Model Correction Inconsistency (MCI)
calc_mci.RdMCI provides a score to quantify how invasive the correction method transformed the multivariate temporal rank structure of the model after correcting the model bias. Therefore, this model compares non-exceedance probabilities of an event t before and after bias correction. A non-invasive method preserves the model-specific rank structure.
Arguments
- model
data.frame
Simulation climate data from the climate model.- model_correction
data.frame
Simulation climate datamodelfrom the climate model corrected by an arbitrary bias correction method.- time_p
date
An optional vector with a time point for each observation/row in the (corrected) model data. Default is NA which indicates that no temporal information are given.- p
numeric(1)
The power of the global MCI. Default is 1.
Value
data.table
Contains the points of time if supplied in time_p and the respective
local MCI. Note that the last time step is truncated while derivation. The
attribute global contains the global MCI, which is the mean over all MCI
scores. The estimated multivariate distribution of model and correction are
placed in attributes in of the returned object.
Examples
#' \dontrun{
#' # Start with a simple example:
#' # There is one timeline that should be corrected and projection and
#' # calibaration period are both in 2010.
#' set.seed(1234L)
#' library(VBC)
#' library(data.table)
#' library(patchwork)
#' data("climate")
#' margins_controls <- list(xmin = c(NaN, 0, NaN, 0, 0),
#' type = c("c", "zi", "c", "zi", "c"))
#'
#' climate_2010 = lapply(climate, function(data) data[year(time) == 2010, ])
#'
#' mp_vbc = vbc(climate_2010$mp[, -"time"], climate_2010$mp[, -"time"],
#' climate_2010$rp[, -"time"], margins_controls = margins_controls,
#' family_set = "tll", trunc_lvl = Inf)
#'
#' plot_tails(climate_2010$mp, "pr", scale_d = 0.1, mult = 4, xmin = 0)
#' plot_tails(climate_2010$rp, "pr", scale_d = 1, mult = 4, xmin = 0)
#' plot_tails(round(mp_vbc, 3), "pr", scale_d = 1, mult = 3, xmin = 0)
#' calc_wasserstein(climate_2010$mp[, "pr"], climate_2010$rp[, "pr"])
#' calc_wasserstein(climate_2010$rp[, "pr"], mp_vbc[, "pr"])
#'
#' calc_wasserstein(climate_2010$rp[, -"time"], climate_2010$mp[, -"time"])
#' calc_wasserstein(climate_2010$rp[, -"time"], mp_vbc)
#'
#' # An example with temporal subsetting using the method of fragments
#'
#' temp_subs <- list(DJF = list(hours = seq(0, 21, 3), month = c(12, 1, 2)),
#' MAM = list(hours = seq(0, 21, 3), month = 3:5),
#' JJA = list(hours = seq(0, 21, 3), month = 6:8),
#' SON = list(hours = seq(0, 21, 3), month = 9:11))
#'
#' mp_vbc = vbc_tsub(climate$mp, climate$mc, climate$rc, t_subs = temp_subs,
#' margins_controls = margins_controls, family_set = "tll",
#' trunc_lvl = Inf)
#' class(mp_vbc)
#' attr(mp_vbc, "mvd")
#'
#' measure = lapply(temp_subs, function(sub) {
#' idx = which(hour(mp_vbc$time) %in% sub$hour &
#' month(mp_vbc$time) %in% sub$month)
#' idxrp = which(hour(climate$rp$time) %in% sub$hour &
#' month(climate$rp$time) %in% sub$month)
#' mci = calc_mci(climate$mp[idx, -"time"], mp_vbc[idx, -"time"],
#' time = climate$mp$time[idx])
#' mci = attr(mci, "global_mci")
#'
#' uncorrected = calc_wasserstein(climate$rp[idxrp, -"time"],
#' climate$mp[idx, -"time"])
#' corrected = calc_wasserstein(climate$rp[idxrp, -"time"],
#' mp_vbc[idx, -"time"])
#' data.table(
#' ModelCorrectionInconsistancy = mci,
#' Wasserstein2_uncorrected = uncorrected[2],
#' Wasserstein2_corrected = corrected[2],
#' Improvement_Wasserstein2 = uncorrected[2] - corrected[2],
#' month = paste(sub$month, collapse = "-")
#' )
#' })
#' rbindlist(measure)
#' }