meteo (version 2.0-2)

data.prepare: Prepare data

Description

Function for data preparation for RFSI and STRK functions. It transforms data to a data.frame.

Usage

data.prepare(data,
             data.staid.x.y.z=NULL,
             obs.col=NULL,
             s.crs=NA
)

Value

A list with the following elements:

data.df

A data.frame obtained from data.

data.staid.x.y.z

Positions of the station ID (staid), longitude (x), latitude (y) and 3rd component (z) columns in data.frame object (e.g. c(1,2,3,4)).

s.crs

Source CRS of data.

obs.col

Column number showing position of the observation column in the data.

Arguments

data

sf-class, sftime-class, SpatVector-class, SpatRaster-class or data.frame; Contains target variable (observations) and covariates. If data.frame object, it should have next columns: station ID (staid), longitude (x), latitude (y), 3rd component - time, depth, ... (z) of the observation, and observation value (obs).

data.staid.x.y.z

numeric or character vector; Positions or names of the station ID (staid), longitude (x), latitude (y) and 3rd component (z) columns in data.frame object (e.g. c(1,2,3,4)). If data is sf-class, sftime-class, or SpatVector-class object, data.staid.x.y.z is used to point staid and z position. Set z position to NA (e.g. c(1,2,3,NA)) or ommit it (e.g. c(1,2,3)) for spatial interpolation. Default is NULL.

obs.col

numeric or character; Column name or number showing position of the observation column in the data. Default is 1.

s.crs

st_crs or crs; Source CRS of data. If data contains crs, s.crs will not be used. Default is NA.

Author

Aleksandar Sekulic

References

Sekulić, A., Kilibarda, M., Heuvelink, G. B., Nikolić, M. & Bajat, B. Random Forest Spatial Interpolation. Remote. Sens. 12, 1687, https://doi.org/10.3390/rs12101687 (2020).

See Also

near.obs rfsi tune.rfsi cv.rfsi

Examples

library(sf)
library(meteo)
library(sp)

# prepare data
demo(meuse, echo=FALSE)
meuse <- meuse[complete.cases(meuse@data),]
data = st_as_sf(meuse, coords = c("x", "y"), crs = 28992, agr = "constant")
data.df <- data.prepare(data,
                        obs.col="zinc")
str(data.df)