ordinalForest (version 1.0)

ordinalForest-package: Ordinal Forests: Prediction, Class Width Inference and Variable Ranking with Ordinal Target Variables

Description

Ordinal forests (OF) are a method for ordinal regression with high-dimensional and low-dimensional data that is able to predict the values of the ordinal target variable for new observations and at the same time estimate the relative widths of the classes of the ordinal target variable. Using a (permutation-based) variable importance measure it is moreover possible to rank the importances of the covariates. OF will be presented in an upcoming technical report by Hornung et al..

Details

For a detailed description of OF see: ordfor

The main functions are: ordfor (construction of OF), predict.ordfor (prediction of the target variable values of new observations), and plot.ordfor (visualization of the estimated relative widths of the classes of the ordinal target variable).

Examples

# NOT RUN {
# Illustration of the key functionalities of the package:
##########################################################

# Load example dataset:

data(hearth)

# Inspect the data:
table(hearth$Class)
dim(hearth)

head(hearth) 


# Split into training dataset and test dataset:

set.seed(123)
trainind <- sort(sample(1:nrow(hearth), size=floor(nrow(hearth)*(2/3))))
testind <- setdiff(1:nrow(hearth), trainind)

datatrain <- hearth[trainind,]
datatest <- hearth[testind,]


# Construct OF using the training dataset:

ordforres <- ordfor(depvar="Class", data=datatrain, ndiv=1000, ntreeperdiv=100, 
  ntreefinal=5000, perfmeasure = "equal")
ordforres

# Study variable importance values:
sort(ordforres$varimp, decreasing=TRUE)

# Take a closer look at the top variables:
boxplot(datatrain$oldpeak ~ datatrain$Class, horizontal=TRUE)
fisher.test(table(datatrain$exang, datatrain$Class))

# Plot the optimized partition:
# using ggplot2:
plot(ordforres)
# without ggplot2:
plot(ordforres, useggplot=FALSE)


# Predict values of the ordinal target variable in the test dataset:

preds <- predict(ordforres, newdata=datatest)
preds

# Compare predicted values with true values:
table(data.frame(true_values=datatest$Class, predictions=preds$ypred))
# }
# NOT RUN {
# }