localizationClassifier: Localization classifier

In localFDA: Localization Processes for Functional Data Analysis

Description

Given a training sample with g groups, it predicts the group of the test sample.

Usage

localizationClassifier(trainingSample, testSample, classNames, k_opt, g_pi)

Arguments

trainingSample	matrix p by n, being n the number of functions and p the number of grid points. The colnames of the trainingSample matrix are i_groupName where i goes from 1 to the sample size of the group.
testSample	matrix p by n, being n the number of functions to classify and p the number of grid points.
classNames	character vector with the group names.
k_opt	Maximum order of the localization processes used in the classification rule.
g_pi	Vector of size g with a priori probabilities for the bayes classifier. If it is missing the probability is defined by the proportion of curves of each group.

Value

Two named training and test. Training contains the estimations made with the training sample (localization statistics and localization distances). Test contains the classification results (for each incoming data, localization distances in each group, prior probabilities used, likelihood in each group and the predicted_class).

References

Elías, Antonio, Jiménez, Raúl and Yukich, Joe (2020). Localization processes for functional data analysis (submitted).

Examples

X <- classificationData
ids_training <- sample(colnames(X), 90)
ids_testing <- setdiff(colnames(X), ids_training)
trainingSample <- X[,ids_training]
testSample <- X[,ids_testing]; colnames(testSample) <- NULL #blind
classNames <- c("G1", "G2")
classification_results <- localizationClassifier(trainingSample, testSample, classNames, k_opt = 3)

localFDA documentation built on Oct. 23, 2020, 6:17 p.m.