acc.metric.fun: Accuracy metrics calculation

In meteo: RFSI & STRK Interpolation for Meteo and Environmental Variables

Accuracy metrics calculation

Description

Calculates classification and regression accuracy metrics for given coresponding observation and prediction vectors.

Usage

acc.metric.fun(obs, pred, acc.m)

Arguments

obs	numeric or factor vector; Observations.
pred	numeric or factor vector; Predictions.
acc.m	character; Accuracy metric. Possible values for regression: "ME", "MAE", "NMAE", "RMSE", "NRMSE", "R2", "CCC". Possible values for classification: "Accuracy", "Kappa", "AccuracyLower", "AccuracyUpper", "AccuracyNull", "AccuracyPValue", "McnemarPValue".

Value

Accuracy metric value.

Author(s)

Aleksandar Sekulic [e-mail]

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

acc.metric.fun rfsi pred.rfsi tune.rfsi cv.rfsi pred.strk cv.strk

Examples

library(sp)
library(sf)
library(CAST)
library(ranger)
library(plyr)
library(meteo)

# preparing data
demo(meuse, echo=FALSE)
meuse <- meuse[complete.cases(meuse@data),]
data = st_as_sf(meuse, coords = c("x", "y"), crs = 28992, agr = "constant")
fm.RFSI <- as.formula("zinc ~ dist + soil + ffreq")

# making tgrid
n.obs <- 1:3
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)


# do cross-validation
rfsi_cv <- cv.rfsi(formula=fm.RFSI, # without nearest obs
                   data = data,
                   zero.tol=0,
                   tgrid = tgrid, # combinations for tuning
                   tgrid.n = 5, # number of randomly selected combinations from tgrid for tuning
                   tune.type = "LLO", # Leave-Location-Out CV
                   k = 5, # number of folds
                   seed = 42,
                   acc.metric = "RMSE", # R2, CCC, MAE
                   output.format = "data.frame",
                   cpus=2, # detectCores()-1,
                   progress=1,
                   importance = "impurity")
summary(rfsi_cv)

# accuracy metric calculation
acc.metric.fun(rfsi_cv$obs, rfsi_cv$pred, "R2")
acc.metric.fun(rfsi_cv$obs, rfsi_cv$pred, "RMSE")
acc.metric.fun(rfsi_cv$obs, rfsi_cv$pred, "NRMSE")
acc.metric.fun(rfsi_cv$obs, rfsi_cv$pred, "MAE")
acc.metric.fun(rfsi_cv$obs, rfsi_cv$pred, "NMAE")
acc.metric.fun(rfsi_cv$obs, rfsi_cv$pred, "CCC")

meteo documentation built on May 29, 2024, 6:13 a.m.