rfsi: Random Forest Spatial Interpolation (RFSI) model

Description

Function for creation of Random Forest Spatial Interpolation (RFSI) model (Sekulić et al. 2020). Besides environmental covariates, RFSI uses additional spatial covariates: (1) observations at n nearest locations and (2) distances to them, in order to include spatial context into the random forest.

Usage

rfsi(formula,
     data,
     data.staid.x.y.z = NULL,
     n.obs = 5,
     avg = FALSE,
     increment = 10000,
     range = 50000,
     quadrant = FALSE,
     use.idw = FALSE,
     idw.p = 2,
     s.crs = NA,
     p.crs = NA,
     cpus = detectCores()-1,
     progress = TRUE,
     soil3d = FALSE,
     depth.range = 0.1,
     no.obs = 'increase',
     ...)

Value

RFSI model of class ranger.

Arguments

formula: formula; Formula for specifying target variable and covariates (without nearest observations and distances to them). If z~1, an RFSI model using only nearest obsevrations and distances to them as covariates will be made.
data: sf-class, sftime-class, SpatVector-class or data.frame; Contains target variable (observations) and covariates used for making an RFSI model. If data.frame object, it should have next columns: station ID (staid), longitude (x), latitude (y), 3rd component - time, depth, ... (z) of the observation, observation value (obs) and covariates (cov1, cov2, ...). If covariates are missing, the RFSI model using only nearest obsevrations and distances to them as covariates (formula=z~1) will be made.
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.
n.obs: numeric; Number of nearest observations to be used as covariates in RFSI model (see function near.obs). Note that it cannot be larger than number of obsevrations. Default is 5.
avg: boolean; Averages in circles covariate - will averages in circles with different radiuses be calculated (see function near.obs). Default is FALSE.
increment: numeric; Increment of radiuses for calculation of averages in circles with different radiuses (see function near.obs). Units depends on CRS.
range: numeric; Maximum radius for calculation of averages in circles with different radiuses (see function near.obs). Units depends on CRS.
quadrant: boolean; Nearest observations in quadrants covariate - will nearest observation in quadrants be calculated (see function near.obs). Default is FALSE.
use.idw: boolean; IDW prediction as covariate - will IDW predictions from n.obs nearest observations be calculated (see function near.obs). Default is FALSE.
idw.p: numeric; Exponent parameter for IDW weights (see function near.obs). Default is 2.
s.crs: st_crs or crs; Source CRS of data. If data contains crs, s.crs will be overwritten. Default is NA.
p.crs: st_crs or crs; Projection CRS for data reprojection. If NA, s.crs will be used for distance calculation. Note that observations should be in projection for finding nearest observations based on Eucleadean distances (see function near.obs). Default is NA.
cpus: numeric; Number of processing units. Default is detectCores()-1.
progress: logical; If progress bar is shown. Default is TRUE.
soil3d: logical; If 3D soil modellig is performed and near.obs.soil function is used for finding n nearest observations and distances to them. In this case, z position of the data.staid.x.y.z points to the depth column.
depth.range: numeric; Depth range for location mid depth in which to search for nearest observations (see function near.obs.soil). It's in the mid depth units. Default is 0.1.
no.obs: character; Possible values are increase (default) and exactly. If set to increase, in case if there is no n.obs observations in depth.range for a specific location, the depth.range is increased (multiplied by 2, 3, ...) until the number of observations are larger or equal to n.obs. If set to exactly, the function will raise an error when it come to the first location with no n.obs observations in specified depth.range (see function near.obs.soil).
...: Further arguments passed to ranger, such as quantreg, importance, etc.

Author

Aleksandar Sekulic asekulic@grf.bg.ac.rs

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).

Examples

Run this code

library(ranger)
library(sp)
library(sf)
library(terra)
library(meteo)
# 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 = terra::vect(meuse)
# data.frame
# data <- as.data.frame(meuse)
# data$id = 1:nrow(data)
# data.staid.x.y.z <- c(15,"x","y",NA)
fm.RFSI <- as.formula("zinc ~ dist + soil + ffreq")

# fit the RFSI model
rfsi_model <- rfsi(formula = fm.RFSI,
                   data = data, # meuse.df (use data.staid.x.y.z)
                   # data.staid.x.y.z = data.staid.x.y.z, # only if class(data) == data.frame
                   n.obs = 5, # number of nearest observations
                   # s.crs = st_crs(data), # nedded only if the coordinates are lon/lat (WGS84)
                   # p.crs = st_crs(data), # nedded only if the coordinates are lon/lat (WGS84)
                   cpus = detectCores()-1,
                   progress = TRUE,
                   # ranger parameters
                   importance = "impurity",
                   seed = 42,
                   num.trees = 250,
                   mtry = 5,
                   splitrule = "variance",
                   min.node.size = 5,
                   sample.fraction = 0.95,
                   quantreg = FALSE)

rfsi_model
# OOB prediction error (MSE):       47758.14 
# R squared (OOB):                  0.6435869 
sort(rfsi_model$variable.importance)
sum("obs" == substr(rfsi_model$forest$independent.variable.names, 1, 3))

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