gapFilling: Estimating new values in original missing values data series...

In rsnotivoli/reddPrec: Reconstruction of Daily Data - Precipitation

Estimating new values in original missing values data series of daily precipitation

Description

This function uses the neighboring observations to estimate new precipitation values in those days and locations where no records exist.

Usage

gapFilling(
  prec,
  sts,
  dates,
  stmethod = NULL,
  thres = NA,
  neibs = 10,
  coords,
  crs,
  coords_as_preds = TRUE,
  window,
  ncpu = 2
)

Arguments

prec	matrix or data.frame containing the original (cleaned) precipitation data. Each column represents one station. The names of columns must coincide with the names of the stations.
sts	matrix or data.frame. A column "ID" (unique ID of stations) is required. The rest of the columns (all of them) will act as predictors of the model.
dates	vector of class "Date" with all days of observations (yyyy-mm-dd).
stmethod	standardization method. 'quant' or 'ratio', see details.
thres	numeric. Maximum radius (in km) where neighboring stations will be searched. NA value uses the whole spatial domain.
neibs	integer. Number of nearest neighbors to use.
coords	vector of two character elements. Names of the fields in "sts" containing longitude and latitude.
crs	character. Coordinates system in EPSG format (e.g.: "EPSG:4326").
coords_as_preds	logical. If TRUE (default), "coords" are also taken as predictors.
window	odd integer. Length of data considered for standardization
ncpu	number of processor cores used to parallel computing.

Details

After the gap filling, "stmethod" allows for an standardization of the predictions based on the observations. It only works for daily data. For other timescales (monthly, annual) use "stmethod=NULL". The "window" parameter is a daily-moving centered window from which data is collected for each year (i.e. a 15-day window on 16th January will take all predictions from 1st to 30th January of all years to standardize them with their corresponding observations. Only standardized prediction of 16th January is returned. Process is repeated for all days).

Examples

## Not run: 
set.seed(123)
prec <- round(matrix(rnorm(30*50, mean = 1.2, sd = 6), 30, 50), 1)
prec[prec<0] <- 0
prec <- apply(prec, 2, FUN = function(x){x[sample(length(x),5)] <- NA; x})
colnames(prec) <- paste0('sts_',1:50)
sts <- data.frame(ID = paste0('sts_',1:50), lon = rnorm(50,0,1), 
                  lat = rnorm(50,40,1), dcoast = rnorm(50,200,50))
filled <- gapFilling(prec, sts, 
                    dates = seq.Date(as.Date('2023-04-01'),
                    as.Date('2023-04-30'),by='day'), 
                    stmethod = "ratio", thres = NA, coords = c('lon','lat'),
                    coords_as_preds = TRUE, crs = 'EPSG:4326', neibs = 10, 
                    window = 11, ncpu = 2)
str(filled)
summary(filled)

## End(Not run)

rsnotivoli/reddPrec documentation built on April 20, 2024, 11:07 a.m.