geosimilarity (version 3.8)

gos: geographically optimal similarity

Description

Computationally optimized function for geographically optimal similarity (GOS) model

Usage

gos(formula, data = NULL, newdata = NULL, kappa = 0.25, cores = 1)

Value

A tibble made up of predictions and uncertainties.

pred

GOS model prediction results

uncertainty90

uncertainty under 0.9 quantile

uncertainty95

uncertainty under 0.95 quantile

uncertainty99

uncertainty under 0.99 quantile

uncertainty99.5

uncertainty under 0.995 quantile

uncertainty99.9

uncertainty under 0.999 quantile

uncertainty100

uncertainty under 1 quantile

Arguments

formula

A formula of GOS model.

data

A data.frame or tibble of observation data.

newdata

A data.frame or tibble of prediction variables data.

kappa

(optional) A numeric value of the percentage of observation locations with high similarity to a prediction location. \(kappa = 1 - tau\), where tau is the probability parameter in quantile operator. The default kappa is 0.25, meaning that 25% of observations with high similarity to a prediction location are used for modelling.

cores

(optional) Positive integer. If cores > 1, a parallel package cluster with that many cores is created and used. You can also supply a cluster object. Default is 1.

References

Song, Y. (2022). Geographically Optimal Similarity. Mathematical Geosciences. doi: 10.1007/s11004-022-10036-8.

Examples

data("zn")
# log-transformation
hist(zn$Zn)
zn$Zn <- log(zn$Zn)
hist(zn$Zn)
# remove outliers
k <- removeoutlier(zn$Zn, coef = 2.5)
dt <- zn[-k,]
# split data for validation: 70% training; 30% testing
split <- sample(1:nrow(dt), round(nrow(dt)*0.7))
train <- dt[split,]
test <- dt[-split,]
system.time({
g1 <- gos(Zn ~ Slope + Water + NDVI  + SOC + pH + Road + Mine,
          data = train, newdata = test, kappa = 0.25, cores = 1)
})
test$pred <- g1$pred
plot(test$Zn, test$pred)
cor(test$Zn, test$pred)