SecDim Package for The Second Dimension of Spatial Association

 

 

Introduction to SecDim package

Install and use the package SecDim

## install and library the pacakge
install.packages("SecDim")
library("SecDim")

Here is an example of the spatial prediction using SDA models.

  1. Preparing data
# spatial data of response variables
data("obs")
# spatial data of optional SDA explanatory variables:
# b = 1, 3, 5, 7, and 9 km
# tau = seq(0, 1, 0.1)
# eight variables
data("sample_vars_sda")
  1. Data pre-processing: logarithm transformation and removing outliers
obs$y <- obs$Cr_ppm
hist(obs$y)
obs$y <- log(obs$y)
hist(obs$y)

krm <- rmvoutlier(obs$y)
y <- obs$y[-krm]
x <- lapply(sample_vars_sda, function(x) x[-krm,])
  1. selecting the second dimension variables for SDA models
system.time({ # ~2s
  sx <- selectvarsda(y, xlist = x)
})
  1. SDA modeling
data.sda <- cbind(y, sx)
sda.lm <- lm(y ~., data.sda)
summary(sda.lm)
  1. comparing with FDA
data("sample_vars_fda")
data.fda <- data.frame(y, sample_vars_fda[-krm,])
fda.lm <- lm(y ~., data.fda)
summary(fda.lm)

 

Cross validation

Figure 1. Comparison of cross validation between SDA and FDA models for spatial predictions.
Figure 1. Comparison of cross validation between SDA and FDA models for spatial predictions.
## install and library the pacakge
install.packages("SecDim")
library("SecDim")

R2 <- function(o, p) 1 - sum((o-p)^2)/sum((o-mean(o))^2)

## Example 

# spatial data of response variables
data("obs")
# spatial data of optional SDA explanatory variables:
# b = 1, 3, 5, 7, and 9 km
# tau = seq(0, 1, 0.1)
# eight variables
data("sample_vars_sda")

# data pre-processing: logarithm transformation
obs$y <- obs$Cr_ppm
hist(obs$y)
obs$y <- log(obs$y)
hist(obs$y)

################################################
## SDA cross validation
################################################

# cross validation: 70% training and 30% testing
set.seed(100)
train <- sample(nrow(obs), 0.7*nrow(obs), replace = FALSE)

trainy <- obs[train,]
testy <- obs[-train,]
trainx <- lapply(sample_vars_sda, function(x) x[train,])
testx <- lapply(sample_vars_sda, function(x) x[-train,])

# removing outliers for training data
krm <- rmvoutlier(trainy$y)
trainy <- trainy$y[-krm]
trainx <- lapply(trainx, function(x) x[-krm,])

# generating explanatory variables for testing data
sdaxv <- sdapredvars(testx)

# selecting the second dimension variables for SDA models
system.time({ # ~1.8s
  sx <- selectvarsda(y = trainy, xlist = trainx)
})

# SDA modeling and prediction
data.sda <- cbind("y" = trainy, sx)
sda.lm <- lm(y ~., data.sda)
sda.lm.pred <- predict(sda.lm, newdata = sdaxv)
R2(testy$y, sda.lm.pred)
plot(testy$y, sda.lm.pred, xlim = c(3, 7), ylim = c(3, 7))

################################################
## FDA cross validation
################################################
data("sample_vars_fda")

# cross validation: 70% training and 30% testing
set.seed(100)
train <- sample(nrow(obs), 0.7*nrow(obs), replace = FALSE)

trainy <- obs[train,]
testy <- obs[-train,]
trainx <- sample_vars_fda[train,]
testx <- sample_vars_fda[-train,]

# removing outliers for training data
krm <- rmvoutlier(trainy$y)
trainy <- trainy$y[-krm]
trainx <- trainx[-krm,]

# FDA modeling and prediction
data.fda <- data.frame("y" = trainy, trainx)
fda.lm <- lm(y ~., data.fda)
fda.lm.pred <- predict(fda.lm, newdata = data.frame(testx))
R2(testy$y, fda.lm.pred)
plot(testy$y, fda.lm.pred, xlim = c(3, 7), ylim = c(3, 7))