <char> <char>
#> 1: New infections per day 2804 (2403 -- 3257)
#> 1: New infections per day 2801 (2409 -- 3289)
#> 2: Expected change in daily reports Decreasing
#> 3: Effective reproduction no. 0.89 (0.81 -- 0.97)
#> 4: Rate of growth -0.031 (-0.062 -- 0.0012)
#> 5: Doubling/halving time (days) -22 (570 -- -11)
#> 3: Effective reproduction no. 0.89 (0.8 -- 0.98)
#> 4: Rate of growth -0.03 (-0.064 -- 0.00074)
#> 5: Doubling/halving time (days) -23 (940 -- -11)
# elapsed time (in seconds)
get_elapsed_time(no_delay$fit)
#> warmup sample
#> chain:1 33.675 32.734
#> chain:2 43.810 35.023
#> chain:3 47.974 42.682
#> chain:4 48.048 32.326
#> chain:1 40.605 34.244
#> chain:2 44.485 36.481
#> chain:3 41.615 37.638
#> chain:4 38.434 40.516
# summary plot
plot(no_delay)
```
Expand All @@ -540,18 +534,18 @@ non_parametric <- estimate_infections(reported_cases,
summary(non_parametric)
#> measure estimate
#> <char> <char>
#> 1: New infections per day 2730 (2689 -- 2768)
#> 1: New infections per day 2730 (2688 -- 2774)
#> 2: Expected change in daily reports Decreasing
#> 3: Effective reproduction no. 0.92 (0.86 -- 0.96)
#> 4: Rate of growth -0.024 (-0.025 -- -0.022)
#> 5: Doubling/halving time (days) -29 (-31 -- -28)
# elapsed time (in seconds)
get_elapsed_time(non_parametric$fit)
#> warmup sample
#> chain:1 4.114 0.720
#> chain:2 4.542 0.649
#> chain:3 5.133 0.419
#> chain:4 4.258 0.474
#> chain:1 4.475 0.516
#> chain:2 4.663 0.641
#> chain:3 4.827 0.549
#> chain:4 4.169 0.725
# summary plot
plot(non_parametric)
```
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