Qtools (version 1.5.8)

dqc: Directional Quantile Classification

Description

This function is used to classify multivariate observations by means of directional quantiles.

Usage

dqc(formula, data, df.test, subset, weights, na.action, control = list(),
	fit = TRUE)
dqc.fit(x, z, y, control)

Value

a list of class dqc containing the following components

call

the matched call.

ans

a data frame with predictions.

nx

number of observations in the training dataset.

nz

number of observations in the prediction dataset.

p

number of variables.

control

control parameters used for fitting.

Arguments

formula

an object of class formula: a two-sided formula of the form y ~ x1 + ... + xn where y represents the groups (i.e., labels) for the observations and x1, ..., xn are the variables used for classification.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables for classification (training). If not found in data, the variables are taken from environment(formula), typically the environment from which dqc is called.

df.test

a required data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables for prediction.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

weights

an optional vector of weights to be used in the fitting process.

na.action

a function which indicates what should happen when the data contain NAs.

control

list of control parameters of the fitting process. See dqcControl.

fit

logical flag. If FALSE the function returns a list of arguments for fitting.

x

design matrix of dimension \(nx * p\) for training.

z

design matrix of dimension \(nz * p\) for prediction.

y

vector of labels of length \(nx\).

Author

Marco Geraci with contributions from Cinzia Viroli

Details

Directional quantile classification is described in the article by Viroli et al (2020).

References

Viroli C, Farcomeni A, Geraci M (2020). Directional quantile-based classifiers (in preparation).

See Also

dqcControl

Examples

if (FALSE) {
# Iris data
data(iris)

# Create training and prediction datasets

n <- nrow(iris)
ng <- length(unique(iris$Species))
df1 <- iris[c(1:40, 51:90, 101:140),]
df2 <- iris[c(41:50, 91:100, 141:150),]

# Classify
ctrl <- dqcControl(nt = 10, ndir = 5000, seed = 123)
fit <- dqc(Species ~ Sepal.Length + Petal.Length,
	data = df1, df.test = df2, control = ctrl)

# Data frame with predictions
fit$ans

# Confusion matrix
print(cm <- xtabs( ~ fit$ans$groups + df2$Species))

# Misclassification rate
1-sum(diag(cm))/nrow(df2)
}