meteo (version 2.0-2)

tune.rfsi: Tuning of Random Forest Spatial Interpolation (RFSI) model

Description

Function for tuning of Random Forest Spatial Interpolation (RFSI) model using k-fold leave-location-out cross-validation (Sekulić et al. 2020).

Usage

tune.rfsi(formula,
          data,
          data.staid.x.y.z = NULL,
          use.idw = FALSE,
          s.crs = NA,
          p.crs = NA,
          tgrid,
          tgrid.n=10,
          tune.type = "LLO",
          k = 5,
          seed=42,
          folds,
          acc.metric,
          fit.final.model=TRUE,
          cpus = detectCores()-1,
          progress = TRUE,
          soil3d = FALSE,
          no.obs = 'increase',
          ...)

Value

A list with elements:

combinations

data.frame; All tuned parameter combinations with chosen accuracy metric value.

tuned.parameters

numeric vector; Tuned parameters with chosen accuracy metric value.

final.model

ranger; Final RFSI model (if fit.final.model=TRUE).

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 tuned.

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 tuned.

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.

use.idw

boolean; IDW prediction as covariate - will IDW predictions from n.obs nearest observations be calculated and tuned (see function near.obs). Default is FALSE.

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.

tgrid

data.frame; Possible tuning parameters. The column names are same as the tuning parameters. Possible tuning parameters are: n.obs, num.trees, mtry, min.node.size, sample.fraction, splirule, idw.p, and depth.range.

tgrid.n

numeric; Number of randomly chosen tgrid combinations for tuning of RFSI. If larger than tgrid, will be set to length(tgrid)

tune.type

character; Type of cross-validation: leave-location-out ("LLO"), leave-time-out ("LTO") - TO DO, and leave-location-time-out ("LLTO") - TO DO. Default is "LLO".

k

numeric; Number of random folds that will be created with CreateSpacetimeFolds function if folds is column. Default is 5.

seed

numeric; Random seed that will be used to generate folds with CreateSpacetimeFolds function.

folds

numeric or character vector or value; Showing folds column (if value) or rows (vector) of data observations used for cross-validation. If missing, will be created with CreateSpacetimeFolds function.

acc.metric

character; Accuracy metric that will be used as a criteria for choosing an optimal RFSI model. Possible values for regression: "ME", "MAE", "NMAE", "RMSE" (default), "NRMSE", "R2", "CCC". Possible values for classification: "Accuracy","Kappa" (default), "AccuracyLower", "AccuracyUpper", "AccuracyNull", "AccuracyPValue", "McnemarPValue".

fit.final.model

boolean; Fit the final RFSI model. Defailt is TRUE.

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.

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.

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 pred.rfsi cv.rfsi

Examples

library(CAST)
library(doParallel)
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")

# making tgrid
n.obs <- 1:6
min.node.size <- 2:10
sample.fraction <- seq(1, 0.632, -0.05) # 0.632 without / 1 with replacement
splitrule <- "variance"
ntree <- 250 # 500
mtry <- 3:(2+2*max(n.obs))
tgrid = expand.grid(min.node.size=min.node.size, num.trees=ntree,
                    mtry=mtry, n.obs=n.obs, sample.fraction=sample.fraction)

if (FALSE) {
# Tune RFSI model
rfsi_tuned <- tune.rfsi(formula = fm.RFSI,
                        data = data,
                        # data.staid.x.y.z = data.staid.x.y.z, # data.frame
                        # s.crs = st_crs(data),
                        # p.crs = st_crs(data),
                        tgrid = tgrid, # combinations for tuning
                        tgrid.n = 20, # number of randomly selected combinations from tgrid
                        tune.type = "LLO", # Leave-Location-Out CV
                        k = 5, # number of folds
                        seed = 42,
                        acc.metric = "RMSE", # R2, CCC, MAE
                        fit.final.model = TRUE,
                        cpus = detectCores()-1,
                        progress = TRUE,
                        importance = "impurity") # ranger parameter

rfsi_tuned$combinations
rfsi_tuned$tuned.parameters
# min.node.size num.trees mtry n.obs sample.fraction     RMSE
# 3701             3       250    6     5            0.75 222.6752
rfsi_tuned$final.model
# OOB prediction error (MSE):       46666.51 
# R squared (OOB):                  0.6517336 
}