⸂𝖶𝖩𝖣 .

[turxy]

This script reproduces, in sequence, the results and examples

in the accompannying paper. (Please note that a full run of the

script may take between a few hours and a day to run depending

on the computer.)

Package version information:

A3 0.9.2

e1071 1.6-1

randomForest 4.6-7

xtable 1.7-1

pbapply 1.0-5

R 2.15.2 OS X 64 bit

Due to the usage of stochasticity in the A3 method, minor

differences in results may be obtained if different versions of

packages are used. These differences will be minor and not

affect overall conclusions.

Each block represents an output (either a table or a figure) in

the original paper. Blocks are sequential as the results appear

in the paper.

set.seed(1)
library("A3")
print(a3(formula = rating ~ ., data = attitude, model.fn = lm))

library("e1071")
print(a3(rating ~ . + 0, attitude, svm))

library("randomForest")
out.rf <- a3(rating ~ . + 0, attitude, randomForest, p.acc = 0.05)
print(out.rf)

print(a3(rating ~ . + 0, attitude, randomForest, p.acc = 0.05, model.args = list(ntree = 1000)))

print(out.rf)

plotPredictions(out.rf)

plotSlopes(out.rf)

data("housing", package = "A3")
reg <- lm(MED.VALUE ~ AGE + ROOMS + NOX + PUPIL.TEACHER + HIGHWAY, housing)
print(summary(reg))

NOTE: this may take 2 hours+; this data is cached in a file in data/ for article generation

housing.lm <- a3.lm(MED.VALUE ~ AGE +ROOMS + NOX + PUPIL.TEACHER + HIGHWAY, housing,
p.acc = 0.01, n.folds = 50)
print(housing.lm)

NOTE: this may take 2 hours+; this data is cached in a file in data/ for article generation

housing.svm <- a3(MED.VALUE ~ AGE +ROOMS + NOX + PUPIL.TEACHER + HIGHWAY+0, housing, svm,
p.acc = 0.01, n.folds = 50)
housing.rf <- a3(MED.VALUE ~ AGE +ROOMS + NOX + PUPIL.TEACHER + HIGHWAY+0, housing, randomForest,
p.acc = 0.01, n.folds = 50)

print(housing.svm)
print(housing.rf)

plotSlopes(housing.rf)

NOTE: this may take 2 hours+; this data is cached in a file in data/ for article generation

data("multifunctionality", package = "A3")
reg <- lm(MUL ~ SR + SLO + SAC + PCA_C1 + PCA_C2 + PCA_C3 + PCA_C4 + LAT + LONG + ELE,
multifunctionality)
print(summary(reg))

NOTE: this may take 2 hours+; this data is cached in a file in data/ for article generation

mult.lm <- a3.lm(MUL ~ SR + SLO + SAC + PCA_C1 + PCA_C2 + PCA_C3 + PCA_C4 + LAT + LONG + ELE,
multifunctionality, p.acc = 0.01, n.folds = 50)
print(mult.lm)

NOTE: this may take 2 hours+; this data is cached in a file in data/ for article generation

mult.rf <- a3(MUL ~ SR + SLO + SAC + PCA_C1 + PCA_C2 + PCA_C3 + PCA_C4 + LAT + LONG + ELE + 0,
multifunctionality, randomForest, p.acc = 0.01, n.folds = 50)
print(mult.rf)

set.seed(1)
createAutoCorrelatedSeries <- function(n, r) {
dat <- rnorm(n, 0, 1)
for(i in 2:n) dat[i] <- dat[i-1]r + dat[i](1-r)
dat
}
sample <- data.frame(x = createAutoCorrelatedSeries(100, 0.95),
y = createAutoCorrelatedSeries(100, 0.95))
reg <- lm(y ~ x, sample)
print(summary(reg))