GD.R
In GD: Geographical Detectors for Assessing Spatial Factors

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  echo = TRUE,
  comment = "##"
)

## ----eval = FALSE-------------------------------------------------------------
# ## install and library the pacakge
# install.packages("GD")
# library("GD")
# 
# ## Example 1
# ## NDVI: ndvi_40
# ## set optional parameters of optimal discretization
# ## optional methods: equal, natural, quantile, geometric, sd and manual
# discmethod <- c("equal","natural","quantile")
# discitv <- c(4:6)
# ## "gdm" function
# ## In this case, Climatezone and Mining are categorical variables,
# ## and Tempchange and GDP are continuous variables.
# ndvigdm <- gdm(NDVIchange ~ Climatezone + Mining + Tempchange + GDP,
#                continuous_variable = c("Tempchange", "GDP"),
#                data = ndvi_40,
#                discmethod = discmethod, discitv = discitv) # ~3s
# ndvigdm
# plot(ndvigdm)
# 
# ## Example 2
# ## H1N1: h1n1_100
# ## set optional parameters of optimal discretization
# discmethod <- c("equal","natural","quantile","geometric","sd")
# discitv <- c(3:7)
# continuous_variable <- colnames(h1n1_100)[-c(1,11)]
# ## "gdm" function
# h1n1gdm <- gdm(H1N1 ~ .,
#                continuous_variable = continuous_variable,
#                data = h1n1_100,
#                discmethod = discmethod, discitv = discitv)
# h1n1gdm
# plot(h1n1gdm)

## -----------------------------------------------------------------------------
library("GD")
data("ndvi_40")
head(ndvi_40)[1:3,]

## ----eval = FALSE-------------------------------------------------------------
# ## discretization methods: equal, natural, quantile (default), geometric, sd and manual
# ds1 <- disc(ndvi_40$Tempchange, 4)
# ds1
# plot(ds1)

## ----eval = FALSE-------------------------------------------------------------
# ## set optional discretization methods and numbers of intervals
# discmethod <- c("equal","natural","quantile","geometric","sd")
# discitv <- c(4:7)
# ## optimal discretization
# odc1 <- optidisc(NDVIchange ~ Tempchange, data = ndvi_40,
#                  discmethod, discitv)
# odc1
# plot(odc1)

## ----eval = FALSE-------------------------------------------------------------
# ## a categorical explanatory variable
# g1 <- gd(NDVIchange ~ Climatezone, data = ndvi_40)
# g1
# 
# ## multiple categorical explanatory variables
# g2 <- gd(NDVIchange ~ ., data = ndvi_40[,1:3])
# g2
# plot(g2)
# 
# ## multiple variables including continuous variables
# discmethod <- c("equal","natural","quantile","geometric","sd")
# discitv <- c(3:7)
# data.ndvi <- ndvi_40
# 
# data.continuous <- data.ndvi[, c(1, 4:7)]
# odc1 <- optidisc(NDVIchange ~ ., data = data.continuous, discmethod, discitv) # ~14s
# data.continuous <- do.call(cbind, lapply(1:4, function(x)
#   data.frame(cut(data.continuous[, -1][, x], unique(odc1[[x]]$itv), include.lowest = TRUE))))
#     # add stratified data to explanatory variables
# data.ndvi[, 4:7] <- data.continuous
# 
# g3 <- gd(NDVIchange ~ ., data = data.ndvi)
# g3
# plot(g3)

## ----eval = FALSE-------------------------------------------------------------
# ## categorical explanatory variables
# rm1 <- riskmean(NDVIchange ~ Climatezone + Mining, data = ndvi_40)
# rm1
# plot(rm1)
# ## multiple variables inclusing continuous variables
# rm2 <- riskmean(NDVIchange ~ ., data = data.ndvi)
# rm2
# plot(rm2)

## ----eval = FALSE-------------------------------------------------------------
# ## categorical explanatory variables
# gr1 <- gdrisk(NDVIchange ~ Climatezone + Mining, data = ndvi_40)
# gr1
# plot(gr1)
# ## multiple variables inclusing continuous variables
# gr2 <- gdrisk(NDVIchange ~ ., data = data.ndvi)
# gr2
# plot(gr2)

## ----eval = FALSE-------------------------------------------------------------
# ## categorical explanatory variables
# gi1 <- gdinteract(NDVIchange ~ Climatezone + Mining, data = ndvi_40)
# gi1
# ## multiple variables inclusing continuous variables
# gi2 <- gdinteract(NDVIchange ~ ., data = data.ndvi)
# gi2
# plot(gi2)

## ----eval = FALSE-------------------------------------------------------------
# ## categorical explanatory variables
# ge1 <- gdeco(NDVIchange ~ Climatezone + Mining, data = ndvi_40)
# ge1
# ## multiple variables inclusing continuous variables
# gd3 <- gdeco(NDVIchange ~ ., data = data.ndvi)
# gd3
# plot(gd3)

## ----eval = FALSE-------------------------------------------------------------
# ndvilist <- list(ndvi_20, ndvi_30, ndvi_40, ndvi_50)
# su <- c(20,30,40,50) ## sizes of spatial units
# ## "gdm" function
# gdlist <- lapply(ndvilist, function(x){
#   gdm(NDVIchange ~ Climatezone + Mining + Tempchange + GDP,
#       continuous_variable = c("Tempchange", "GDP"),
#       data = x, discmethod = "quantile", discitv = 6)
# })
# sesu(gdlist, su) ## size effects of spatial units