Create an approximate confidence band for the Rt or incidence estimate. Note that the variance computation is approximate.
Usage
confband(object, lambda, level = 0.95, type = c("Rt", "Yt"), ...)
Arguments
- object
a
poisson_rt
orcv_poisson_rt
object.- lambda
the selected lambda. May be a scalar value, or in the case of
cv_poisson_rt
objects,"lambda.min"
or"lambda.max"
.- level
the desired confidence level(s). These will be sorted if necessary.
- type
the type
Rt
orYt
for confidence intervals of fitted Rt or fitted incident cases- ...
additional arguments for methods. Unused.
Value
A data.frame
containing the estimates Rt
or Yt
at the chosen
lambda
, and confidence limits corresponding to level
Examples
y <- c(1, rpois(100, dnorm(1:100, 50, 15) * 500 + 1))
out <- estimate_rt(y, nsol = 10)
head(confband(out, out$lambda[2]))
#> An `rt_confidence_band` object.
#>
#> * type = Rt
#> * lambda = 110.771
#> * degrees of freedom = 4
#>
#> # A tibble: 6 × 3
#> fit `2.5%` `97.5%`
#> <dbl> <dbl> <dbl>
#> 1 0.783 NaN NaN
#> 2 0.833 0.215 1.45
#> 3 0.884 0 1.92
#> 4 0.934 0 1.98
#> 5 0.984 0.145 1.82
#> 6 1.03 0.204 1.86
head(confband(out, out$lambda[2], level = c(0.95, 0.8, 0.5)))
#> An `rt_confidence_band` object.
#>
#> * type = Rt
#> * lambda = 110.771
#> * degrees of freedom = 4
#>
#> # A tibble: 6 × 7
#> fit `2.5%` `10.0%` `25.0%` `75.0%` `90.0%` `97.5%`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.783 NaN NaN NaN NaN NaN NaN
#> 2 0.833 0.215 0.431 0.622 1.04 1.24 1.45
#> 3 0.884 0 0.213 0.532 1.24 1.55 1.92
#> 4 0.934 0 0.256 0.579 1.29 1.61 1.98
#> 5 0.984 0.145 0.439 0.698 1.27 1.53 1.82
#> 6 1.03 0.204 0.494 0.750 1.32 1.57 1.86
cv <- cv_estimate_rt(y, nfold = 3, nsol = 30)
head(confband(cv, "lambda.min", c(0.5, 0.9)))
#> An `rt_confidence_band` object.
#>
#> * type = Rt
#> * lambda = 163.308
#> * degrees of freedom = 4
#>
#> # A tibble: 6 × 5
#> fit `5%` `25%` `75%` `95%`
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.777 NaN NaN NaN NaN
#> 2 0.830 0.338 0.629 1.03 1.32
#> 3 0.884 0.0402 0.540 1.23 1.73
#> 4 0.938 0.0697 0.584 1.29 1.81
#> 5 0.991 0.294 0.707 1.28 1.69
#> 6 1.04 0.362 0.766 1.32 1.73