Combining reliability diagram objects

Combine two or more 'reliabilitydiag' objects that are based on the same observations. Other objects are coerced by as.reliabilitydiag before combination.

# S3 method for reliabilitydiag
c(
  ...,
  tol = sqrt(.Machine$double.eps),
  xtype = NULL,
  xvalues = NULL,
  region.level = 0.9,
  region.method = NULL,
  region.position = "diagonal",
  n.boot = 100
)

Arguments

...

objects to be concatenated.

tol

accuracy when comparing y in 'reliabilitydiag' objects.

xtype

a string specifying whether the prediction values should be treated as "continuous" or "discrete".

xvalues

a numeric vector of possible prediction values; values in x are rounded to the nearest value in xvalues and xtype is set to "discrete".

region.level

a value in (0, 1) specifying the level at which consistency or confidence regions are calculated.

region.method

a string specifying whether "resampling", "continuous_asymptotics", or "discrete_asymptotics" are used to calculate consistency/confidence regions.

region.position

a string specifying whether consistency regions around the "diagonal" or confidence regions around the "estimate" are calculated.

n.boot

the number of bootstrap samples when region.method == "resampling".

Value

an object inheriting from the class 'reliabilitydiag'.

See also

as.reliabilitydiag, [.reliabilitydiag.

Examples

data("precip_Niamey_2016", package = "reliabilitydiag")

X <- precip_Niamey_2016[c("EMOS", "ENS")]
Y <- precip_Niamey_2016$obs
r0 <- reliabilitydiag0(Y)
r1 <- c(r0, X, EPC = precip_Niamey_2016$EPC, region.level = NA)
r1

#> 'brier' score decomposition (see also ?summary.reliabilitydiag)
#> # A tibble: 3 × 5
#>   forecast mean_score miscalibration discrimination uncertainty
#>   <chr>         <dbl>          <dbl>          <dbl>       <dbl>
#> 1 EMOS          0.232         0.0183         0.0305       0.244
#> 2 ENS           0.266         0.0661         0.0441       0.244
#> 3 EPC           0.234         0.0223         0.0323       0.244
c(r1, reliabilitydiag(Logistic = precip_Niamey_2016$Logistic, y = Y))

#> 'brier' score decomposition (see also ?summary.reliabilitydiag)
#> # A tibble: 4 × 5
#>   forecast mean_score miscalibration discrimination uncertainty
#>   <chr>         <dbl>          <dbl>          <dbl>       <dbl>
#> 1 EMOS          0.232         0.0183         0.0305       0.244
#> 2 ENS           0.266         0.0661         0.0441       0.244
#> 3 EPC           0.234         0.0223         0.0323       0.244
#> 4 Logistic      0.206         0.0171         0.0555       0.244